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12 results for “Decarbonisation”
Result data related to Tröndle et al (2024): Rebuilding Ukraine's energy supply in a secure, economic, and decarbonised way
<p>This dataset contains the result data of all the scenarios ran in the scientific article "Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way".</p> <p>The results of the main five scenarios of the study are available as PyPSA result files:</p> <ul> <li>nuclear-and-renewables-high.nc: A scenario with nuclear in the mix and high economic growth assumption.</li> <li>nuclear-and-renewables-low.nc: A scenario with nuclear in the mix and low economic growth assumption.</li> <li>only-renewables-high-low-bio.nc: A scenario with only renewables, high economic growth assumption, and only 10% of assumed biomass potential.</li> <li>only-renewables-high.nc: A scenario with only renewables and high economic growth assumption.</li> <li>only-renewables-low.nc: A scenario with only renewables and low economic growth assumption.</li> </ul> <p>See the PyPSA documentation for more information: <a href="https://pypsa.readthedocs.io" target="_blank" rel="noopener">https://pypsa.readthedocs.io</a>.</p> <p>The results of the 330 global sensitivity analysis runs are available as summary files in CSV format:</p> <ul> <li>gsa-capacities-energy-gwh.csv: The installed energy storage capacities for each scenario.</li> <li>gsa-capacities-power-gw.csv: The installed generation capacities for each scenario.</li> <li>gsa-lcoe.csv: The levelised cost of electricity for each scenario.</li> </ul>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
Deep decarbonisation for refineries starts in Ireland: introducing the REALISE pilot campaigns
<p>This webinar shared the cutting-edge research on CO<sub>2</sub> capture solvent stability and solvent management being carried out in a real-life refinery setting in order to support industry’s decarbonisation ambitions. The event introduced the scientists engaged in solvent testing campaigns at Irving Oil Whitegate Refinery in Ireland and SINTEF’s CO<sub>2</sub> laboratories at Tiller in Norway. It included a short film and a panel Q&A. </p> <p><strong>Programme</strong></p> <ul> <li>Welcome, introduction & REALISE CCUS project overview – Peter van Os, TNO</li> <li>Solvent testing in a real-life refinery setting: introduction – Juliana Monteiro, TNO</li> <li>Demonstration at Irving Oil Whitegate Refinery: video premiere and insights – Eirini Skylogianni, TNO</li> <li>Demonstration goals at SINTEF’s Tiller CO<sub>2</sub> labs – Thor Mejdell, SINTEF</li> <li>Panel Q&A</li> </ul>
JRC-TEMBA - African decarbonisation pathways
<p>This dataset underpins the report provided to the project JRC-TEMBA - African decarbonisation pathways.</p> <p>The report 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.</p> <p>The energy supply systems of forty-seven African countries are modelled individually and connected via gas and electricity trade links to identify the cost-optimal solution to satisfy each country´s total final energy demand for the period 2015-2065. In this analysis, The Electricity Model Base for Africa (TEMBA) was extended to include a simple representation of the full energy system. It was also updated to include new data. It is run using the medium- to long-term Open Source Energy Modelling System tool (OSeMOSYS).</p> <p>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.</p> <p>The OSeMOSYS model and input data used to produce these results can be found at <a href="https://github.com/KTH-dESA/jrc_temba">https://github.com/KTH-dESA/jrc_temba</a>.</p>
Identifying cost-effective decarbonisation pathways for South Africa's power sector (EMP-A 2023) - Dataset
<p>Dataset of the project "Identifying cost-effective decarbonisation pathways for South Africa's power sector (EMP-A 2023)".</p>
REINVENT Decarbonisation Innovations Database [Data set]
<p>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.</p> <p>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 <a href="https://static1.squarespace.com/static/59f0cb986957da5faf64971e/t/5d6e590ef37f240001b5ea5d/1567512873422/D2.1+Decarbonisation+innovations+database.pdf">here</a>. </p> <p>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: <a href="https://www.reinvent-project.eu">www.reinvent-project.eu</a>. </p>
Brazilian Starter Data Kit Update to Support the Paper "Integrated Long-Term Expansion Planning and Short-Term Operation Assessment for Decarbonisation Pathways in Brazil Considering Utility-Scale Storage"
<p>This dataset presents updates on the Brazilian starter data kit, considering current load profile data and specific renewable energy sources production, using public data from official Brazilian power system agents. The dataset contains the input data and respective files to run OSeMOSYS and Flextool, including the utility-scale storage model. Additionally, this dataset contains the results of the study "Integrated Long-Term Expansion Planning and Short-Term Operation Assessment for Decarbonisation Pathways in Brazil Considering Utility-Scale Storage". This study evaluates the increase in variable renewable energy in Brazil and its integration with Storage systems. </p>
Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector. Energy Policy (2023). Supplementary Information on Assumptions and Results
<p>This dataset supplements the article with the title "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector", published in Energy Policy. </p> <p>The dataset contains the following:</p> <ul> <li>The Latin Hypercube Sample of the multipliers that are applied to the key input parameters of ETSAP-TIAM in order to generate 1000 different states of the world regarding economic and demographic growth, energy resources potentials, energy technology costs, climate sensitivity and radiative forcing, LULUCF CO<sub>2</sub> sink potential, CO<sub>2</sub> sequestration potential, and decoupling between energy consumption and economic development. The multipliers are sampled from the underlying probability distributions described in the article. </li> <li>The results (at the global scale) from four scenario families for each one of the 1000 wofld states. These scenario families are: <ul> <li>BASE_SSP2: Describes the development of the global energy system consistent with recent trends and policies.</li> <li>2C_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 2 °C as the maximum post-industrial temperature change from 2020 to 2100.</li> <li>2C_SSP2_DA30: Delayed climate action. The climate change mitigation policies of 2C_SSP2 start in 2030. </li> <li>1p5c_OS_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 1.5 °C as the maximum post-industrial temperature change from 2020 onwards to 2100</li> </ul> </li> </ul> <p>Key results included in the dataset are: Temperature change, Radiative Forcing, GHG concentrations, CO2 emissions, Marginal abatement cost, Electricity Supply Primary Energy Consumption, and Annual Total Global Energy System Cost.</p> <p>The dataset also includes sectoral results regarding energy consumption and use, such as shares of different electric uses, shares of hydrogen consumption in end-use sectors, hydrogen supply, Demand electrification by sector, Renewable energy consumption by sector, Alternative fuels consumption in transport, and Total final energy consumption by sector. </p>
Dataset for "The contribution of taxes, subsidies and regulations to British electricity decarbonisation"
<p>Raw and derived results to support the paper ""The contribution of taxes, subsidies and regulations to British electricity decarbonisation".</p> <p>Description and readme contained inside the file.</p>
The potential of decentral heat pumps as flexibility option for decarbonised energy systems: Balmorel input data
<p><strong>Description</strong></p> <p>This dataset holds all Balmorel model input data as well as the Balmorel code used for the scenarios of the paper 'The potential of decentral heat pumps as a flexibility option for Austria's electricity system in 2030' submitted to 'Applied Energy'.</p> <p>The original Balmorel source code is available under https://github.com/balmorelcommunity/Balmorel under the ISC license. It was adapted in the course of this paper.</p> <p><strong>Data format</strong></p> <p>We provide the data in form of the data folders holding the .inc files for all scenarios.</p>
JRC - Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system - Materials demand database
<p>This dataset contains the results of the materials demand scenarios for wind and solar PV technologies developed in the following report by the European Commission's Joint Research Centre (JRC):</p> <p>Carrara S., Alves Dias P., Plazzotta B. and Pavel C., Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system, EUR 30095 EN, Publication Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-16225-4, doi:10.2760/160859, JRC119941</p> <p>The report can be found at the following link:</p> <p><a href="https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system">https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system</a></p>
Supplementary data: "Early decarbonisation of the European energy system pays off"
<p>This repository includes input data to run the model <a href="https://github.com/martavp/pypsa-eur-sec-30-path">PyPSA-Eur-Sec-30-paths</a> and the results discussed in the paper "<a href="https://arxiv.org/abs/2004.11009">Early decarbonisation of the European energy system pays off</a>"</p> <p>The code to run the model can be found in the Github repository <a href="https://github.com/martavp/pypsa-eur-sec-30-path">PyPSA-Eur-Sec-30-paths</a> and the permanent repository <a href="https://zenodo.org/record/4014807#.X1IKRYtS-Uk">10.5281/zenodo.4014807</a></p> <p>The directory 'data/' includes all the datasets needed to run the model.</p> <p>The directories 'version-*/' include the network objects obtained as an outcome of the optimization in the different scenarios.</p> <p>The directory 'summaries/' includes summaries with the most relevant aggregated results.</p> <p>All input data (in the directory 'data/') and results (in the directories 'version-*/' and 'summaries/') are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p> </p>
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