Life cycle data and data quality management
This course looks at effective and efficient ways to collect, store, retrieve and edit data for Life Cycle Inventory (LCI) Analysis. Benefits and limitations (e.g. uncertainty) of the different types of data are examined.
- Types of data used in life cycle assessment: market data, production functions and environmental data.
- National statistics as a data source.
- Industrial data, waste management and recycling data, transport data, infrastructure data, household data.
- Production, supply and consumption mixes. Production volumes.
- Price data, trade margins, product taxes and subsidies.
- Data quality assessment and documenting data uncertainty.
- Data availability and data mining techniques. Handling confidentiality. Identifying and avoiding data gaps. Estimation techniques. Choice of correct data in different situations. Manipulating data to suit specific contexts. Nowcasting and forecasting.
- Structuring and prioritising data collection.
- LCA data formats and documentation. Naming conventions. Classification systems.
- Combining data from different sources. Documenting and justifying data treatment.
- Parameterisation of data. Sub-dividing activities with combined production.
- Techniques to identify and prevent errors in data and data manipulation. Data review.
- Database building and management, now and in the future. The UNEP/SETAC guideline.
Form and academic recognition:
Form: 10 hours lectures. 10 hours workshops/exercises. Additionally 5 hours follow-up course work if ECTS-points are requested.
Academic recognition: 1 ECTS-point
Knowing the types, sources and relevance of data used in LCA. To be able to choose the right data sources for a specific purpose. To be able to identify, document and manage information on data quality and uncertainty. Knowing how to identify the sufficiency and appropriateness of the available data and to manage situations where the available data are insufficient. To be able to structure and prioritise data collection for a specific LCA. Knowing how data from different sources can be combined, the potential errors in this, and how to document data and data manipulation in a standardised format. Be able to identify errors in data and avoid mistakes in data manipulation. Knowing the procedural aspects of database building and management.
Masters degree or equivalent. Must bring own laptop computer. A good understanding and/or experience with life cycle assessment is an asset.