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Supplementary material for the publication " Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions.

The dataset "RoRCC" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information: 1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle.

- EddyPro v6 and v7 for flux calculations [https://www.licor.com/env/products/eddy_covariance/eddypro] - bico for the conversion of binary raw data files to ASCII (2013-2016, 2020-2022) [https://gitlab.ethz.ch/flux/bico] - fluxrun for the flux calculation using EddyPro (2013-2016, 2020-2022) [https://gitlab.ethz.ch/flux/fluxrun] - Various versions of FCT (flux calculation using EddyPro) were used for years 1997-2004 and 2017-2019 [https://gitlab.ethz.ch/holukas/fct-flux-calculation-tool] - scop v0.1 (self-heating correction for open-path IRGAs) for the self-heating correction of IRGA75 fluxes [https://gitlab.ethz.ch/holukas/scop] - diive v0.21.0 (legacy version) for file merging, quality control, storage correction, outlier removal [https://gitlab.ethz.ch/diive/diive-legacy/-/tree/v0.21.0] - ReddyProc v1.2.2 for application of the constant ustar threshold, MDS gap-filliing and partitioning, in R Studio v1.3.959 [https://cran.r-project.org/web/packages/REddyProc/index.html]

Structural, optoelectronic, photovoltaic, and supplementary characterization data for “Benzylammonium-Mediated Formamidinium Lead Iodide Perovskite Phase Stabilization for Photovoltaics”, DOI:10.1002/adfm.202101163. Figure_2_XRD.zip: Data described in Figure 2 (XRD patterns) as Origin (.opj) software file. Figure_3_NMR_data.zip: Data described in Figure 3 (NMR spectra) in the file structure of the TopSpin software, which is available from Bruker. Figure_4_spectra.zip: Data described in Figure 4 (UV-vis absorption, PL and IPCE spectra) as Origin (.opj) software files. Figure_5_PV.zip: Data described in Figure 5 (photovoltaic characterization) as Origin (.opj) software files. Figure_6_spectra.zip: Data described in Figure 6 (PLQY and TRPL) as Origin (.opj) and *.csv files. Figure_7_stability.zip: Data described in Figure 7 (stability analysis) as Origin (.opj) software files. Figure_SI.zip: Data described in the Supporting Information Figures S1, S2, S3, S5, and S6 (XRD data, reciprocal space maps, radial profiles of q-maps, UV-vis absorption spectra, PL spectra, and additional photovoltaic characterization) as Origin (.opj), text (.txt), and image (.tiff) files.

Variation in food resources can result in dramatic fluctuations in the body condition of animals dependent on those resources. Decreases in body mass can disrupt patterns of energy allocation and impose stress, thereby altering immune function. In this study we investigated links between changes in body mass of captive cane toads (Rhinella marina), their circulating white blood cell populations, and their performance in immune assays. Captive toads that lost weight over a 3-month period had increased levels of monocytes and heterophils and reduced levels of eosinophils. Basophil and lymphocyte levels were unrelated to changes in mass. Because individuals that lost mass had higher heterophil levels but stable lymphocyte levels, the ratio of these cell types was also higher, partially consistent with a stress response. Phagocytic ability of whole blood was higher in toads that lost mass, due to increased circulating levels of phagocytic cells. Other measures of immune performance were unrelated to mass change. These results highlight the challenges faced by invasive species as they expand their range into novel environments which may impose substantial seasonal changes in food availability that were not present in the native range. Individuals facing energy restrictions may shift their immune function towards more economical and general avenues of combating pathogens.

This dataset contains the underlying data for the following publication: Song, L., Lieu, J., Nikas, A., Arsenopoulos, A., Vasileiou, G., & Doukas, H. (2020). Contested energy futures, conflicted rewards? Examining low-carbon transition risks and governance dynamics in China's built environment. Energy Research & Social Science, 59, 101306., https://doi.org/10.1016/j.erss.2019.101306. Full details of methods used to create the dataset and provided within this publication.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

pre-built Euro-Calliope Ready to use models of the European electricity system built using Calliope. Models are available on three different spatial resolutions: continental, national, and regional. In addition, euro-calliope models can be built manually which adds more configuration options. To build euro-calliope manually, head over to GitHub. At a glance euro-calliope models the European electricity system with each location representing an administrative unit. It is built on three spatial resolutions: on the continental level as a single location, on the national level with 34 locations, and on the regional level with 497 locations. On each node, renewable generation capacities (wind, solar, bioenergy) and balancing capacities (battery, hydrogen) can be built. In addition, hydro electricity and pumped hydro storage capacities can be built up to the extent to which they exist today. All capacities are used to satisfy electricity demand on all locations which is based on historic data. Locations are connected through transmission lines of unrestricted capacity. Using Calliope, the model is formulated as a linear optimisation problem with total monetary cost of all capacities as the minimisation objective. The pre-built models can be manipulated by updating any of the files. In addition to the pre-built models, models can be built manually. Manual builds provide more flexibility in adapting and configuring the model. To build euro-calliope manually, head over to GitHub. Get ready to run the models You need a Gurobi license installed on your computer. You may as well choose another solver than Gurobi. See Calliope���s documentation to understand how to switch to another solver. You need to have Calliope and Gurobi installed in your environment. The easiest way to do so is using conda. Using conda, you can create a conda environment from within you can build the model: conda env create -f environment.yaml conda activate euro-calliope Run the models There are three models in this directory ��� one for each of the three spatial resolutions continental, national, and regional. You can run all three models out-of-the-box, but you may want to modify the model. By default, the model runs for the first day of January only. To run the example model on the continental resolution type: $ calliope run ./continental/example-model.yaml For more information on how to use and modify Calliope models, see Calliope���s documentation. Manipulating the model using overrides Calliope overrides allow to easily manipulate models. An override named freeze-hydro-capacities can be used for example in this way: calliope run build/model/continental/example-model.yaml --scenario=freeze-hydro-capacities You can define your own overrides to manipulate any model component. The following overrides are built into euro-calliope: directional-rooftop-pv By default, euro-calliope contains a single technology for rooftop PV. This technology comprises the total rooftop PV potential in each location, in particular including east-, west-, and north-facing rooftops. While this allows to fully exploit the potential of rooftop PV, it leads to less than optimal capacity factors as long as the potential is not fully exploited. That is because, one would likely first exploit all south-facing rooftop, then east- and west-facing rooftops, and only then ��� if at all ��� north-facing rooftops. By default, euro-calliope cannot model that. When using the directional-rooftop-pv override, there are three instead of just one technologies for rooftop PV. The three technologies comprise (1) south-facing and flat rooftops, (2) east- and west-facing rooftops, and (3) north-facing rooftops. This leads to higher capacity factors of rooftop PV as long as the potential of rooftop PV is not fully exploited. However, this also increases the complexity of the model. freeze-hydro-capacities By default, euro-calliope allows capacities of run-of-river hydro, reservoir hydro, and pumped storage hydro capacities up to today���s levels. Alternatively, it���s possible to freeze these capacities to today���s levels using the freeze-hydro-capacities override. Model components The models contain the following files. All files in the root directory are independent of the spatial resolution. All files that depend on the spatial resolution are within subfolders named by the resolution. ��������� {resolution} <- For each spatial resolution an individual folder. ��� ��������� capacityfactors-{technology}.csv <- Timeseries of capacityfactors of all renewables. ��� ��������� directional-rooftop.yaml <- Override discriminating rooftop PV by orientation. ��� ��������� electricity-demand.csv <- Timeseries of electricity demand on each node. ��� ��������� example-model.yaml <- Calliope model definition. ��� ��������� link-all-neighbours.yaml <- Connects neighbouring locations with transmission. ��� ��������� locations.csv <- Map from Calliope location id to name of location. ��� ��������� locations.yaml <- Defines all locations and their max capacities. ��������� build-metadata.yaml <- Metadata of the build process. ��������� demand-techs.yaml <- Definition of demand technologies. ��������� environment.yaml <- Conda file defining an environment to run the model in. ��������� interest-rate.yaml <- Interest rates of all capacities. ��������� link-techs.yaml <- Definition of link technologies. ��������� README.md <- The file you are currently looking at. ��������� renewable-techs.yaml <- Definition of supply technologies. ��������� storage-techs.yaml <- Definition of storage technologies. Units of quantities The units of quantities within the models are the following: power: 100,000 MW energy: 100,000 MWh area: 10,000 km2 monetary cost: 1e+09 EUR These units were chosen in order to minimise numerical issues within the optimisation algorithm. License and attribution euro-calliope has been developed and is maintained by Tim Tr��ndle, IASS Potsdam. If you use euro-calliope in an academic publication, please cite the following article: Tr��ndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Contains modified Copernicus Atmosphere Monitoring Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Contains modified data from Renewables.ninja. Contains modified data from Open Power System Data.

Introduction to the new WeDoWind platform. Presentation at the ASME Wind Digital Solutions Summit on April 24th, 2021 (Part 2: Building the Foundations of the New Digital Wind Industry: Open Source Projects)