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ireland-carbon-budget-scenarios-2021

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Carbon Budget Scenarios for Ireland's Energy System, 2021-50

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<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.

# Carbon Budget Scenarios for Ireland's Energy System, 2021-50

[![Frictionless](https://github.com/MaREI-EPMG/ireland-carbon-budget-scenarios-2021/actions/workflows/frictionless.yaml/badge.svg)](https://repository.frictionlessdata.io/report?user=MaREI-EPMG&repo=ireland-carbon-budget-scenarios-2021&flow=frictionless)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5517363.svg)](https://doi.org/10.5281/zenodo.5517363)

This repository constains energy system scenarios for Ireland meeting decarbonisation targets for 2030 and 2050 using the TIMES-Ireland Model (TIM).

## Exploring the Scenarios

Various tools can be used to explore the scenarios in this data package. These include a dedicated [web app][TIM Carbon Budgets 2021 SPA], [pyam][pyam github repository] package, [Friendly data][friendly_data github repository] and spreadsheet software (e.g. MS Excel).

### TIM Carbon Budgets 2021 Web App

The [TIM Carbon Budgets 2021 Web App][TIM Carbon Budgets 2021 SPA] visualises most of the scenarios included in this data package. Up to two scenarios can be selected for comparisson and the difference can be computed. In addition to the scenario results visualisation, **TIM documentation** and **scenario description** can be found there.

### pyam

[pyam][pyam github repository] provides a suite of tools and functions that can be used to analyse and visualise scenario data included in this data package. A brief pyam-tutorial is included in this repository (many thanks to [@danielhuppmann](https://github.com/danielhuppmann/) for contributing) and the rendered version can be accessed [here][rendered pyam-tutorial].

### Friendly data

[Friendly data][friendly_data github repository] allows easy conversion of a frictionless data package to standard Python data structures e.g., pandas DataFrame. For instance the following code snippet can be used to convert the data package to a list of pandas DataFrame (many thanks to [@suvayu](https://github.com/suvayu/) for providing an example):
```
from friendly_data.dpkg import read_pkg
from friendly_data.converters import to_df

pkg = read_pkg("datapackage.json")

dfs = [to_df(r) for r in pkg.resources]   # data package as a list of pandas.DataFrame
```

[TIM Carbon Budgets 2021 SPA]: https://tim-carbon-budgets-2021.netlify.app/
[pyam github repository]: https://github.com/IAMconsortium/pyam
[friendly_data github repository]: https://github.com/sentinel-energy/friendly_data
[rendered pyam-tutorial]: https://github.com/MaREI-EPMG/ireland-carbon-budget-scenarios-2021/blob/main/pyam-tutorial.ipynb

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