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This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7 The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).

We have gathered data on the power generation of seven different PV modules from three demonstration sites in Oslo, Touzer, and Sevilla for a comprehensive analysis. This data was sourced from TIGO cloud for the PV modules and Solcast, an open-source platform, for historical weather information. The data set is spanning from May 2021 to November 2023. These datasets are characterized by high-resolution recordings taken every 5 minutes.

{"references": ["UNSD Demographic Statistics, available at http://data.un.org", "The World Bank GDP data, available at https://data.worldbank.org/", "UNFCCC: Greenhouse Gas Inventory Data, available at https://unfccc.int/process/transparency-and-reporting/greenhouse-gas-data/what-is-greenhouse-gas-data"]} Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2021-12-03. The dataset is also available via datalad. To obtain the dataset with datalad, see the instructions at https://github.com/mikapfl/unfccc_di_data .

The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

Absorption Koeffizient der farbigen detritalen Substanz bei 443nm (adg in m^-1 bei 4 km Auflösung): Der Absorptionskoeffizient adg stellt den Anteil des einfallenden Lichts dar, das sowohl von detritalen Partikeln als auch von farbiger gelöster organischer Substanz (CDOM) absorbiert wird. Gelöste organische Substanz ist ein wichtiger Bestandteil des ozeanischen Kohlenstoffkreislaufs. Es wird auch als Proxy verwendet, um die Auswirkungen von Terrigenous Inputs in Küstengewässern zu bewerten. Συντελεστής απορρόφησης της χρωματισμένης αποτριχωτικής ύλης στα 443nm (adg σε m^-1 σε ανάλυση 4 km): Ο συντελεστής απορρόφησης adg αντιπροσωπεύει το κλάσμα του προσπίπτοντος φωτός που απορροφάται τόσο από τα διακριτικά σωματίδια όσο και από τη χρωματισμένη διαλυμένη οργανική ύλη (CDOM). Η διαλυμένη οργανική ύλη είναι ένα σημαντικό συστατικό του ωκεάνιου κύκλου του άνθρακα. Χρησιμοποιείται επίσης ως υποκατάστατο για την εκτίμηση των επιπτώσεων των εδαφικών εισροών στα παράκτια ύδατα. Współczynnik absorpcji barwnej substancji detrytalnej przy 443 nm (adg w m^-1 przy rozdzielczości 4 km): Współczynnik absorpcji adg reprezentuje ułamek padającego światła pochłanianego zarówno przez cząstki detrytalne, jak i przez kolorowe rozpuszczone substancje organiczne (CDOM). Rozpuszczone materia organiczna jest ważnym składnikiem oceanicznego cyklu węgla. Jest on również wykorzystywany jako wskaźnik zastępczy do oceny wpływu czynników atmosferycznych w wodach przybrzeżnych. Coeficientul de absorbție al materiei detritale colorate la 443nm (adg în m^-1 la o rezoluție de 4 km): Coeficientul de absorbție adg reprezintă fracțiunea de lumină incidentă absorbită atât de particulele detritale, cât și de materia organică colorată dizolvată (CDOM). Materia organică dizolvată este o componentă importantă a ciclului carbonului oceanic. Acesta este, de asemenea, utilizat ca indicator pentru a evalua impactul factorilor de producție terrigeni în apele costiere. Assorbiment Koeffiċjent tal-materja detritali kkulurita f’443nm (adg f’m^-1 b’riżoluzzjoni ta’ 4 km): Il-koeffiċjent ta’ assorbiment adg jirrappreżenta l-frazzjoni ta’ dawl inċidentali assorbit kemm minn partiċelli detritali kif ukoll minn materja organika maħlula kkulurita (CDOM). Il-materja organika maħlula hija komponent importanti taċ-ċiklu tal-karbonju oċeaniku. Tintuża wkoll bħala indikatur biex jiġi vvalutat l-impatt tal-inputs terriġenużi fl-ilmijiet kostali. Coefficiente di assorbimento della materia detritale colorata a 443nm (adg in m^-1 a risoluzione di 4 km): Il coefficiente di assorbimento adg rappresenta la frazione di luce incidente assorbita sia dalle particelle detritali che dalla materia organica disciolta colorata (CDOM). La materia organica disciolta è una componente importante del ciclo del carbonio oceanico. Viene anche utilizzato come proxy per valutare l'impatto degli input terrigeni nelle acque costiere. Coeficiente de absorción de materia detrital de color a 443 nm (adg en m^-1 a 4 km de resolución): El coeficiente de absorción adg representa la fracción de luz incidente absorbida tanto por partículas detritales como por materia orgánica disuelta coloreada (CDOM). La materia orgánica disuelta es un componente importante del ciclo del carbono oceánico. También se utiliza como representante para evaluar el impacto de los insumos territoriales en las aguas costeras. Коефициент на абсорбция на цветна детритална материя при 443nm (adg в m^-1 при разделителна способност 4 km): Коефициентът на поглъщане adg представлява частта от падащата светлина, абсорбирана както от детритните частици, така и от оцветената разтворена органична материя (CDOM). Разтворената органична материя е важен компонент на океанския въглероден цикъл. Той се използва и като заместител за оценка на въздействието на теригенните суровини в крайбрежните води. Coefficient d’absorption de la matière détritique colorée à 443nm (adg en m^-1 à 4 km de résolution): Le coefficient d’absorption adg représente la fraction de lumière incidente absorbée à la fois par les particules détritales et par la matière organique dissoute colorée (CDOM). La matière organique dissoute est une composante importante du cycle du carbone océanique. Il sert également de proxy pour évaluer l’impact des apports terriens dans les eaux côtières. Absorptiecoëfficiënt van gekleurde detritale materie bij 443nm (adg in m^-1 bij 4 km resolutie): De absorptiecoëfficiënt adg vertegenwoordigt de fractie van invallend licht geabsorbeerd door zowel detritale deeltjes als gekleurd opgelost organisch materiaal (CDOM). Opgelost organisch materiaal is een belangrijk onderdeel van de oceanische koolstofcyclus. Het wordt ook gebruikt als volmacht om de impact van terrigeneuze inputs in kustwateren te beoordelen.

Assumptions for this work was collected and the analysis was completed in FY22. This contains information for more than 20 types of medium and heavy duty vehicles. Vehicles with various levels of hybridization, electric and fuel cell powertrains are considered in this work. More details are available in the report published by Argonne accessible from https://vms.taps.anl.gov/research-highlights/u-s-doe-vto-hfto-r-d-benefits/. TechScape, a convenient data visualization tool is also provided by Argonne for this data, accessible from [TechScape Web](https://vms.taps.anl.gov/data/techscape-web-2023/).

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper: Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019 Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de). Climate change impact data File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries. File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action). In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019). Climate change mitigation cost data The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2]. File 4: REMIND_scenario_results_economic_data.csv File 5: REMIND_scenarios_climate_data.csv Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature. In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios. The first dimension specifies the climate policy regime (delayed action, baseline scenarios): 1xx: climate action from 2010 5xx: climate action from 2015 2xx climate action from 2020 (used in this study) 3xx climate action from 2030 4x1 weak policy baseline (before Paris agreement) The second dimension specifies the technology portfolio and assumptions: x1x Full technology portfolio (used in this study) x2x noCCS: unavailability of CCS x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed x4x NucPO: phase out of investments into nuclear energy x5x Limited SW: penetration of solar and wind power limited x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases) x6x noBECCS: unavailability of CCS in combination with bioenergy The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.). xx1 0$/tCO2 (baseline) xx2 10$/tCO2 xx3 30$/tCO2 xx4 50$/tCO2 xx5 100$/tCO2 xx6 200$/tCO2 xx7 500$/tCO2 xx8 40$/tCO2 xx9 20$/tCO2 xx0 5$/tCO2 For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price). [1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a. [2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains: 2521 historical floods with impact data (1870-2020); 237 further historical floods with significant impacts, but without precise impact data (1950-2020) Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020). Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders): HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020) HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020) HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020) HANZE_list_of_references.csv - List of all references used in the catalogues HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods Regions_v2010_simplified.zip - Map of subnational regions (v2010) Regions_v2021_simplified.zip - Map of subnational regions (regions v2021) v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected. This work was supported by the German Research Foundation (DFG) through project "Decomposition of flood losses by environmental and economic drivers" (FloodDrivers), project no. 449175973