EP8108 - Life Cycle Assessment and Environmental Systems Analysis


Lessons are not given in the academic year 2017/2018

Course content

This is an advanced course addressing life-cycle assessment, input-output analysis and the assessment of environmental consequences, with a focus on the former. The course introduces the state-of-the-art and scientific debates connected to both inventory modeling and impact assessment. The core of the course addresses the modeling of product systems and larger production-consumption networks. The course addresses recent advances related to the adaptation of input-output techniques and data for LCA. Allocation issues, attributional and consequential LCA, and the treatment of recycling will be discussed. The impact assessment with impact-category and damage-oriented approaches are reviewed using the new LCimpact methods. The assessment of climate forcing from bioenergy systems is also addressed. In addition to the presentation of an up-to-date overview of the LCA methodology, it will be discussed how to use LCA to illuminate critical issues relevant for environmental and technology policy. The planning and interpretation of LCA results will be highlighted using real-world case studies from industry and public policy.

Learning outcome

After having the completed course, the students should
1. have an overview of methods in environmental systems analysis and understand the aims and application of these methods in relationship to LCA
2. understand the structure and procedure of LCA, including methods for inventory collection and modeling and for impact assessment
3. have an overview over research questions which are commonly addressed with LCA and research related to LCA methods
4. be able to evaluate LCA studies and their contribution to the knowledge front
5. be able to apply matrix calculation methods in inventory modeling, including those in process analysis, input-output based LCA, and hybrid LCA
6. be able to apply methods for impact assessment and interpret results
7. be able to model and judge uncertainty in LCA

Learning methods and activities

The course will be taught as an intensive 2-week session in May/June, involving both lectures and exercises. The exercises are mostly in the computer lab and utilize the MatLab programming environment. There is an extensive reading list, and students are required to complete the reading before the session. This course is produced in collaboration with other universities in Europe and allows for networking among PhD students. To pass the course a score of at least 70 percent is required.

Compulsory assignments

  • Exercises

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Required previous knowledge

Admission to the PhD Program

Course materials

Collection of articles



  • * The location (room) for a written examination is published 3 days before examination date.
If more than one room is listed, you will find your room at Studentweb.