A qualitative-computational cataloguing of the EU-level public research and innovation portfolio of clean energy technologies (2014–2020)

To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.

The description and details of the computational framework and data analysis together with the data and file usage is described here: Data Analysis Description

Citation

@article{KORETSKY2021100084,
title = {A qualitative-computational cataloguing of the EU-level public research and innovation portfolio of clean energy technologies (2014–2020)},
journal = {Current Research in Environmental Sustainability},
volume = {3},
pages = {100084},
year = {2021},
issn = {2666-0490},
doi = {https://doi.org/10.1016/j.crsust.2021.100084},
url = {https://www.sciencedirect.com/science/article/pii/S2666049021000608},
author = {Zahar Koretsky and Pedro V. {Hernández Serrano} and Seun Adekunle and Michel Dumontier},
keywords = {Innovation policy, Mitigation, Horizon 2020, Clean technology, Sustainability, Text mining}
}

Access Protocols 🔐

All Digital Objects (DOs) contained in this repository (i.e. Software, Datasets) are Open Access, meaning that they are retrievable with a standard HTTP request from any browser such as cloning the repository, curl the resource or sumply pressing the download bottom. 👆🏼

The repository can also be downloaded in Zenodo at zenodo.org/record/4657236 and at Dataverse at dataverse.nl/doi:10.34894/Q80QUE
SPARQL Endpoint
Unfortunately, this data does not have a data service available due to a lack of resources; however, the dataset ../data/joint_results_cleantechtag.csv is semantically annotated following the standard vocabularies following the FAIR principles; therefore, you could easily set up a local SPARQL endpoint.
1. Open your terminal (you need Python installed)
2. Install the following

pip install rdflib-endpoint@git+https://github.com/vemonet/rdflib-endpoint@main

Expose the RDF file to the local service

rdflib-endpoint serve ../data/cordis-cleantechtag.nt

Go to your localhost at http://0.0.0.0:8000

Terms of Use 📃

Research Software LICENSE
The code contained in this Github repository is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

Data LICENSE
The data terms of use are specific for this research project. There can be found in /clean-technologies-nlp/data/LICENSE document.