Course content

Built environment stocks - such as buildings, infrastructures, or vehicles - satisfy human needs and wants. At the same time, their build-up, use, and end-of-life management shape the main anthropogenic material and energy flows. Transforming the built environment stocks is therefore key for balancing human needs with environmental protection. The consequences of such transformations are often delayed due to the time it takes until new technologies have penetrated use (e.g., for reducing emissions) or reach end-of-life (e.g., for becoming available for recycling). To capture these delay effects for resource use and emissions, it is critical to analyze the dynamics of built environment stocks. This course introduces dynamic Material Flow Analysis (dMFA), a tool used to describe changing systems of resource use and emissions using mass and energy balance principles. The students learn how to develop and use dMFA models to generate forecasting and backcasting scenarios to determine how different emission targets can be reached under different conditions such as population growth, existing stock, climate, novel technologies, user behavior, regulations.

Lectures on the principles of dMFA and stock dynamics are complemented with introductory information on the use of Python and Jupyter Notebooks and practical exercises. In the exercises, the students work in groups of two to develop their own models and scenarios for vehicle and building stocks and interpret the results from a climate policy perspective. The students will present their intermediate and final work in a seminar. The course involves guest lectures from industry and government and an excursion.

Learning outcome

Knowledge: The students will be able to formulate the key problems related to selected built environment systems, to describe the state-of-the-art tools used to analyze these problems (including their strengths and limitations), and to describe the legal and socio-economic context.

Skills: The students will acquire the knowledge and skills necessary to select appropriate system boundaries to answer specific questions related to a problem, to develop models suitable to identify alternative transformation pathways, to use Python for programming dMFAs, and to discuss different interventions in terms of their effectiveness and potential co-benefits, trade-offs, risks, and barriers.

General competence: The students will be able to conduct literature research using online databases and library systems, to work effectively in a team, to organize a code base for scientific research, and to communicate their findings in an effective and transparent way in oral (presentation) and written form (scientific report).

Learning methods and activities

Lectures by teachers and external experts, guided self-study for selected case studies (individual and in groups). The students work on their own projects with guidance in exercise sessions and seminars where they present their work. Course language is English.

Recommended previous knowledge

TEP4285 Material Flow Analysis or equivalent competence. It is recommended that students have taken some courses in Industrial Ecology.

Some prior exposure to python and Jupyter Notebooks is of advantage, but we provide warm-ups that are tailored to this course.

Required previous knowledge

None.

Course materials

Various course materials will be used and distributed electronically on Blackboard during the semester.

Credit reductions