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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.

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

The dataset CO2_flux_measurements_Acoculco contains data on CO2 fluxes, coordinates (UTM), air temperature, atmospheric pressure measured in selected sites belonging to the Acoculco Geothermal Field: in particular, the areas named Lagunilla, Alcaparrosa, Los Azufres and also the area between them were investigated. CO2 flux measurements were performed using the accumulation chamber method. The dataset Field_meas_Acoculco_waters reports the ID, coordinates (UTM), Altitude (m.a.s.l.), temperature, flow rate, pH, Electrical Conductivity and Dissolved Oxygen for water samples collected in the central sector of the Acoculco geothermal field, but also in other sectors located inside and outside the Acoculco caldera. Total depth is also included for samples collected from water wells. The dataset Chemical_isotopic_data_Acoculco_waters reports major and minor chemical components and stable isotopic composition for hydrogen and oxygen determined in collected water samples in Acoculco geothermal field. Calculated partial pressures (in bars and log10-value) and CO2 concentrations of dissolved CO2 were also included. The dataset Chemical_isotopic_data_Acoculco_gas reports chemical and isotopic data for collected samples from Los Azufres and Alcaparrosa natural gas manifestations.

This dataset entails various structural material data that was used to provide additional evidence for arguments presented in publication "Deposition of Sn-Zr-Se precursor by thermal evaporation and PLD for the synthesis of SnZrSe3 thin films". Mainly data consists of: SEM, XRD, Raman, Auger and TGA raw data. Summary of results is provided in Extended_data.pdf file

Overview The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished. Scenario The considered network scenario includes 8 three- (_results_gcas.csv) or one-cell (_results_scas.csv & _results_kras.csv) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. Methodology The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs. Simulation setup The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the Related works section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day. Results The results of the aforementioned investigations have been included in the attached files, which can be described as follows: File _results_gcas.csv The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. Files _results_scas.csv & _results_kras.csv The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively. Acknowledgment More details about the conducted studies have been described within the attached papers (Related works section). The data has been collected within the COST CA10210 INTERACT. M. Deruyck is a Post-Doctoral Fellow of the FWO-V (Research Foundation – Flanders, ref: 12Z5621N). The work (including the following dataset preparation) by A. Samorzewski and A. Kliks was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.

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

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

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