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A model of the European power system built using Calliope. This repository contains the workflow routines that automatically build the model from source data. Alternatively to building models yourself, you can use pre-built models that run out-of-the-box. See README.md for further information. 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.

ERIGrid JRA2: Test case TC3 mosaik implementation

This script was created to facilitate analysis of data (see data publication) pertaining to the publication: Jongejans, L. L., Strauss, J., Lenz, J., Peterse, F., Mangelsdorf, K., Fuchs, M. and Grosse, G. (2018). Organic matter characteristics in yedoma and thermokarst deposits on Baldwin Peninsula, west Alaska. Biogeosciences 15, 6033-6048. doi:10.5194/bg-15-6033-2018. The input data stem from sediment samples taken from two permafrost exposures in west Alaska. The input parameters are total organic carbon content, bulk density, wedge ice volume, deposit thickness and coverage. The script calculates the organic carbon storage of the permafrost sediments using a bootstrapping approach. The script was written for statistical software R (version 3.6.1). The current code and the required files are available on GitHub.

This map shows all emissions of CO and CO2 in Europe in selected industrial categories that contain processes with Carbon4PUR compatible emissions according to the E-PRTR. The information contained in this document has been prepared solely for the purpose of providing information about the Carbon4PUR consortium and its project. The document reflects only the Carbon4PUR consortium’s view and the European Commission is not responsible for any use that may be made of the information it contains. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768919. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768919 {"references": ["https://carbonnext-eu.github.io/"]}

Hydroelectricity is critical for decarbonizing global energy production, but hydropower plants affect rivers, disrupt their continuity, and threaten migrating fishes. This puts hydroelectricity production in conflict with efforts to protect threatened species and re-connect fragmented ecosystems. Assessing the impact of hydropower on fishes will support informed decision-making during planning, commissioning, and operation of hydropower facilities. Few methods estimate mortalities of single species passing through hydropower turbines, but no commonly agreed tool assesses hazards of hydropower plants for fish populations. The European Fish Hazard Index bridges this gap. This assessment tool for screening ecological risk considers constellation specific effects of plant design and operation, the sensitivity and mortality of fish species and overarching conservation and environmental development targets for the river. Further, it facilitates impact mitigation of new and existing hydropower plants of various types across Europe. The tool does not yet support VBAs. In order to use it and produce reliable results, all input fields have to be reset manually before making a new assessment. The input window contains examplary dummy data.

This MATLAB code is intended to help end-users who wish to work with the sea-level rise projections of Kopp et al. (2014) and successor papers in greater detail than provided by the supplementary tables accompanying that table but without re-running the full global analysis using the supplementary code accompanying the paper. Key functionality these routines provide include: 1. Local sea-level rise projections at decadal time points and arbitrary quantiles 2. Localized Monte Carlo samples, disaggregatable by contributory process 3. Localized variance decomposition plots This code was originally developed to accompany the results of R. E. Kopp, R. M. Horton, C. M. Little, J. X. Mitrovica, M. Oppenheimer, D. J. Rasmussen, B. H. Strauss, and C. Tebaldi (2014). Probabilistic 21st and 22nd century sea-level projections at a global network of tide gauge sites. Earth's Future 2: 287–306, doi:10.1002/2014EF000239. Please cite that paper, along with the paper(s) associated with the specific projections you are using, when using any sea-level rise projections generated with this code.

An Excel-based model to estimate the LCOE of the offshore wind farms. The Discounted Cash Flow (DCF) approach is followed in the model. Modifications in Version 1.1, 1) Dependability between model variables is improved in this version. 2) Detailed earnings estimation is included in the model (e.g., different subsidy periods for the offshore wind farm, varying electricity prices across different years)

Energy system modelling and data processing tool.