• Energy Research
• DE close
• FR close
• IT close

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.

This data publication offers updated data about low-carbon energy technology (LCET) patents and citations links to the scientific literature. Compared to a [previous version](https://doi.org/10.4119/unibi/2941555), it also contains data on biofuels and fuels from waste technologies. The updated version also contains the code (R-scripts) that have been used to (1) compile the data and (2) to reproduce the statistical analysis including figures and tables presented in the final paper Hötte, Pichler, Lafond (2021): "The rise of science in low-carbon energy technologies", RSER. DOI: [10.1016/j.rser.2020.110654](10.1016/j.rser.2020.110654). This data publication contains different data sets (in .RData and (long-term archivable) .tsv format). Further information about each data set is provided in more detail below. - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID, Paper year, cited (binary 0-1, indicates whether the paper is cited by a patent). - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number, Patent year (grant year), CPC class, Patent date, Patent title, citing_to_science (binary 0-1, indicates whether the patent is citing to science). - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number, Patent year (grant year), Technology type, CPC class, Patent date, Patent title. - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing, cited (Patent numbers). - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number, Paper ID, Patent year, Paper year, Technology type, WoS field, Patent title, Paper title, DOI, Confidence Score, Citation type, Reference type, Journal/ Conf. name, Journal ID, Conference ID, CPC class, Patent date, US patent. - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing, cited (Patent numbers). Moreover, this data publication contains a folder "code" with 2 subfolders: - "R_code_create_data" contains the R-scripts used to create the data sample. - "R_code_plots_and_figures" contains all R-scripts used to make the statistical analyses presented in the text (including figures and tables). Please check the read-me documents in the code folder for further detail. ### License and terms of use ### This data is licensed under the CC BY 4.0 license. See: https://creativecommons.org/licenses/by/4.0/legalcode Please find the full license text below. If you want to use the data, do not forget to give appropriate credit by citing this article: Kerstin Hötte, Anton Pichler, François Lafond, The rise of science in low-carbon energy technologies, Renewable and Sustainable Energy Reviews, Volume 139, 2021. https://doi.org/10.1016/j.rser.2020.110654 ### LCET definition and concepts ### LCET are defined by Cooperative Patent Classification (CPC) codes. CPC offers "tags" that are assigned to patents that are useful for the adaptation and mitigation of climate chagen. LCET are identified by YO2E codes, i.e. that are assigned to technologies that contribute to the "REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION". Only the subset of Y02E01 ("Energy generation through renewable energy sources"), Y02E03 ("Energy generation of nuclear origin") and Y02E5 ("Technologies for the production of fuel of non-fossil origin") technologies are used. 10 different LCET are distinguished: Solar PV, Wind, Solar thermal, Ocean power, Hydroelectric, Geothermal, Biofuels, Fuels from waste, Nuclear fission and Nuclear fusion. More information about the Y02-tags can be found in: Veefkind, Victor, et al. "A new EPO classification scheme for climate change mitigation technologies." World Patent Information 34.2 (2012): 106-111. DOI: [https://doi.org/10.1016/j.wpi.2011.12.004](https://doi.org/10.1016/j.wpi.2011.12.004) ### Data sources and compilation ### The data was generated by the merge of different data sets. 1.) Patent data from USPTO was downloaded here: https://bulkdata.uspto.gov/ 2.) Complementary data on grant year and patent title was taken from: https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data 3.) Citations to science come from the Reliance on Science (RoS) data set https://zenodo.org/record/3685972 (v23, Feb. 24, 2020) DOI: 10.5281/zenodo.3685972 The directory ("code") offers the R-scripts that were used to process MAG data and to link it to patent data. The header of the R-scripts offer additional technical information about the subsetting procedures and data retrieval. For more information about the patent data, see: Pichler, A., Lafond, F. & J, F. D. (2020), Technological interdependencies predict innovation dynamics, Working paper pp. 1–33. URL: [https://arxiv.org/abs/2003.00580](https://arxiv.org/abs/2003.00580) For more information about MAG data, see: Marx, Matt, and Aaron Fuegi. "Reliance on science: Worldwide front‐page patent citations to scientific articles." Strategic Management Journal 41.9 (2020): 1572-1594. DOI: [https://doi.org/10.1002/smj.3145](https://doi.org/10.1002/smj.3145) Marx, Matt and Fuegi, Aaron, Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. Boston University Questrom School of Business Research Paper No. 3331686. DOI: [http://dx.doi.org/10.2139/ssrn.3331686 ](http://dx.doi.org/10.2139/ssrn.3331686 ) ### Detailed information about the data ### - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID: Unique paper-identifier used by MAG Paper year: Year of publication cited: binary 0-1, indicates whether the paper is cited by a patent, citation links are made in the text body and front-page of the patent, and added by examiners and applicants. - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number: Number given by USPTO. Can be used for manual patent search in http://patft.uspto.gov/netahtml/PTO/srchnum.htm (numeric) Patent year: Year when the patent was granted (numeric) CPC class: Detailed 8-digit CPC code (numeric) Patent date: Exact date of patent granting (numeric) Patent title: Short title (character) citing_to_science: binary 0-1, indicates whether the patent is citing to science as identified by citation links in RoS. (numeric) - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number: (numeric) Patent year: (numeric) Technology type: Short code used to tag 10 different types of LCET (pv, (nuclear) fission, (solar) thermal, (nuclear) fusion, wind, geo(termal), sea (ocean power), hydro, biofuels, (fuels from) waste) (character) CPC class: Detailed 8-digit CPC code (character) Patent date: (numeric) Patent title: (numeric) - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number: see above (numeric) Paper ID: see above (numeric) Patent year: see above (numeric) Paper year: see above (numeric) Technology type: see above (character) WoS field: Web of Science field of research, WoS fields were probabilistically assigned to papers and are used as given by RoS (character) Patent title: see above (character) Paper title: Title of scientific article (character) DOI: Paper DOI if available (character) Confidence Score: Reliability score of citation link (numeric). Links were probabilistically assigned. See Marx and Fuegi 2019 for further detail. Citation type: Indicates whether citation made in text body of patent document or its front page (character) Reference type: Examiner or applicant added citation link (or unknown). (character) Journal/ Conf. name: Name of journal or conference proceeding where the cited paper was published (character) Journal ID: Journal identifier in MAG (numeric) Conference ID: Conference identifier in MAG (numeric) CPC class: see above (character) Patent date: see above (numeric) US patent: binary US-patent indicator as provided by RoS (numeric) - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) **Note:** The citation links were probabilistically retrieved. During the analysis, we identified manually some false-positives are removed them from the "LCET_subset_with_metainfo_final.RData" data set. The list is available, too: "list_of_false_positives.tsv" We do not claim to have a perfect coverage, but expect a precision of >98% as described by Marx and Fuegi 2019. ### Statistics about the data ### Full data set: - #papers in MAG: 179,083,029 - #all patents: 10,160,667 - #citing patents: 2,058,233 - #cited papers: 4,404,088 - #citation links from patents to papers: 34,959,193 LCET subset: - #LCET patents: 65,305 - #citing LCET patents: 22,017 - #cited papers: 103,645 - #citation links from LCET patents to papers: 396,504 Meta-information: Papers: - Publication year, 251 Web-of-Science (WoS) categories, Journal/ conference proceedings name, DOI, Paper title Patents: - Grant year, >240,000 hierarchical CPC classes, 10 LCET types Citation links: - Reference type, citation type, reliability score If you have further questions about the data or suggestions, please contact: **kerstin.hotte@oxfordmartin.ox.ac.uk** ### Acknowledgements ### The authors want to thank the Center for Research Data Management of Bielefeld University and in particular Cord Wiljes for excellent support. ### License issues ### Terms of use of the source data: - Reliance on Science data [https://zenodo.org/record/3685972](https://zenodo.org/record/3685972), Open Data Commons Attribution License (ODC-By) v1.0, https://opendatacommons.org/licenses/by/1.0/ - "Google Patents Public Data” by IFI CLAIMS Patent Services and Google (https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data), Creative Commons Attribution 4.0 International License (CC BY 4.0), https://console.cloud.google.com/marketplace/details/google_patents_public_datasets/google-patents-public-data - USPTO patent data (https://bulkdata.uspto.gov/), see: https://bulkdata.uspto.gov/data/2020TermsConditions.docx

The PV-Live dataset comprises data from a network of 40 solar irradiance measurement stations across the German state of Baden-Württemberg. All stations measure global horizontal irradiance and temperature with a pyranometer, and global tilted irradiance in east, south, and west direction with tilt angles of 25° with three photovoltaic reference cells in minute resolution. A quality control scheme has been developed specifically for this dataset and is applied to the measurements before publication. The minute resolution irradiance and temperature measurements are published with the derived quality flags. A description of the dataset and the quality control scheme is given in Lorenz et al. (2022) and Lorenz et al. (2020). The dataset contains data from September 2020 onwards. It will be continuously extended by adding data of the previous month on a monthly basis. QUALITY FLAGS Two types of flags are provided with the data. Each sensor is assigned a general quality flag, which is based on a combination of different tests. In addition, a shading flag is given, which is not sensor specific, i.e. it applies to all irradiance sensors simultaneously. The flags can have the levels 'passed, all tests complete', 'passed, not all tests complete', 'failed tests, likely erroneous' or 'failed tests, most likely erroneous'. If the flag level of the general quality flags is 'failed tests, most likely erroneous' the corresponding measurement value is set to NaN. DATA FORMAT The data is published as monthly .zip archives. Each archive contains the following files: 1. Tab separated data files (tng000XX_YYYY-MM.tsv) for each station, containing measurements and quality flags of one month 2. Tab separated station location metadata (metadata_stations_YYYY-MM.tsv) 3. Metadata of the dataset describing variable names and quality flag levels (metadata_measurements.json) 4. General comments on data availability and quality for the month (comments_quality_control_YYYY-MM.txt) 5. Log file of changes (change_log_YYYY-MM.txt) Station location metadata is given on a monthly basis because stations can be relocated. Therefore, we recommend to use the metadata valid for its corresponding month. VERSION UPDATES Version 7: - Recalculation of height information for all stations - One station has been moved to a new, nearby location on 16th September 2021 ACKNOWLEDGEMENTS The data have been collected and processed by Fraunhofer ISE in the framework of PV-Live, a project in cooperation with TransnetBW. We thank our station partners for cooperation in installing and maintaining our measurement stations: EnBW Solar, Badenova, Pohlen Solar, Oekogeno Solar7, Ecovision, Hochschule Ulm, Hofgemeinschaft Heggelbach, Soltechnics-solution and the Stadtwerke Karlsruhe, Grünstadt, Buchen, Crailsheim, Schwäbisch Hall, Pforzheim, Konstanz, Waldshut-Tiengen, Schwäbisch Gmünd, Ravensburg, Eberbach, Baden-Baden.

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

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

L'empreinte carbone des technologies numériques est une préoccupation depuis plusieurs années. Cela concerne principalement la consommation électrique des datacenters; beaucoup de fournisseurs dans le domaine du cloud s'engagent à n'utiliser que des sources d'énergie renouvelables. Cependant, cette approche néglige la phase de fabrication des composants des infrastructures numériques. Nous considérons dans ce travail de recherche la question du dimensionnement des énergies renouvelables pour une infrastructure de type cloud géographiquement distribuée autour de la planète, considérant l'impact carbone à la fois de l'électricité issue du réseau électrique local en fonction de la location de sa production, et de la fabrication des panneaux photovoltaïques et des batteries pour la part renouvelable de l'alimentation des ressources. Nous avons modélisé ce problème de minimisation de l'impact carbone d'une telle infrastructure cloud sous la forme d'un programme linéaire. La solution est le dimensionnement optimal d'une fédération de cloud sur une année complète en fonction des localisations des datacenters, des traces réelles des travaux à exécuter et valeurs d'irradiation solaire heure par heure. Nos résultats montrent une réduction de l'impact carbone de 30% comparés à la même architecture cloud totalement alimentée par des énergies renouvelables et 85% comparés à un modèle qui n'utiliserait qu'une alimentation via le réseau local d'électricité. The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model. Données computationnelles ou de simulation: En tenant compte des données en entrée (description de la fédération de centres de données, fichiers de configuration appropriés, conditions météorologiques, etc.), le logiciel est capable de proposer un dimensionnement optimal pour la fédération des datacenters à faible émission de carbone distribuée à l'échelle mondiale : surface des panneaux photovoltaïques et capacité des batteries pour chaque datacenter de la fédération. Des scripts sont disponibles pour mettre en forme les solutions proposées. Simulation or computational data: Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration. Audience: Research, Policy maker UpdatePeriodicity: as needed

Complete replication data and code for article "A Meta-Analysis of Country Level Studies on Environmental Change and Migration". The rdata file contains both the meta and country level data. The data is also saved separately as xlsx files.

The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time.