UC4

Low carbon energy and energy efficiency policies

General description of use case

The motivation for this use case is the significant role of policies, laws, and regulations in catalyzing the transformation of the energy system towards greenhouse gas and energy efficiency. They provide the ground for accelerating research and innovation, incentivizing behavioral changes, and safeguarding the necessary transformation processes. Through their texts and specifications, policies, laws, and regulations are not only defining the space for the application of energy technologies and low carbon energy materials, but they also provide the framework for market activities in general. An example is the enabling of new actors to appear on the energy market (e.g. prosumers, community energy, and energy cooperatives). Currently, a new role for data on low carbon energy and energy efficiency policies is emerging. The need to provide documentation on compliance with regulations is starkly increasing in complexity as integrated monitoring and assessment across fields and economic sectors are increasing. This not only refers to international compliance requirements (e.g. National Greenhouse Gas Inventories, Intended Nationally Determined Contributions, Progress Reports for Sustainable Development Goals), but also to the level of companies whose produces have to adhere to given standards and documentation has to be provided for the entire supply chain (c.f. Ly et al. 2015 on a review of business process compliance approaches). Machine-actionability of relevant policy databases from local to global scale is therefore key for automated tracking, compilation, and inter-comparison with peers. Here, new business opportunities are arising. Overall, the state about the application of FAIR principles is still in its infancy. Openness is by nature less of an issue for this use case, but a specific challenge originates from mixed data formats, which include texts as well as number formats. Examples of databases from which EERAdata starts the use case activities include the IEA policy & measures databases, SETIS research, and innovation data, ResLegal, JRC in-house methodology for Monitoring R&I in Low-Carbon Energy Technologies, InnovationDB or activities, and tools developed in H2020 project ODYSSEE-MURE

List of selected databases

During the first workshop (see notes from Day 2), the following databases were selected to analyze and improve their compliance with FAIR and Open data principles:

Name of database	Short description	Reasoning of choice	Current state of FAIR/O principles	Target of FAIR/O to achieve within EERAdata
Database 1	Write a short description, e.g., "Database 1 is about XXX, containing XXX data, covering the period xxx."	Summarize shortly the main reasons, why this DB was chosen. Link to the discussion page of WS1UC4.	What is the current FAIR/O state for this database. Summarize here. In case more space is needed, link to a section of the discussion page of WS1UC4.	What FAIR/O target was decided?
OECD database makes available data related to agriculture, development, economy, education, energy, environment, finance, government, health, innovation and technology, jobs and society. In the area of energy, data can be assigned thematically to: primary energy supply, crude oil production, electricity generation, renewable energy, nuclear power plants, crude oil import prices. The data comes from over 130 countries around the world. The database allows sharing data in the form of indicators, maps and tables. The data is collected and made available since 1960. || Example || Example || Example || Example
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Metadata assessments

Databases above were assessed with respect to their current meta practices. The table below summarizes the current state and issues identified during WS 1:

Name of database	Type of metadata provided	Extend of metadata provided	Level of implementation of FAIR/O principles	Frameworks for metadata used	Technical implementation of metadata
Database 1	Which types of metadata are covered? Administrative, descriptive, structural, provenance of data, etc.?	Summarize: Is it rich or basic metadata provided for each of the types?	Check the Wilkinson criteria for metadata and summarize here. In case more space is needed, link to a section of WS1UC4.	What framework is used, e.g., controlled vocabulary, taxonomy, thesaurus, ontology?	How are metadata implemented? As xml, plain text, RDF, etc.
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