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The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

Ekvivalentinės juodosios anglies matavimai Isproje, Italijoje. Măsurători ale carbonului negru echivalent în Ispra, Italia. Вимірювання еквівалентного чорного вуглецю в Іспрі, Італія. Измервания на еквивалентен черен въглерод в Испра, Италия. Merania ekvivalentného čierneho uhlíka v Ispre, Taliansko. Tomhais de charbón dubh coibhéiseach in Ispra na hIodáile. Metingen van equivalente zwarte koolstof in Ispra, Italië. Mediciones de carbono negro equivalente en Ispra, Italia. Measurements of equivalent black carbon in Ispra, Italy. Pomiary równoważnego czarnego węgla w Ispra we Włoszech.

Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed rice.

Στοιχεία σχετικά με: (1) χωρική κατανομή της αστικής μελισσοκομίας (αριθμός κυψελών και αριθμός μελισσοκομικών τοποθεσιών) σε 14 ελβετικές πόλεις (Γενεύη, Λωζάνη, Biel, Neuchatel, Βασιλεία, Ζυρίχη, Chur, Luzern, St. Gallen, Winterthur, Βέρνη, Λουγκάνο, Bellinzona, Thun) για την περίοδο 2012-2018· (2) συγκεντρωτικά δεδομένα για τη μοντελοποίηση της βιωσιμότητας της αστικής μελισσοκομίας. Στοιχεία σχετικά με: (1) χωρική κατανομή της αστικής μελισσοκομίας (αριθμός κυψελών και αριθμός μελισσοκομικών τοποθεσιών) σε 14 ελβετικές πόλεις (Γενεύη, Λωζάνη, Biel, Neuchatel, Βασιλεία, Ζυρίχη, Chur, Luzern, St. Gallen, Winterthur, Βέρνη, Λουγκάνο, Bellinzona, Thun) για την περίοδο 2012-2018· (2) συγκεντρωτικά δεδομένα για τη μοντελοποίηση της βιωσιμότητας της αστικής μελισσοκομίας. Στοιχεία σχετικά με: (1) χωρική κατανομή της αστικής μελισσοκομίας (αριθμός κυψελών και αριθμός μελισσοκομικών τοποθεσιών) σε 14 ελβετικές πόλεις (Γενεύη, Λωζάνη, Biel, Neuchatel, Βασιλεία, Ζυρίχη, Chur, Luzern, St. Gallen, Winterthur, Βέρνη, Λουγκάνο, Bellinzona, Thun) για την περίοδο 2012-2018· (2) συγκεντρωτικά δεδομένα για τη μοντελοποίηση της βιωσιμότητας της αστικής μελισσοκομίας. Données sur: (1) répartition spatiale de l’apiculture urbaine (nombre de ruches et nombre de sites apicoles) dans 14 villes suisses (Genève, Lausanne, Biel, Neuchatel, Bâle, Zurich, Chur, Luzern, St Gallen, Winterthur, Berne, Lugano, Bellinzona, Thun) pour la période 2012-2018; (2) des données agrégées pour modéliser la durabilité de l’apiculture urbaine. Données sur: (1) répartition spatiale de l’apiculture urbaine (nombre de ruches et nombre de sites apicoles) dans 14 villes suisses (Genève, Lausanne, Biel, Neuchatel, Bâle, Zurich, Chur, Luzern, St Gallen, Winterthur, Berne, Lugano, Bellinzona, Thun) pour la période 2012-2018; (2) des données agrégées pour modéliser la durabilité de l’apiculture urbaine. Données sur: (1) répartition spatiale de l’apiculture urbaine (nombre de ruches et nombre de sites apicoles) dans 14 villes suisses (Genève, Lausanne, Biel, Neuchatel, Bâle, Zurich, Chur, Luzern, St Gallen, Winterthur, Berne, Lugano, Bellinzona, Thun) pour la période 2012-2018; (2) des données agrégées pour modéliser la durabilité de l’apiculture urbaine. Údaje o: (1) prostorové rozložení včelařství (počet úlů a počet včelařských lokalit) ve 14 švýcarských městech (Ženeva, Lausanne, Biel, Neuchatel, Basilej, Curych, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) na období 2012–2018; (2) souhrnné údaje pro modelování udržitelnosti včelařství ve městech. Údaje o: (1) prostorové rozložení včelařství (počet úlů a počet včelařských lokalit) ve 14 švýcarských městech (Ženeva, Lausanne, Biel, Neuchatel, Basilej, Curych, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) na období 2012–2018; (2) souhrnné údaje pro modelování udržitelnosti včelařství ve městech. Údaje o: (1) prostorové rozložení včelařství (počet úlů a počet včelařských lokalit) ve 14 švýcarských městech (Ženeva, Lausanne, Biel, Neuchatel, Basilej, Curych, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) na období 2012–2018; (2) souhrnné údaje pro modelování udržitelnosti včelařství ve městech. Data dwar: (1) id-distribuzzjoni spazjali tal-apikultura urbana (l-għadd ta’ doqqajs u n-numru ta’ postijiet tat-trobbija tan-naħal) f’14-il belt Svizzera (Ġinevra, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) għall-perjodu 2012–2018; (2) dejta aggregata biex timmudella s-sostenibbiltà tat-trobbija urbana tan-naħal. Data dwar: (1) id-distribuzzjoni spazjali tal-apikultura urbana (l-għadd ta’ doqqajs u n-numru ta’ postijiet tat-trobbija tan-naħal) f’14-il belt Svizzera (Ġinevra, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) għall-perjodu 2012–2018; (2) dejta aggregata biex timmudella s-sostenibbiltà tat-trobbija urbana tan-naħal. Daten zu: (1) räumliche Verteilung der Bienenzucht (Anzahl der Bienenstöcke und Anzahl der Imkereistandorte) in 14 Schweizer Städten (Genf, Lausanne, Biel, Neuchatel, Basel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) für den Zeitraum 2012-2018; (2) aggregierte Daten zur Modellierung der Nachhaltigkeit der städtischen Bienenzucht. Daten zu: (1) räumliche Verteilung der Bienenzucht (Anzahl der Bienenstöcke und Anzahl der Imkereistandorte) in 14 Schweizer Städten (Genf, Lausanne, Biel, Neuchatel, Basel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) für den Zeitraum 2012-2018; (2) aggregierte Daten zur Modellierung der Nachhaltigkeit der städtischen Bienenzucht. Sonraí maidir le: (1) dáileadh spásúil na beachaireachta uirbí (líon na gcoirceog agus líon na láithreacha beachaireachta) i 14 chathair na hEilvéise (an Ghinéiv, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) don tréimhse 2012-2018; (2) sonraí comhiomlánaithe chun inbhuanaitheacht na beachaireachta uirbí a shamhaltú. Gegevens over: (1) ruimtelijke verdeling van de stedelijke bijenteelt (aantal bijenkasten en aantal bijenteeltlocaties) in 14 Zwitserse steden (Geneva, Lausanne, Biel, Neuchatel, Bazel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) voor de periode 2012-2018; (2) geaggregeerde gegevens om de duurzaamheid van de stedelijke bijenteelt te modelleren. Dane dotyczące: 1) rozmieszczenie przestrzenne pszczelarstwa miejskiego (liczba uli i liczba miejsc pszczelarskich) w 14 miastach Szwajcarii (Genewa, Lozanna, Biel, Neuchatel, Bazylea, Zurych, Chur, Luzern, St. Gallen, Winterthur, Berno, Lugano, Bellinzona, Thun) w latach 2012–2018; 2) dane zagregowane w celu modelowania zrównoważonego rozwoju pszczelarstwa miejskiego. Tiedot seuraavista: 1) kaupunkien mehiläishoidon alueellinen jakautuminen (pesien lukumäärä ja mehiläishoitopaikkojen lukumäärä) 14 Sveitsin kaupungissa (Geneva, Lausanne, Biel, Neuchatel, Basel, Zürich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) vuosina 2012–2018; (2) aggregoidut tiedot kaupunkien mehiläishoidon kestävyyden mallintamiseksi. Datos sobre: (1) distribución espacial de la apicultura urbana (número de colmenas y número de lugares de apicultura) en 14 ciudades suizas (Ginebra, Lausana, Biel, Neuchatel, Basilea, Zúrich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) para el período 2012-2018; (2) datos agregados para modelar la sostenibilidad de la apicultura urbana.

This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.

This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.

Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

Geoven (http://www.geoven.fr) is a geothermal power-plant project led by Fonroche Géothermie (now Arverne). The project is implemented on the site of the Rhenan Ecoparc at Vendenheim, North of Strasbourg. The future geothermal power-plant was expected to produce 6 MW of electrical energy and 40 MW of thermal energy. To this end, two wells were used to draw the hot water and reinject it at more than four thousand meters deep.