Advanced life cycle assessment methods

• Relate the environmental impacts of engineering systems by using advanced life cycle assessment methods.
• Use a commercial LCA simulator (SimaPro) in combination with advanced life cycle assessment methods to reduce the environmental footprint of products and services.
• Describe how life cycle inventory data can be transformed to represent future changes in the technosphere.
• Extract information from a prospective database.
• Analyze environmental progress in background systems factoring shared socioeconomic pathways and representative concentration pathways.
• Calculate environmental learning rates and project environmental progress in foreground systems.
• Observe and critically evaluate the impacts of environmental learning effects on future sustainability potentials.
• Organize economic and environmental data to form the basis for an environmentally extended input-output model.
• Estimate the environmental impacts of large-scale systems using environmentally extended input-output analysis.
• Develop sustainable-by-design strategies for reaching absolute sustainability in the framework of prospective life cycle assessment.

Introduction to prospective life cycle assessment (pLCA). Principles of environmentally extended input-output modeling. Presentation of environmental learning theory and quantification of environmental learning rates. Introduction to background system modelling, shared socioeconomic pathways (SSPs), representative concentration pathways (RCPs), and integrated assessment models (IAMs). Exploring the sustainable-by-design concept. Linking prospective environmental impacts to absolute sustainability. Practical problem-solving using Python and software tools like SimaPro and MATLAB. Students will learn to critically reflect on the limitations of advanced assessment results for decision support, considering the application context and uncertainties. Participants will discuss and interpret results from a chosen application, formulate technical summaries, provide peer feedback, and present simulation results.

• 6 – 20 (Tues 8-12)

12772/02002/02003, Or other equivalent LCA course and other equivalent course that provides Python competences and/or experience working with Python and rudimentary knowledge of MATLAB.

Lectures and exercises