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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 data publication provides two datasets of turbulent kinetic energy dissipation rates sampled during the MomarSat 2022 cruise. One dataset was gathered with a deep autonomous Vertical Microstructure Profiler (VMP-6000). The second dataset was gathered with the MicroRiYo mooring as described in the reference paper (Ferron et al. 2024). The two datasets, one for each instrument, are available as tar files. Each tar file contains fourteen NetCDF files. Each NetCDF file contains the dissipation rate profile, the time (UTC) of the profile start, the geographical position (deployment of the VMP or mooring position), and the mean pressure for each dissipation rate estimate (two estimates at each pressure level from the two shear sensors). Each dissipation rate comes with a quality control matrix QC (14 x 4) that characterizes how the associated mean shear spectrum fitted the expected theoretical Nasmyth spectrum: QC( 1:10, 1 ) : Value of the 10 criteria used (see reference paper) for the dissipation rates of shear 1. QC( 1:10, 2 ): Criteria met (=1) or not met (=0) for shear 1 dissipation rates. QC(11,1): Same criteria as QC(10,1) expressed in terms of mean absolute deviation (MAD) instead of variance (see Lueck et al. 2022) (shear 1). QC(11,2): state whether criteria QC(11,1) is met (=1) or not met (=0) (shear 1). QC(12,1): Number of shear spectra averaged to compute one dissipation rate estimate (shear 1). QC(12,2): Number of accelerometer used to remove vibrations (Goodman et al. 2006; Lueck et al. 2022; Ferron et al. 2023) (shear 1) QC(13,1): MAD (shear 1) QC(13,2): unused QC(14,1): index of first used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(14,2): index of last used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(:,3): same as QC(:,1) for shear 2. QC(:,4): same as QC(:,2) for shear 2. Shear data were processed following the processing flow chart of the Atomix SCOR Working Group 160 (https://wiki.app.uib.no/atomix/index.php?title=Flow_chart_for_shear_probes). References: Ferron, B., S. Leizour, M. Hamon, O. Peden, 2024: MicroRiYo : An observing system for deep repeated profiles of kinetic energy dissipation rates from shear-microstructure turbulence along a mooring line, submitted to J. Atmos. Ocean. Tech. Lueck, R. G., 2022: The Statistics of Oceanic Turbulence Measurements. Part II: Shear Spectra and a New Spectral Model. J. Atmos. Oceanic Technol., 39, 1273–1282, https://doi.org/10.1175/JTECH-D-21-0050.1.

The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

This repository contains a series of .csv files developed for the study titled "Plant canopies promote climatic disequilibrium in Mediterranean recruit communities", authored by: Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcántara JM and Verdú M. The author of these files is Perez-Navarro MA. These files are used to characterize species niches, estimate climatic disequilibrium for recruit communities growing under plant canopies and open spaces, and conduct statistical analyses. Variables description of each table is compiled in the METADATA.txt file. Please visit Github readme () to correctly place these files in the folder tree and check for the corresponding scripts where they are required. Please notice that although alternative approaches were calibrated to estimate species niche (accordingly producing multiple niche, distances and disequilibrium dataframes), only niche centroid calibrated discarding 95 percentile of lowest niche density was used for paper results and figures. Also, in case of univariate analyses only bio01, bio06 and bio12 were used in analyses, though species niche and further niche and community estimations were obtained for all 19 variables. This is version 2 (v2) and include extra intermediate .csv required to run all the R scripts included in the abovementioned Github repository. NAs or empty cells present in the .csv files of this repository means no data and do not contribute to the analyses. Visit METADATA.txt file for variables description. These data are under CC0 license. It is possible to share, copy and redistribute the material in any medium or format, and adapt, remix, transform, and build upon the material for any purpose. Studies using R scripts or any data files from these study should cite the abovementioned paper (Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcantara JM, Verdu M. (2024). Plant canopies promote climatic disequilibrium in Mediterranean recruit communities). Please contact m.angeles582@gmail.com in case of having doubts or problems with the existing files and scripts. Current rates of climate change are exceeding the capacity of many plant species to track climate, thus leading communities to be in disequilibrium with climatic conditions. Plant canopies can contribute to this disequilibrium by buffering macro-climatic conditions and sheltering poorly adapted species to the oncoming climate, particularly in their recruitment stages. Here we analyze differences in climatic disequilibrium between understory and open ground woody plant recruits in 28 localities, covering more than 100,000 m2, across an elevation range embedding temperature and aridity gradients in the southern Iberian Peninsula. This study demonstrates higher climatic disequilibrium under canopies compared with open ground, supporting that plant canopies would affect future community climatic lags by allowing the recruitment of less arid-adapted species in warm and dry conditions, but also it endorse that canopies could favor warm-adapted species in extremely cold environments as mountain tops, thus pre-adapting communities living in these habitats to climate change.

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.

This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.MOHC.HadGEM3-GC31-MM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

These data record the results of processing otter trawl catches (OTSB14; Merrett & Marshall, 1980) from the National Oceanography Centre (NOC, UK) long-term study of the Porcupine Abyssal Plain (PAP), including the PAP-Sustained Observatory time-series. The data concern catches recovered during the RRS Challenger cruise 135 in 1997. Billett, D.S.M. et al. (1998). RRS Challenger Cruise 135, 15 Oct-30 Oct 1997. BENGAL: High resolution temporal and spatial study of the BENthic biology and Geochemistry of a north-eastern Atlantic abyssal Locality. Southampton Oceanography Centre Cruise Report, No. 19, 49pp.| https://www.bodc.ac.uk/resources/inventories/cruise_inventory/reports/ch135_97.pdf