• Energy Research
• 7. Clean energy close
• 3. Good health close
• DE close
• IT close

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).

SMARTIE1 is a joint seismological experiment of the Karlsruhe Institute of Technology (KIT) and the Leipzig University. We installed in total 36 seismic stations as ring-like and profile-like measurements near to a single wind turbine (WT) at the Fraunhofer Institute for Chemical Technology (ICT) in Pfinztal, SW Germany, for 21 days. The main goals of this project are a better understanding of a single WT as a seismic source and the development of propagation models for the WT-induced seismic signals, depending on the geological properties. Waveform data are available from the GEOFON data centre, under network code X8 (under CC-BY 4.0 license according to GIPP-rules).

This repository contains the raw data and code to replicate the results in the paper "The impact of carbon prices on renewable energy support" by Mirjam Kosch and Jan Abrell

Mit einem Anteil von rund 30% am Endenergieverbrauch und etwa 20% an den CO2-Emissionen haben private Haushalte in Deutschland einen großen Einfluss auf die Umwelt. Gleichzeitig sind private Haushalte ein zentraler Adressat für politische Interventionen zur Bekämpfung des Klimawandels. Vor diesem Hintergrund hat die Politik zahlreiche Maßnahmen zur Verringerung des Energiekonsums und zur Förderung regenerativer Energietechnologien ergriffen. Diese politischen Maßnahmen bedürfen einer sorgfältigen Evaluierung ihrer Effektivität und Kosteneffizienz, um kostspielige Redundanzen durch sich überlappende Instrumente zu vermeiden. Eine solche Evaluation umwelt- und energiepolitischer Maßnahmen erfordert eine umfangreiche Datenbasis. Besonders im Bereich der privaten Haushalte waren solche Daten in Deutschland bislang nicht verfügbar. Die Reagibilität deutscher Haushalte auf Maßnahmen zur Bekämpfung des Klimawandels war daher weitgehend unbekannt. Das Sozial-Ökologische Panel stellt zu diesem Zweck umfangreiche, frei verfügbare Informationen zum Energieverbrauch und Umweltverhalten privater Haushalte bereit. Die Befragung wurde in vier Wellen durchgeführt. Es liegen Daten für die Jahre 2012, 2013, 2014 und 2015 vor. Diese Daten können anhand einer ID aneinander gespielt werden. Darauf aufbauend können ökonometrische Schätzungen und Analysen verschiedener Präferenzindikatoren sowie des Anpassungsverhaltens privater Haushalte an den Klimawandel durchgeführt werden. Dieser Datensatz umfasst die Daten der Erhebung im Jahr 2012. With a share of 30% in total final energy consumption and around 20% in CO2 emissions, private households in Germany strongly affect the environment. At the same time private households are an important target group for policy interventions to fight climate change. Against this background, numerous policy measures that intend to decrease energy consumption and to support renewable energy technologies have been introduced. These policy measures call for accurate evaluation to avoid expensive redundancies due to overlapping policy instruments. The evaluation of energy and environmental policy measures requires comprehensive and reliable data. So far such data was unavailable in Germany, especially in the context of private households. Hence, the responsiveness of German households to climate protection policies was unknown. For this purpose, the Socio-Ecological Panel offers rich information on household’s energy consumption and environmental behavior. The data was gathered in four household surveys conducted in 2012, 2013, 2014 and 2015. The survey waves can be merged using the household ID. The data builds the basis for empirical analyses of households’ adaptation to climate change and the evaluation of environmental and climate policy measures. This data set comprises the information gathered in the 2012 survey wave and is labelled in German. It is available in German and English. Offline Rekrutierung für das repräsentative forsa omninet panel Selbst ausgefüllter Fragebogen Self-completed questionnaire 10.000 deutsche Haushalte Green-SÖP Green-SÖP

We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.

This dataset underpins the study "Synergies and conflicts of energy development and water security in Africa". The study provides insights into energy supply and demand, power generation, investments and total system costs, water consumption and withdrawal as well as carbon dioxide emissions for the African continent. We developed a model to evaluate energy supply and water requirements to cover the energy needs of the African continent during the period 2015-2065. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS. The objective function is to minimise total energy system costs, rather than, for example, co-optimise the energy and water sectors. Other energy resources were also included in the model except for adding the water analysis, and the dataset was updated based on the latest available information. The OSeMOSYS model developed to conduct the study “Energy projections for African countries”, itself extended from the Electricity Model Base for Africa (TEMBA), was further extended, included exports for all fuels, water loss due to evaporation in hydropower plants and more scenarios examined. Furthermore, the latest available data on the energy system of Africa was also updated. The TEMBA model produces aggregate energy, and detailed power system results in each country in the African continent. The power sector results are also reported with power pool aggregation. The OSeMOSYS model and input data used to produce these results can be found at KTH-dESA/jrc_temba: TEMBA 2.1 (Version v2.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4889373 (Authors: Ioannis Pappis, Vignesh Sridharan, Will Usher, & Mark Howells. (2021). The initial study was funded by the Joint Research Centre of the European Commission (contract number C936531 - JRC/PTT/2018/C.7/0038/NC).

OSeEM-SN is a tool constructed using Oemof Tabular to apply cross-sectoral approaches for analyzing 100% renewable and sector-coupled sub-national energy systems. The model is validated using the case study of Schleswig-Holstein, Germany. To run the scripts, you need to install Oemof Tabular using the following command- pip install oemof.tabular For details on Oemof Tabular please go through the documentation- https://oemof-tabular.readthedocs.io/ The model uses Oemof-Solph, a model generator for energy system modelling and optimisation. The oemof.solph package is part of the Open energy modelling framework (Oemof). This an organisational framework to bundle tools for energy system modelling. Details on Oemof-Solph is described here- https://github.com/oemof/oemof-solph The article based on the OSeEM-SN based analysis of Schleswig-Holstein is available at- https://www.mdpi.com/2071-1050/13/7/3852 The complete model, including all datasets, scripts, and results are available at: Details are also available at: https://github.com/znes/OSeEM-SN If you have any questions about the model, please contact- mnimaruf@gmail.com

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Temporal extent The map is representative for ca. 2018. Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. The dataset features area and mass for different street types area and mass for different rail types area and mass for other infrastructure area, volume and mass for different building types Masses are reported as total values, and per material category. Units area in m² height in m volume in m³ mass in t for infrastructure and buildings Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.