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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.

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.

# Data from: Eocene Shark Teeth from Peninsular Antarctica: Windows to Habitat Use and Paleoceanography. [https://doi.org/10.5061/dryad.qz612jmq2](https://doi.org/10.5061/dryad.qz612jmq2) The repository folder includes scripts and spreadsheets for phosphate oxygen stable isotope (δ18Op) analysis measured from shark tooth biogenic apatite collected from the Eocene deposits of the La Meseta and Submeseta formations (West Antarctica, Seymour Island). It also contains Fourier-Transform Infrared Spectroscopy (FTIR) analysis, a Bayesian model for temperature estimates, and model output extraction scripts from the iCESM simulation for the Early Eocene (Zhu et al., 2020). Scripts and data are stored in specific folders on the type of analysis. All scripts are in R or Python language. **Usage notes** **1 "iCESM modeling scripts" directory** The folder includes scripts in Jupiter Notebook format for extracting and plotting iCESM seawater outputs for the Eocene. The folder includes two files: 1) “d18Ow Analysis Script.ipynb” - This is a Python script primarily using the XArray library, to import iCESM output from Zhu et al. (2020), calculating δ18Ow, and reorganizing the output into monthly time intervals along 25 m and 115 m depth slices, while also averaging output down to these depths; 2) “NetCDF Plotting.ipynb” - this is a Python script primarily using the XArray, Matplotlib, and Cartopy libraries. The script writes a single callable function that creates Matplotlib contour plots from iCESM history output. Variables include temperature, salinity, ideal age, oxygen isotopes, and neodymium isotopes, and map projections include Plate Carree, Mollweide, and orthographic (centering on the Drake Passage). Options are built to enable scale normalization or to set maximum and minimum values for data and select colormaps from a predefined selection of Matplotlib’s “Spectral”, “Viridis”, “Coolwarm”, “GNUplot2”, “PiYG”, “RdYlBu”, and “RdYlGn”. For further questions on model output scripts, please email Adam Aleksinski at [aaleksin@purdue.edu](https://datadryad.org/stash/dataset/doi:10.5061/aaleksin@purdue.edu). **2 "d18O data and maps" directory** The folder includes δ18Op of shark tooth bioapatite and other datasets to interpret shark paleoecology. These datasets include: · δ18Op of shark tooth bioapatite (“shark FEST d18Op.csv”). Isotope measurements were run at the Stable Isotope Ecosystem Laboratory of (SIELO) University of California, Merced (California, USA). · Reference silver phosphate material δ18Op for analytical accuracy and precision (“TCEA reference materials.csv"). Isotope measurements were run at the Stable Isotope Ecosystem Laboratory of (SIELO) University of California, Merced (California, USA). · Bulk and serially sampled δ18Oc data of co-occurring bivalves (Ivany et al., 2008; Judd et al., 2019) (“Ivany et al. 2008_bulk.csv” and “Judd et al., 2019_serial sampling.csv"). · iCESM model temperature and δ18Ow outputs at 3x and 6x pre-industrial CO2 levels for the Early Eocene (Zhu et al., 2020) (“SpinupX3_25m_Mean_Monthly.nc”, “SpinupX6_25m_Mean_Monthly.nc.”, and “CA_x3CO2.csv”). Simulations are integrated from the surface to 25 m. · δ18O values of invertebrate species published in Longinelli (1965) and Longinelli & Nuti (1973), used to convert bulk δ18Oc (V-SMOW) data of bivalves into δ18Op (V-SMOW) values after δ18Oc (V-PDB) - δ18Oc (V-SMOW) conversion found in Kim et al. (2015) (“d18O carbonate and phosphate references.csv”). · R script for data analysis ("d18O data and maps.Rmd”). The script provides annotation through libraries, instrumental accuracy and precision tests, tables, statistical analysis, figures, and model output extractions. . ("TELM_diversity.csv") displays diversity trends of chondrichthyans across TELMs in one of the main figures of the manuscript. **2.1 Dataset description** **shark FEST d18Op.csv** · *Sample_ID*: Identification number of tooth specimens. · *Other_ID*: Temporary identification number of tooth specimens. · *Taxon*: Species assigned to shark tooth specimens. · *TELM*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *d18Op*: Mean δ18Op values of silver phosphate crystals precipitated from shark tooth bioapatite. Specimens were run in triplicates, corrected, and standardized on the V-SMOW scale. · *sd*: Standard deviation of silver phosphate triplicate samples per specimen. · *Protocol*: Silver phosphate protocols used to precipitate crystals from shark tooth bioapatite. We adopted the Rapid UC (“UC_Rapid”) and the SPORA (“SPORA”) protocols after Mine et al. and (2017) Larocca Conte et al. (2024) based on the tooth specimen size and sampling strategy. Descriptions of the methods are included in the main manuscript. · *Environment*: Inferred shark habitat based on taxonomy classified as benthic or pelagic environment. · *Collection*: Institutional abbreviations of museum collections from which shark tooth specimens are housed. NRM-PZ is the abbreviation for the Swedish Natural History Museum (Stockholm, Sweden), PRI is the abbreviation for the Paleontological Research Institute (Ithaca, New York, United States), and UCMP is the University of California Museum of Paleontology (Berkeley, California, United States). **TCEA reference materials.csv** · *Identifier_1*: unique identifier number per sample. · *sample*: reference silver phosphate materials (USGS 80 and USGS 81). · *amount*: weight of samples in mg. · *Area 28*: peak area of mass 28 (12C16O). · *Area 30*: peak area of mass 30 (12C18O). · *d18O_corrected*: corrected δ18Op value of reference materials following drift correction, linearity correction, and 2-point calibration to report values on the V-SMOW scale. **Ivany et al. 2008_bulk.csv** · *Telm*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *Locality*: Locality code from which bivalves were collected. · *Genus*: Genera of bivalves. Specimens are assigned to *Cucullaea* and *Eurhomalea* genera. · *Line*: Sampling areas of specimens. The sampling strategy is described in Ivany et al. (2008). · *d13C*: δ13C values of specimens from sampled lines. Values are reported in the V-PDB scale. · *d18Oc_PDB*: δ18Oc values of specimens from sampled lines. Values are reported in the V-PDB scale. **Judd et al., 2019_serial sampling.csv** · *Horizon:* horizons of the TELM 5 unit (La Meseta Formation) from which bivalves were collected. Horizon 1 is stratigraphically the lowest, while horizon 4 is the highest (Judd et al., 2019). · *ID*: Identification number of specimens. · *Latitude*: Geographic coordinate where bivalve specimens were collected. · *Longitude*: Geographic coordinate where bivalve specimens were collected. · *Surface sampled*: Specific sampling area, indicating whether sampling occurred in the interior or exterior portion of shells. · *distance*: The distance from the umbo in mm from which sampling occurred along a single shell. · *d18Oc_PDB*: δ18Oc values of specimens from sampled areas of shells. Values are reported on the V-PDB scale. **SpinupX3_25m_Mean_Monthly.nc** See section 1 ("iCESM modeling scripts" directory, “d18Ow Analysis Script.ipynb” script) for a full description of the iCESM model output extraction. **SpinupX6_25m_Mean_Monthly.nc** See section 1 ("iCESM modeling scripts" directory, “d18Ow Analysis Script.ipynb” script) for a full description of the iCESM model output extraction. **CA_x3CO2.csv** · *lat*: Geographic coordinate where temperature and δ18Ow model values are extracted from the iCESM simulation scaled at 3x preindustrial CO2 levels (values averaged within a seawater column depth of 25 m). · *long*: Geographic coordinate where temperature and δ18Ow model values are extracted from the iCESM simulation scaled at 3x preindustrial CO2 levels (values averaged within a seawater column depth of 25 m). · *T_mean*: Simulated seawater temperature values in °C. · *d18Ow*: Simulated seawater δ18Ow values (V-SMOW). · *d18Op*: Simulated seawater δ18Op values (V-SMOW). Values were calculated by using seawater temperature and δ18Ow arrays following the paleothermometer equation after Lécuyer et al. (2013). **d18O carbonate and phosphate references.csv** · *species*: Species of invertebrate taxa. · *type*: Specimen type, including barnacles, brachiopods, crabs, and mollusks. · *depth*: Depth of seawater column where specimens were collected, reported in meters below sea level when specified. · *d18Op*: δ18Op values of invertebrate specimens (V-SMOW). · *d18Oc_PDB*: δ18Oc values of invertebrate specimens (V-PDB). · *Reference*: Citations from which data were taken to build the dataset (Longinelli, 1965; Longinelli & Nuti, 1973). **TELM diversity.csv** · *genus:* genera of sharks and rays compiled from literature (Engelbrecht et al., 2016a, 2016b, 2017a, 2017b, 2019; Kriwet, 2005; Kriwet et al., 2016; Long, 1992; Marramá et al., 2018). · *species*: species of sharks and rays compiled from literature (Engelbrecht et al., 2016a, 2016b, 2017a, 2017b, 2019; Kriwet, 2005; Kriwet et al., 2016; Long, 1992; Marramá et al., 2018). · *Environment*: Inferred shark habitat based on taxonomy classified as benthic or pelagic environment. · *TELM*: Stratigraphic units of La Meseta (TELM 1-5; ~44 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). **3 “FTIR data” directory** The folder includes FTIR acquisitions and data analysis scripts on reference materials and shark tooth bioapatite for quality checks to test diagenesis effects on δ18Op of sharks. The folder includes: · The R project file “apatite_ftir.Rproj”. This project file navigates through scripts for raw data processing and data analysis. The background of the raw data was processed following custom R functions from Trayler et al. (2023; [https://github.com/robintrayler/collagen_demineralization](https://github.com/robintrayler/collagen_demineralization)). · The “.Rproj.user” folder includes project-specific temporary files (e.g. auto-saved source documents, window-state, etc.) stored by the R project file “apatite_ftir.Rproj”. The folder may be hidden depending on directory view options. · The “raw data” directory stores spectra acquisitions as .dpt files. Spectra files are stored in the folders “apatite” and “calcite” based on the material type. Spectra were obtained in the 400 – 4000 cm⁻¹ range using a Bruker Vertex 70 Far-Infrared in ATR located at the Nuclear Magnetic Resonance Facility at the University of California Merced (California, USA). · The “processed” directory includes processed spectra stored as .csv files (“apatite_data.csv” and “calcite_data.csv”) following the background correction (Trayler et al., 2023) and processed infrared data from Larocca Conte et al. (2024) (“Larocca Conte et al._SPORA_apatite_data.csv”) from which the NIST SRM 120c spectrum was filtered. Infrared spectra data in “Larocca Conte et al._SPORA_apatite_data.csv” were obtained and corrected following the same methodologies mentioned above. · The “R” directory includes R scripts of customized source functions for background correction (Trayler et al., 2023; inspect the "functions" directory and the R script "0_process_data.R") and data analysis (“data_analysis.R”). The scripts provide annotation through libraries and functions used for data processing and analysis. · Additional datasets. The “data_FTIR_d18O.csv” includes infrared data and δ18Op values of specimens, while the “Grunenwald et al., 2014_CO3.csv” is the dataset after Grunenwald et al. (2014) used to predict carbonate content from the materials featured in this work. **3.1 Dataset description** Spreadsheets included in the “processed” directory The datasets “apatite_data.csv”, “calcite_data.csv”, and “Larocca Conte et al._SPORA_apatite_data.csv” are structured with the following variables: · *wavenumber*: infrared wavenumber in cm-1. · *absorbance*: infrared absorbance value. · *file_name:* .dpt file name from which infrared wavenumber and absorbance values were obtained following the background correction. **data_FTIR_d18O.csv** · *file_name:* .dpt file name from which infrared wavenumber and absorbance values were obtained following the background correction. · *v4PO4_565_wavenumber*: Wavenumber of maximum infrared absorbance around the first νPO4 band, usually at 565 cm-1. · *v4PO4_565*: Peak absorbance value of the first ν4PO4 band (~565 cm-1). · *v4PO4_valley_wavenumber*: Wavenumber of valley between ν4PO4 bands. · *v4PO4_valley*: Absorbance value of the valley between ν4PO4 bands. · *v4PO4_603_wavenumber*: Wavenumber of maximum infrared absorbance around the second ν4PO4 band, usually at 603 cm-1. · *v4PO4_603*: Peak absorbance value of the second ν4PO4 band (~603 cm-1). · *CI*: Crystallinity index calculated after equation provided in (Shemesh, 1990) as (*v4PO4_565* + *v4PO4_603* / *v4PO4_valley*) (i.e., the sum of peak absorbance of νPO4 bands divided by the absorbance value of the valley between peaks). · *material*: Material type of samples (i.e., standard material, enameloid, dentin sampled from the crown or root area of shark teeth, and enameloid mixed with dentin). · *AUC_v3PO4*: Area under the curve of the ν3PO4 and ν1PO4 bands where maximum absorbance is at ~1025 cm-1 and ~960 cm-1, respectively. · *AUC_v3CO3*: Area under the curves of Type-A and Type-B carbonate bands having maximum infrared absorbance at ~1410 (Type-B), ~1456 (Type-B), and ~1545 cm-1 (Type-A). · *v3CO3_v3PO4_ratio*: Ratio between area under the curves of carbonate and phosphate bands (i.e., *AUC_v3CO3* / *AUC_v3PO4*). · *CO3_wt*: Estimated mean carbonate content following the equation in Grunenwald et al. (2014) (i.e. *CO3_wt* = 28.4793 (±1.4803) *v3CO3_v3PO4_ratio* + 0.1808(±0.2710); R2 = 0.985). · *CO3_wt_sd*: Standard deviation of estimated carbonate content calculated by propagating the error around coefficients provided in the Grunenwald et al. (2014) equation (see full equation in *CO3_wt*). · *Taxon*: Species assigned to shark tooth specimens. · *TELM*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *d18Op*: Mean δ18Op values of silver phosphate crystals precipitated from shark tooth bioapatite. Specimens were run in triplicates, corrected, and standardized on the V-SMOW scale. · *sd*: Standard deviation of silver phosphate triplicate samples per specimen. · *Collection*: Institutional abbreviations of museum collections where shark tooth specimens are housed. Infrared spectra were obtained from a selected subset of tooth specimens in the care of the Swedish Natural History Museum (NRM-PZ; Stockholm, Sweden). **Grunenwald et al., 2014_CO3.csv** · *sample*: Sample code. · *material*: Material type of samples (i.e., standard material, bone, and enamel). · *v3CO3*: Area under the curves of Type-A and Type-B carbonate bands having maximum infrared absorbance at ~1410 (Type-B), ~1456 (Type-B), and ~1545 cm-1 (Type-A). · *v3PO4*: *AUC_v3PO4*: Area under the curve of the ν3PO4 and ν1PO4 bands where maximum absorbance is at ~1025 cm-1 and ~960 cm-1, respectively. · *v3CO3_v3PO4_ratio*: *v3CO3_v3PO4_ratio*: Ratio between area under the curves of carbonate and phosphate bands (i.e., *v3CO3* /*v3PO4*). · *CO3_wt*: Carbonate content measured via CO2 coulometry. Further details about the analytical measurements are found in Grunenwald et al. (2014). **4 “Bayes_FEST_Temperautre Estimates” directory** The folder includes the Bayesian approach used to estimate posterior seawater temperature, δ18Ow values from δ18Op of sharks bioapatite using a Bayesian approach modified after Griffiths et al. (2023). The original scripts used in Griffiths et al. (2023) are reposited here: [https://github.com/robintrayler/bayesian_phosphate](https://github.com/robintrayler/bayesian_phosphate). The directory includes: · The R project file “Bayes_FEST.Rproj”. This project file navigates through scripts for raw data analysis. · The “.Rproj.user” folder includes project-specific temporary files (e.g. auto-saved source documents, window-state, etc.) stored by the R project file “Bayes_FEST.Rproj”. The folder may be hidden depending on directory view options. · The “data” folder includes the spreadsheets for modeled seawater temperature and δ18Ow values (“CA_x3CO2.csv”) and δ18Op values of shark tooth bioapatite (“shark FEST d18Op.csv”) used as prior information for the Bayesian model. We refer to section 2.1 for the full description of spreadsheets. · The “R” folder includes customized functions for the Bayesian model stored in the “functions” directory and the script for data analysis (“01_model_sharks.R”). The script includes a comparison of paleothermometer equations after Kolodny et al. (1983), Lécuyer et al. (2013), Longinelli & Nuti (1973), and (Pucéat et al. (2010) using the bulk δ18Op shark tooth bioapatite, simulated seawater temperature and δ18Ow values as prior inputs. While all paleothermometers estimate similar posterior bulk δ18Op close to empirical values, temperature estimates using the Pucéat et al. (2010) method are often the highest, generating estimates ~8°C higher than other equations. We therefore used the Lécuyer et al. (2013) paleothermomether for temperature estimates using δ18Op of shark bioapatite grouped by taxa because it: 1\) Provides consistent posterior temperature estimates relative to other equations (Longinelli & Nuti, 1973, Kolodny et al., 1983). 2\) provides temperature values from fish tooth specimens consistent with estimates of co-existing bivalves or brachiopod carbonate shells. The script provides annotation through libraries, statistical analysis, figures, and tables. **4 Software** **4.1 R** R and R Studio (R Development Core Team, 2024; RStudio Team, 2024) are required to run scripts included in the "d18O data and maps", “FTIR data”, and “Bayes_FEST_Temperautre Estimates” directories, which were created using versions 4.4.1 and 2024.04.02, respectively. Install the following libraries before running scripts: “cowplot” (Wilke, 2024), “colorspace” (Zeileis et al., 2020), “DescTools” (Signorell, 2024), “lattice” (Sarkar, 2008), “flextable” (Gohel & Skintzos, 2024), “ggh4x” (van den Brand, 2024), “ggnewscale” (Campitelli, 2024), “ggpubr” (Kassambara, 2023a), “ggspatial” (Dunnington, 2023), “ggstance” (Henry et al., 2024), “ggstar” (Xu, 2022), “greekLetters” (Kévin Allan Sales Rodrigues, 2023), “gridExtra” (Auguie, 2017), “mapdata” (code by Richard A. Becker & version by Ray Brownrigg., 2022); “mapproj” (for R by Ray Brownrigg et al., 2023), “maps” (code by Richard A. Becker et al., 2023), “ncdf4” (Pierce, 2023), “oce” (Kelley & Richards, 2023), “rasterVis” (Oscar Perpiñán & Robert Hijmans, 2023), “RColorBrewer” (Neuwirth, 2022), “rnaturalearth” (Massicotte & South, 2023), “rnaturalearthhires” (South et al., 2024),”rstatix” (Kassambara, 2023b), “scales” (Wickham et al., 2023), “tidyverse” (Wickham et al., 2019), “viridisLite” (Garnier et al., 2023). **4.2 Python** Python scripts, including “d18O Analysis Script.ipynb” and “NetCDF Plotting.ipynb”, utilize the Jupyter Notebook interactive ‘platform and are executed using Python version 3.9.16. Install the following libraries before running scripts: “xarray” (Hoyer & Joseph, 2017), “matplotlib” (Hunter, 2007), “cartopy” (Met Office, 2015). **5 References** Amenábar, C. R., Montes, M., Nozal, F., & Santillana, S. (2020). 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Sharks in high-latitude oceans, sensitive to these changes, offer insights into both environmental shifts and biological responses, yet few paleoecological studies exist. The Middle-to-Late Eocene units on Seymour Island, Antarctica, provide a rich, diverse fossil record, including sharks. We analyzed the oxygen isotope composition of phosphate from shark tooth bioapatite (δ18Op) and compared our results to co-occurring bivalves and predictions from an isotope-enabled global climate model to investigate habitat use and environmental conditions. Bulk δ18Op values (mean 22.0 ± 1.3‰) show no significant changes through the Eocene. Furthermore, the variation in bulk δ18Op values often exceeds that in simulated seasonal and regional values. Pelagic and benthic sharks exhibit similar δ18Op values across units but are offset relative to bivalve and modeled values. Some taxa suggest movements into warmer or more brackish waters (e.g., Striatolamia, Carcharias) or deeper, colder waters (e.g., Pristiophorus). Taxa like Raja and Squalus display no shift, tracking local conditions in Seymour Island. The lack of difference in δ18Op values between pelagic and benthic sharks in the Late Eocene could suggest a poorly stratified water column, inconsistent with a fully opened Drake Passage. Our findings demonstrate that shark tooth bioapatite tracks the preferred habitat conditions for individual taxa rather than recording environmental conditions where they are found. A lack of secular variation in δ18Op values says more about species ecology than the absence of regional or global environmental changes. See methods in Larocca Conte, G., Aleksinski, A., Liao, A., Kriwet, J., Mörs, T., Trayler, R. B., Ivany, L. C., Huber, M., Kim, S. L. (2024). Eocene Shark Teeth From Peninsular Antarctica: Windows to Habitat Use and Paleoceanography. Paleoceanography and Paleoclimatology, 39, e2024PA004965.

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.

La planta media europea de residuos a energía (WtE) se define sobre la base del tratamiento de los residuos sólidos urbanos (MSW) medios europeos. El tratamiento térmico de una sola fracción de residuos como el papel o el plástico o incluso residuos específicos como la poliamida 6 no se realiza en realidad en una planta WtE para MSW. Los residuos se homogeneizan siempre para obtener un valor calorífico relativo constante y cumplir con las normas de emisión. No obstante, el modelo utilizado y los ajustes utilizados para el RMS medio permiten atribuir la carga ambiental (emisiones y también el consumo de recursos de los auxiliares) a la producción de energía, así como los créditos (exportación de chatarra de metal) a una sola fracción o residuos específicos incinerados dentro de un RMS medio.Por lo tanto, los datos de LCI son válidos para el tratamiento de los residuos específicos dentro de un RMS promedio (la proporción de la fracción de residuos del RMS se muestra en el gráfico circular de abajo, la composición elemental en el primer cuadro a continuación). La siguiente descripción de la tecnología explica los ajustes y la tecnología de la planta de WtE promedio utilizada para generar el conjunto de datos LCI. El valor calorífico neto y la composición elemental de la fracción de residuos o residuos específicos se muestran en los cuadros siguientes (véase la columna correspondiente en las tablas). El conjunto de datos cubre todos los pasos/tecnologías relevantes del proceso a lo largo de la cadena de suministro del inventario de la cuna a puerta representada con una buena calidad general de datos. El inventario se basa principalmente en datos de la industria y se completa, cuando sea necesario, con datos secundarios. Sinónimos: Residuos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidad técnica: Servicio estándar de tratamiento al final de su vida útil para una fracción de residuos específica mediante tratamiento térmico. Representación geográfica: UE-27 La planta media europea de residuos a energía (WtE) se define sobre la base del tratamiento de los residuos sólidos urbanos (MSW) medios europeos. El tratamiento térmico de una sola fracción de residuos como el papel o el plástico o incluso residuos específicos como la poliamida 6 no se realiza en realidad en una planta WtE para MSW. Los residuos se homogeneizan siempre para obtener un valor calorífico relativo constante y cumplir con las normas de emisión. No obstante, el modelo utilizado y los ajustes utilizados para el RMS medio permiten atribuir la carga ambiental (emisiones y también el consumo de recursos de los auxiliares) a la producción de energía, así como los créditos (exportación de chatarra de metal) a una sola fracción o residuos específicos incinerados dentro de un RMS medio. Por lo tanto, los datos de LCI son válidos para el tratamiento de los residuos específicos dentro de un RMS promedio (la proporción de la fracción de residuos del RMS se muestra en el gráfico circular de abajo, la composición elemental en el primer cuadro a continuación).La siguiente descripción de la tecnología explica los ajustes y la tecnología de la planta de WtE promedio utilizada para generar el conjunto de datos LCI. El valor calorífico neto y la composición elemental de la fracción de residuos o residuos específicos se muestran en los cuadros siguientes (véase la columna correspondiente en las tablas). El conjunto de datos cubre todos los pasos/tecnologías relevantes del proceso a lo largo de la cadena de suministro del inventario de la cuna a puerta representada con una buena calidad general de datos. El inventario se basa principalmente en datos de la industria y se completa, cuando sea necesario, con datos secundarios. Sinónimos: Residuos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidad técnica: Servicio estándar de tratamiento al final de su vida útil para una fracción de residuos específica mediante tratamiento térmico. Representación geográfica: UE-27 La planta media europea de residuos a energía (WtE) se define sobre la base del tratamiento de los residuos sólidos urbanos (MSW) medios europeos. El tratamiento térmico de una sola fracción de residuos como el papel o el plástico o incluso residuos específicos como la poliamida 6 no se realiza en realidad en una planta WtE para MSW. Los residuos se homogeneizan siempre para obtener un valor calorífico relativo constante y cumplir con las normas de emisión. No obstante, el modelo utilizado y los ajustes utilizados para el RMS medio permiten atribuir la carga ambiental (emisiones y también el consumo de recursos de los auxiliares) a la producción de energía, así como los créditos (exportación de chatarra de metal) a una sola fracción o residuos específicos incinerados dentro de un RMS medio.Por lo tanto, los datos de LCI son válidos para el tratamiento de los residuos específicos dentro de un RMS promedio (la proporción de la fracción de residuos del RMS se muestra en el gráfico circular de abajo, la composición elemental en el primer cuadro a continuación). La siguiente descripción de la tecnología explica los ajustes y la tecnología de la planta de WtE promedio utilizada para generar el conjunto de datos LCI. El valor calorífico neto y la composición elemental de la fracción de residuos o residuos específicos se muestran en los cuadros siguientes (véase la columna correspondiente en las tablas). El conjunto de datos cubre todos los pasos/tecnologías relevantes del proceso a lo largo de la cadena de suministro del inventario de la cuna a puerta representada con una buena calidad general de datos.El inventario se basa principalmente en datos de la industria y se completa, cuando sea necesario, con datos secundarios. Sinónimos: Residuos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidad técnica: Servicio estándar de tratamiento al final de su vida útil para una fracción de residuos específica mediante tratamiento térmico. Representación geográfica: UE-27 A central europeia média de resíduos para energia (WtE) é definida com base no tratamento da média europeia de resíduos sólidos urbanos (MSW). O tratamento térmico de uma única fração de resíduos, como papel ou plástico, ou mesmo resíduos específicos, como a poliamida 6, não é, na realidade, feito numa instalação WtE para RSU. Os resíduos são sempre homogeneizados para obter um poder calorífico constante relativo e para cumprir as normas de emissão. No entanto, o modelo utilizado e os parâmetros utilizados para os RSU médios permitem atribuir a carga ambiental (emissões e também o consumo de recursos dos auxiliares) à produção de energia, bem como os créditos (exportação de sucata metálica) a uma única fração ou a resíduos específicos incinerados dentro de um RSU médio. Por conseguinte, os dados do ICM são válidos para o tratamento dos resíduos específicos no âmbito de um RSU médio (a parte da fração de resíduos dos RSU é apresentada no gráfico de tartes abaixo, a composição elementar no primeiro quadro abaixo). A descrição tecnológica a seguir explica as configurações e a tecnologia da fábrica média de WtE utilizada para gerar o conjunto de dados do LCI. O poder calorífico inferior e a composição elementar da fração de resíduos ou dos resíduos específicos são apresentados nos quadros abaixo (ver coluna correspondente nos quadros). O conjunto de dados abrange todas as etapas/tecnologias relevantes do processo ao longo da cadeia de abastecimento do inventário do berço ao portão representado, com uma boa qualidade geral dos dados. O inventário baseia-se principalmente em dados da indústria e é completado, sempre que necessário, por dados secundários. Sinônimos: Resíduos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidade técnica: Serviço padrão de tratamento em fim de vida para uma fração de resíduos específica através de tratamento térmico. Representação geográfica:UE-27Os resíduos são sempre homogeneizados para obter um poder calorífico constante relativo e para cumprir as normas de emissão. No entanto, o modelo utilizado e os parâmetros utilizados para os RSU médios permitem atribuir a carga ambiental (emissões e também o consumo de recursos dos auxiliares) à produção de energia, bem como os créditos (exportação de sucata metálica) a uma única fração ou a resíduos específicos incinerados dentro de um RSU médio. Por conseguinte, os dados do ICM são válidos para o tratamento dos resíduos específicos no âmbito de um RSU médio (a parte da fração de resíduos dos RSU é apresentada no gráfico de tartes abaixo, a composição elementar no primeiro quadro abaixo). A descrição tecnológica a seguir explica as configurações e a tecnologia da fábrica média de WtE utilizada para gerar o conjunto de dados do LCI. O poder calorífico inferior e a composição elementar da fração de resíduos ou dos resíduos específicos são apresentados nos quadros abaixo (ver coluna correspondente nos quadros).O conjunto de dados abrange todas as etapas/tecnologias relevantes do processo ao longo da cadeia de abastecimento do inventário do berço ao portão representado, com uma boa qualidade geral dos dados. O inventário baseia-se principalmente em dados da indústria e é completado, sempre que necessário, por dados secundários. Sinônimos: Resíduos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidade técnica: Serviço padrão de tratamento em fim de vida para uma fração de resíduos específica através de tratamento térmico. Representação geográfica: UE-27 L'impianto medio europeo di Waste-to-Energy (WtE) è definito in base al trattamento dei rifiuti solidi urbani medi europei (MSW). Il trattamento termico di una singola frazione di scarto come carta o plastica o anche rifiuti specifici come la poliammide 6 non viene fatto in realtà in un impianto WtE per MSW. I rifiuti vengono sempre omogeneizzati per ottenere un relativo potere calorifico costante e per rispettare gli standard di emissione. Tuttavia, il modello utilizzato e le impostazioni utilizzate per la RMS media consentono di attribuire l'onere ambientale (emissioni e anche il consumo di risorse di energia ausiliari) nonché i crediti (esportazione di rottami metallici) a una singola frazione o a rifiuti specifici inceneriti all'interno di una RMS media. Pertanto i dati LCI sono validi per il trattamento dei rifiuti specifici all'interno di una MSW media (la quota di frazione di rifiuti del MSW è mostrata nel grafico a torta sottostante, la composizione elementare nella prima tabella sottostante). La seguente descrizione della tecnologia spiega le impostazioni e la tecnologia dell'impianto WtE medio utilizzato per generare il set di dati LCI. Il potere calorifico netto e la composizione elementare della frazione di rifiuto o dei rifiuti specifici sono riportati nelle tabelle sottostanti (cfr. colonna corrispondente nelle tabelle). Il set di dati copre tutte le fasi/tecnologie di processo rilevanti lungo la catena di approvvigionamento dell'inventario della culla per gate rappresentata con una buona qualità complessiva dei dati. L'inventario si basa principalmente sui dati del settore ed è completato, ove necessario, da dati secondari. Sinonimi: Rifiuti di materie plastiche (Nylon 6 GF 30, Nylon 66 GF 30) Scopo tecnico: Servizio di trattamento standard di fine vita per una frazione specifica di rifiuti tramite trattamento termico. Rappresentanza geografica:UE-27I rifiuti vengono sempre omogeneizzati per ottenere un relativo potere calorifico costante e per rispettare gli standard di emissione. Tuttavia, il modello utilizzato e le impostazioni utilizzate per la RMS media consentono di attribuire l'onere ambientale (emissioni e anche il consumo di risorse di energia ausiliari) nonché i crediti (esportazione di rottami metallici) a una singola frazione o a rifiuti specifici inceneriti all'interno di una RMS media. Pertanto i dati LCI sono validi per il trattamento dei rifiuti specifici all'interno di una MSW media (la quota di frazione di rifiuti del MSW è mostrata nel grafico a torta sottostante, la composizione elementare nella prima tabella sottostante). La seguente descrizione della tecnologia spiega le impostazioni e la tecnologia dell'impianto WtE medio utilizzato per generare il set di dati LCI. Il potere calorifico netto e la composizione elementare della frazione di rifiuto o dei rifiuti specifici sono riportati nelle tabelle sottostanti (cfr. colonna corrispondente nelle tabelle).Il set di dati copre tutte le fasi/tecnologie di processo rilevanti lungo la catena di approvvigionamento dell'inventario della culla per gate rappresentata con una buona qualità complessiva dei dati. L'inventario si basa principalmente sui dati del settore ed è completato, ove necessario, da dati secondari. Sinonimi: Rifiuti di materie plastiche (Nylon 6 GF 30, Nylon 66 GF 30) Scopo tecnico: Servizio di trattamento standard di fine vita per una frazione specifica di rifiuti tramite trattamento termico. Rappresentanza geografica: UE-27 Media europeană a deșeurilor în energie (WtE) este definită pe baza tratării deșeurilor municipale solide medii europene (MSW). Tratamentul termic al unei singure fracții de deșeuri, cum ar fi hârtia sau plasticul sau chiar deșeurile specifice, cum ar fi Polyamide 6, nu se realizează în realitate într-o instalație WtE pentru MSW. Deșeurile sunt întotdeauna omogenizate pentru a obține o putere calorifică relativ constantă și pentru a respecta standardele de emisie. Cu toate acestea, modelul utilizat și setările utilizate pentru MSW medii permit atribuirea sarcinii de mediu (emisii și, de asemenea, consumul de resurse al agenților auxiliari), precum și a creditelor (exportul deșeurilor metalice) unei singure fracții sau deșeurilor specifice incinerate în cadrul unui MSW mediu. Prin urmare, datele LCI sunt valabile pentru tratarea deșeurilor specifice într-un mediu MSW (partea fracției de deșeuri din MSW este prezentată în diagrama plăcintă de mai jos, compoziția elementară din primul tabel de mai jos). Următoarea descriere a tehnologiei explică setările și tehnologia uzinei medii WtE utilizate pentru generarea setului de date LCI. Puterea calorifică netă și compoziția elementară a fracțiunii de deșeuri sau a deșeurilor specifice sunt prezentate în tabelele de mai jos (a se vedea coloana corespunzătoare din tabele). Setul de date acoperă toate etapele/tehnologiile relevante ale procesului de-a lungul lanțului de aprovizionare al inventarului leagan în poarta reprezentată, cu o bună calitate generală a datelor. Inventarul se bazează în principal pe date din industrie și este completat, dacă este necesar, de date secundare. Sinonime: Deșeuri în energie ale materialelor plastice (Nylon 6 GF 30, Nylon 66 GF 30) Scop tehnic: Serviciul standard de tratare la sfârșitul ciclului de viață pentru o fracțiune specifică de deșeuri prin tratare termică. Reprezentarea geografică:UE-27Deșeurile sunt întotdeauna omogenizate pentru a obține o putere calorifică relativ constantă și pentru a respecta standardele de emisie. Cu toate acestea, modelul utilizat și setările utilizate pentru MSW medii permit atribuirea sarcinii de mediu (emisii și, de asemenea, consumul de resurse al agenților auxiliari), precum și a creditelor (exportul deșeurilor metalice) unei singure fracții sau deșeurilor specifice incinerate în cadrul unui MSW mediu. Prin urmare, datele LCI sunt valabile pentru tratarea deșeurilor specifice într-un mediu MSW (partea fracției de deșeuri din MSW este prezentată în diagrama plăcintă de mai jos, compoziția elementară din primul tabel de mai jos). Următoarea descriere a tehnologiei explică setările și tehnologia uzinei medii WtE utilizate pentru generarea setului de date LCI. Puterea calorifică netă și compoziția elementară a fracțiunii de deșeuri sau a deșeurilor specifice sunt prezentate în tabelele de mai jos (a se vedea coloana corespunzătoare din tabele).Setul de date acoperă toate etapele/tehnologiile relevante ale procesului de-a lungul lanțului de aprovizionare al inventarului leagan în poarta reprezentată, cu o bună calitate generală a datelor. Inventarul se bazează în principal pe date din industrie și este completat, dacă este necesar, de date secundare. Sinonime: Deșeuri în energie ale materialelor plastice (Nylon 6 GF 30, Nylon 66 GF 30) Scop tehnic: Serviciul standard de tratare la sfârșitul ciclului de viață pentru o fracțiune specifică de deșeuri prin tratare termică. Reprezentarea geografică: UE-27 Європейський середній завод з виробництва відходів (WtE) визначається на основі поводження з середньоєвропейськими твердими побутовими відходами (ТПВ). Термічна обробка однієї фракції відходів, таких як папір, пластик або навіть специфічні відходи, такі як поліамід 6, насправді не проводиться на заводі WtE для ТПВ. Відходи завжди гомогенізуються, щоб отримати відносну постійну теплотворну цінність і відповідати стандартам викидів.Тим не менш, використовувана модель і використовувані параметри для середньої ТПВ дозволяють віднести екологічне навантаження (викиди, а також споживання ресурсів допоміжних засобів) виробництва енергії, а також кредити (експорт металобрухту) до однієї фракції або конкретних відходів, спалених в межах середньої ТПВ. Тому дані LCI дійсні для обробки конкретних відходів в межах середнього ТПВ (частка відходів ТПВ показана в круговій діаграмі нижче, елементарної композиції в першій таблиці нижче). Наступний опис технології пояснює налаштування та технологію середнього заводу WtE, який використовується для створення набору даних LCI. Чиста теплотворна цінність і елементарний склад фракції відходів або конкретних відходів показані в таблицях нижче (див. відповідну колонку в таблицях). Набір даних охоплює всі відповідні етапи процесу / технології в ланцюжку поставок представленої колиски до інвентаризації воріт з хорошою загальною якістю даних. Інвентаризація в основному базується на галузевих даних і завершується, коли це необхідно, вторинними даними. Синоніми: Відходи до енергії пластмас (Нейлон 6 GF 30, нейлон 66 GF 30) Технічне призначення: Стандартний термін служби обробки для конкретної фракції відходів шляхом термічної обробки. Географічне представництво: ЄС-27 Європейський середній завод з виробництва відходів (WtE) визначається на основі поводження з середньоєвропейськими твердими побутовими відходами (ТПВ). Термічна обробка однієї фракції відходів, таких як папір, пластик або навіть специфічні відходи, такі як поліамід 6, насправді не проводиться на заводі WtE для ТПВ.Відходи завжди гомогенізуються, щоб отримати відносну постійну теплотворну цінність і відповідати стандартам викидів. Тим не менш, використовувана модель і використовувані параметри для середньої ТПВ дозволяють віднести екологічне навантаження (викиди, а також споживання ресурсів допоміжних засобів) виробництва енергії, а також кредити (експорт металобрухту) до однієї фракції або конкретних відходів, спалених в межах середньої ТПВ. Тому дані LCI дійсні для обробки конкретних відходів в межах середнього ТПВ (частка відходів ТПВ показана в круговій діаграмі нижче, елементарної композиції в першій таблиці нижче). Наступний опис технології пояснює налаштування та технологію середнього заводу WtE, який використовується для створення набору даних LCI. Чиста теплотворна цінність і елементарний склад фракції відходів або конкретних відходів показані в таблицях нижче (див. відповідну колонку в таблицях). Набір даних охоплює всі відповідні етапи процесу / технології в ланцюжку поставок представленої колиски до інвентаризації воріт з хорошою загальною якістю даних.Інвентаризація в основному базується на галузевих даних і завершується, коли це необхідно, вторинними даними. Синоніми: Відходи до енергії пластмас (Нейлон 6 GF 30, нейлон 66 GF 30) Технічне призначення: Стандартний термін служби обробки для конкретної фракції відходів шляхом термічної обробки. Географічне представництво: ЄС-27 Den genomsnittliga europeiska avfalls-till-energianläggningen (WtE) definieras på grundval av behandlingen av genomsnittligt kommunalt fast avfall i Europa. Termisk behandling av en enda avfallsfraktion som papper eller plast eller till och med specifikt avfall som Polyamid 6 sker inte i verkligheten i en WtE-anläggning för hushållsavfall. Avfallet är alltid homogeniserat för att uppnå ett relativt konstant värmevärde och uppfylla utsläppsnormerna. Den använda modellen och de använda inställningarna för det genomsnittliga maskinavfallet gör det dock möjligt att tillskriva energiproduktionen (utsläpp och resursförbrukning) energiproduktion samt krediter (export av metallskrot) till en enda fraktion eller specifikt avfall som förbränns inom ett genomsnittligt kommunalt avfall. LCI-uppgifterna är därför giltiga för behandling av det specifika avfallet inom ett genomsnittligt kommunalt avfall (avfallsfraktionens andel av kommunalt avfall visas i cirkeldiagrammet nedan, den elementära sammansättningen i den första tabellen nedan). Följande teknikbeskrivning förklarar inställningarna och tekniken för den genomsnittliga WtE-anläggningen som används för att generera LCI-datauppsättningen. Nettovärmevärdet och den elementära sammansättningen av avfallsfraktionen eller det specifika avfallet visas i tabellerna nedan (se motsvarande kolumn i tabellerna). Datauppsättningen omfattar alla relevanta processsteg/tekniker över leveranskedjan för den representerade vaggan till grindinventering med en god övergripande datakvalitet. Inventeringen baseras huvudsakligen på branschdata och kompletteras vid behov med sekundärdata. Synonymer: Avfall till energi från plast (Nylon 6 GF 30, Nylon 66 GF 30) Tekniskt syfte: Standardbehandlingstjänst för uttjänta produkter för en specifik avfallsfraktion genom termisk behandling. Geografisk representation:EU-27Avfallet är alltid homogeniserat för att uppnå ett relativt konstant värmevärde och uppfylla utsläppsnormerna. Den använda modellen och de använda inställningarna för det genomsnittliga maskinavfallet gör det dock möjligt att tillskriva energiproduktionen (utsläpp och resursförbrukning) energiproduktion samt krediter (export av metallskrot) till en enda fraktion eller specifikt avfall som förbränns inom ett genomsnittligt kommunalt avfall. LCI-uppgifterna är därför giltiga för behandling av det specifika avfallet inom ett genomsnittligt kommunalt avfall (avfallsfraktionens andel av kommunalt avfall visas i cirkeldiagrammet nedan, den elementära sammansättningen i den första tabellen nedan). Följande teknikbeskrivning förklarar inställningarna och tekniken för den genomsnittliga WtE-anläggningen som används för att generera LCI-datauppsättningen. Nettovärmevärdet och den elementära sammansättningen av avfallsfraktionen eller det specifika avfallet visas i tabellerna nedan (se motsvarande kolumn i tabellerna).Datauppsättningen omfattar alla relevanta processsteg/tekniker över leveranskedjan för den representerade vaggan till grindinventering med en god övergripande datakvalitet. Inventeringen baseras huvudsakligen på branschdata och kompletteras vid behov med sekundärdata. Synonymer: Avfall till energi från plast (Nylon 6 GF 30, Nylon 66 GF 30) Tekniskt syfte: Standardbehandlingstjänst för uttjänta produkter för en specifik avfallsfraktion genom termisk behandling. Geografisk representation: EU-27 Eiropas vidējo atkritumu pārvēršanas elektrostaciju (WtE) nosaka, pamatojoties uz Eiropas vidējo cieto sadzīves atkritumu (MSW) apstrādi. Vienas atkritumu frakcijas, piemēram, papīra vai plastmasas, vai pat specifisku atkritumu, piemēram, poliamīda 6, termiskā apstrāde faktiski netiek veikta WtE rūpnīcā CSA vajadzībām. Atkritumus vienmēr homogenizē, lai iegūtu relatīvi nemainīgu siltumspēju un atbilstu emisiju standartiem.Tomēr vidējais CSA izmantotais modelis un izmantotie iestatījumi ļauj attiecināt vides slogu (palīgierīču emisijas un arī resursu patēriņu) enerģijas ražošanu, kā arī kredītus (metālu lūžņu eksportu) vienai frakcijai vai konkrētiem atkritumiem, kas sadedzināti vidējos CSA. Tāpēc DII dati ir derīgi, lai apstrādātu konkrētos atkritumus vidējā CSA (atkritumu frakcijas daļa ir norādīta zem pīrāga diagrammas, pirmās tabulas elementārais sastāvs). Tālāk sniegtais tehnoloģiju apraksts izskaidro vidējās WtE rūpnīcas iestatījumus un tehnoloģiju, ko izmanto DII datu kopas ģenerēšanai. Atkritumu frakcijas vai īpašo atkritumu zemākā siltumspēja un elementārais sastāvs ir parādīts tabulās zem (sk. attiecīgo sleju tabulās). Datu kopa aptver visus attiecīgos procesa posmus/tehnoloģijas visā pārstāvētā “no šūpuļa līdz vārtiem” krājumu piegādes ķēdē ar labu vispārējo datu kvalitāti. Inventarizācija galvenokārt balstās uz nozares datiem, un vajadzības gadījumā to papildina ar sekundāriem datiem. Sinonīmi: Plastmasas atkritumi enerģijā (Nylon 6 GF 30, Nylon 66 GF 30) Tehniskais mērķis: Standarta poligona beigu apstrādes pakalpojums konkrētai atkritumu frakcijai, izmantojot termisko apstrādi. Ģeogrāfiskā pārstāvība: ES-27 Eiropas vidējo atkritumu pārvēršanas elektrostaciju (WtE) nosaka, pamatojoties uz Eiropas vidējo cieto sadzīves atkritumu (MSW) apstrādi. Vienas atkritumu frakcijas, piemēram, papīra vai plastmasas, vai pat specifisku atkritumu, piemēram, poliamīda 6, termiskā apstrāde faktiski netiek veikta WtE rūpnīcā CSA vajadzībām.Atkritumus vienmēr homogenizē, lai iegūtu relatīvi nemainīgu siltumspēju un atbilstu emisiju standartiem. Tomēr vidējais CSA izmantotais modelis un izmantotie iestatījumi ļauj attiecināt vides slogu (palīgierīču emisijas un arī resursu patēriņu) enerģijas ražošanu, kā arī kredītus (metālu lūžņu eksportu) vienai frakcijai vai konkrētiem atkritumiem, kas sadedzināti vidējos CSA. Tāpēc DII dati ir derīgi, lai apstrādātu konkrētos atkritumus vidējā CSA (atkritumu frakcijas daļa ir norādīta zem pīrāga diagrammas, pirmās tabulas elementārais sastāvs). Tālāk sniegtais tehnoloģiju apraksts izskaidro vidējās WtE rūpnīcas iestatījumus un tehnoloģiju, ko izmanto DII datu kopas ģenerēšanai. Atkritumu frakcijas vai īpašo atkritumu zemākā siltumspēja un elementārais sastāvs ir parādīts tabulās zem (sk. attiecīgo sleju tabulās). Datu kopa aptver visus attiecīgos procesa posmus/tehnoloģijas visā pārstāvētā “no šūpuļa līdz vārtiem” krājumu piegādes ķēdē ar labu vispārējo datu kvalitāti.Inventarizācija galvenokārt balstās uz nozares datiem, un vajadzības gadījumā to papildina ar sekundāriem datiem. Sinonīmi: Plastmasas atkritumi enerģijā (Nylon 6 GF 30, Nylon 66 GF 30) Tehniskais mērķis: Standarta poligona beigu apstrādes pakalpojums konkrētai atkritumu frakcijai, izmantojot termisko apstrādi. Ģeogrāfiskā pārstāvība: ES-27 Evropski povprečni obrat za odpadno energijo (WtE) je opredeljen na podlagi obdelave povprečnih evropskih komunalnih trdnih odpadkov. Toplotna obdelava posamezne frakcije odpadkov, kot sta papir ali plastika ali celo posebni odpadki, kot je poliamid 6, se v obratu za komunalne odpadke dejansko ne izvaja. Odpadki se vedno homogenizirajo, da se dobi relativna konstantna kalorična vrednost in da se upoštevajo emisijski standardi. Kljub temu uporabljeni model in uporabljene nastavitve za povprečne komunalne odpadke omogočajo, da se okoljska obremenitev (emisije in tudi poraba virov pomožnih pripomočkov) kot tudi dobropisi (izvoz odpadnih kovin) pripišejo eni sami frakciji ali posebnim odpadkom, ki se sežigajo v povprečnih komunalnih odpadkih. Zato podatki ISD veljajo za obdelavo določenih odpadkov v okviru povprečnega komunalnega komunalnega odpadkov (delež frakcij odpadkov v komunalnih odpadkih je prikazan v spodnjem tortnem diagramu, osnovna sestava v prvi tabeli spodaj). V naslednjem opisu tehnologije so pojasnjene nastavitve in tehnologija povprečne tovarne WtE, ki se uporablja za ustvarjanje nabora podatkov ISD. Neto kalorična vrednost in osnovna sestava frakcije odpadkov ali posebnih odpadkov sta prikazani v spodnjih tabelah (glej ustrezni stolpec v tabelah). Podatkovni niz zajema vse ustrezne korake/tehnologije postopka v dobavni verigi zastopane zibelke do inventarja z dobro splošno kakovostjo podatkov. Popis temelji predvsem na podatkih industrije in je po potrebi dopolnjen s sekundarnimi podatki. Sopomenke: Pridobivanje energije iz plastike (Nylon 6 GF 30, najlon 66 GF 30) Tehnični namen: Standardna storitev obdelave ob koncu življenjske dobe za določeno frakcijo odpadkov s toplotno obdelavo. Geografska zastopanost:EU-27Odpadki se vedno homogenizirajo, da se dobi relativna konstantna kalorična vrednost in da se upoštevajo emisijski standardi. Kljub temu uporabljeni model in uporabljene nastavitve za povprečne komunalne odpadke omogočajo, da se okoljska obremenitev (emisije in tudi poraba virov pomožnih pripomočkov) kot tudi dobropisi (izvoz odpadnih kovin) pripišejo eni sami frakciji ali posebnim odpadkom, ki se sežigajo v povprečnih komunalnih odpadkih. Zato podatki ISD veljajo za obdelavo določenih odpadkov v okviru povprečnega komunalnega komunalnega odpadkov (delež frakcij odpadkov v komunalnih odpadkih je prikazan v spodnjem tortnem diagramu, osnovna sestava v prvi tabeli spodaj). V naslednjem opisu tehnologije so pojasnjene nastavitve in tehnologija povprečne tovarne WtE, ki se uporablja za ustvarjanje nabora podatkov ISD. Neto kalorična vrednost in osnovna sestava frakcije odpadkov ali posebnih odpadkov sta prikazani v spodnjih tabelah (glej ustrezni stolpec v tabelah).Podatkovni niz zajema vse ustrezne korake/tehnologije postopka v dobavni verigi zastopane zibelke do inventarja z dobro splošno kakovostjo podatkov. Popis temelji predvsem na podatkih industrije in je po potrebi dopolnjen s sekundarnimi podatki. Sopomenke: Pridobivanje energije iz plastike (Nylon 6 GF 30, najlon 66 GF 30) Tehnični namen: Standardna storitev obdelave ob koncu življenjske dobe za določeno frakcijo odpadkov s toplotno obdelavo. Geografska zastopanost: EU-27 Ο ευρωπαϊκός μέσος ευρωπαϊκός σταθμός παραγωγής ενέργειας (WtE) ορίζεται με βάση την επεξεργασία των μέσων ευρωπαϊκών αστικών στερεών αποβλήτων (MSW). Η θερμική επεξεργασία ενός μόνο κλάσματος αποβλήτων όπως το χαρτί ή το πλαστικό ή ακόμη και συγκεκριμένα απόβλητα όπως το Polyamide 6 δεν γίνεται στην πραγματικότητα σε μονάδα WtE για MSW. Τα απόβλητα είναι πάντοτε ομογενοποιημένα ώστε να επιτυγχάνεται σχετική σταθερή θερμογόνος δύναμη και να συμμορφώνονται με τα πρότυπα εκπομπών. Ωστόσο, το χρησιμοποιούμενο μοντέλο και οι χρησιμοποιούμενες ρυθμίσεις για το μέσο MSW επιτρέπουν την απόδοση της περιβαλλοντικής επιβάρυνσης (εκπομπές και κατανάλωση πόρων από βοηθητικούς φορείς) της παραγωγής ενέργειας, καθώς και των πιστώσεων (εξαγωγή μεταλλικών απορριμμάτων) σε ένα μόνο κλάσμα ή σε συγκεκριμένα απόβλητα που αποτεφρώνονται μέσα σε ένα μέσο MSW. Ως εκ τούτου, τα δεδομένα LCI είναι έγκυρα για την επεξεργασία των συγκεκριμένων αποβλήτων στο πλαίσιο ενός μέσου MSW (το μερίδιο του κλάσματος αποβλήτων του MSW εμφανίζεται στο διάγραμμα πίτας κάτω, η στοιχειώδης σύνθεση στον πρώτο πίνακα κατωτέρω). Η ακόλουθη περιγραφή της τεχνολογίας εξηγεί τις ρυθμίσεις και την τεχνολογία του μέσου εργοστασίου WtE που χρησιμοποιείται για τη δημιουργία του συνόλου δεδομένων LCI. Η κατώτερη θερμογόνος δύναμη και η στοιχειώδης σύνθεση του κλάσματος αποβλήτων ή των ειδικών αποβλήτων παρουσιάζονται στους παρακάτω πίνακες (βλ. αντίστοιχη στήλη στους πίνακες). Το σύνολο δεδομένων καλύπτει όλα τα σχετικά στάδια/τεχνολογίες της διαδικασίας σε όλη την αλυσίδα εφοδιασμού του αντιπροσωπευόμενου λίκνου έως την πύλη απογραφής με καλή συνολική ποιότητα δεδομένων. Η απογραφή βασίζεται κυρίως σε δεδομένα του κλάδου και συμπληρώνεται, όπου είναι απαραίτητο, με δευτερεύοντα στοιχεία. Συνώνυμα: Απόβλητα σε ενέργεια από πλαστικά (νάυλον 6 GF 30, νάυλον 66 GF 30) ΤΕΧΝΙΚΟΣ ΣΚΟΠΟΣ: Τυποποιημένη υπηρεσία επεξεργασίας στο τέλος του κύκλου ζωής ενός συγκεκριμένου κλάσματος αποβλήτων μέσω θερμικής επεξεργασίας. Γεωγραφική Αντιπροσώπευση: ΕΕ-27 La moyenne européenne des déchets à l’énergie (WtE) est définie sur la base du traitement des déchets solides municipaux (MSW) européens moyens. Le traitement thermique d’une fraction de déchets unique comme le papier ou le plastique ou même des déchets spécifiques comme Polyamide 6 ne se fait pas en réalité dans une usine WtE pour MSW. Les déchets sont toujours homogénéisés pour obtenir un pouvoir calorifique relativement constant et pour se conformer aux normes d’émission. Néanmoins, le modèle utilisé et les paramètres utilisés pour le MSW moyen permettent d’attribuer la charge environnementale (émissions et consommation de ressources des auxiliaires) ainsi que les crédits (exportation de déchets métalliques) à une seule fraction ou à des déchets spécifiques incinérés dans un MSW moyen. Par conséquent, les données de l’ICL sont valables pour le traitement des déchets spécifiques à l’intérieur d’un MSW moyen (la part de la fraction de déchets du MSW est indiquée dans le tableau ci-dessous, la composition élémentaire dans le premier tableau ci-dessous). La description de la technologie suivante explique les paramètres et la technologie de l’usine WtE moyenne utilisée pour générer l’ensemble de données LCI. Le pouvoir calorifique net et la composition élémentaire de la fraction de déchets ou des déchets spécifiques sont indiqués dans les tableaux ci-dessous (voir la colonne correspondante dans les tableaux). L’ensemble de données couvre toutes les étapes/technologies pertinentes du processus sur la chaîne d’approvisionnement de l’inventaire de berceau à porte représenté avec une bonne qualité globale des données. L’inventaire est principalement basé sur les données de l’industrie et est complété, le cas échéant, par des données secondaires. Synonymes: Déchets énergétiques des matières plastiques (Nylon 6 GF 30, Nylon 66 GF 30) Objet technique: Service standard de traitement en fin de vie d’une fraction de déchets spécifique par traitement thermique. Représentation géographique: EU-27

Tree cores were sampled using increment borers. At each site three trees were chosen for coring, with two or three cores taken per tree. Cores were sanded and ring widths measured based on high-resolution images of the sanded cores. Cores were cross-dated and summary statistics used to compare cross-dating accuracy. The dataset contains the resulting dated ring width series. This dataset includes tree ring width data, derived from tree cores, that were sampled from sites across the Rhön Biosphere Reserve (Germany). At each chosen site three trees were cored, with two or three cores taken per cored tree. Data was collected in August 2021.