Course language: English
This course is a specialisation module within the MSc Industrial Ecology (joint degree Leiden University and TU Delft).
Expected prior knowledge:
To take part in the course Environmental Input-Output Analysis students must be familiar with matrix calculations and the use of computer software. A bachelor-level background in linear algebra, programming, and/or micro/macroeconomics is helpful but not mandatory.
In this course you will learn Environmental Input-Output Analysis (EIOA), a standard methodology which models environmental interventions associated with production and consumption of products, across multiple
spatial and sectoral scales. The course is divided in two parts. In the first part you will learn basic concepts, such as the System of National Accounts and computational aspects. In the second part you will explore
various applications such as the calculation of footprints, decomposition analysis, hybrid and multi-regional models. The course follows a hands-on approach, with every lecture accompanied by a tutorial in which you will develop your programming skills, using the python open-source language
After completing this course, you will be able to:
1. use national input-output tables or multi-regional input-output tables and use these datasets to model and analyze the impact of consumer behavior and environmental policy across supply chains;
2. use the vocabulary of the System of National Accounts and know where
and how find relevant statistical data;
3. implement matrix algebra and data transformation operations in python;
4. write and present a quantitative study, identifying its main results and potential limitations.
Teaching methods/mode of instruction
In each week there is a single lecture with four 45 minutes blocks. In the first block we will first revise the homework from the previous week and introduce the new week’s material. In the second block a problem is
presented which illustrates the week’s material and the students and its theoretical implications explored. In the third block the problem is addressed from a computational point of view, and concludes with the handing of the week’s homework (which can be reading literature or solving a problem). The final block is a Q&A session, in which the theoretical and computational problems are addressed. There will be one guest lecture. The course has 6 ECTS = 168 total study hours.
Type of assessment
50% of the final grade will result from the exam. The other 50% will result from a final assignment to be completed at the end of the course. The final assignment consists of a quantitative study in which a
specific topic learned during the course can be applied in depth. The specific format of the assignment is clarified during the course, as it depends on the number of students enrolled.
Course materials/reading list
There is no mandatory reading list, as handouts will be provided. The reading material will consist of lecture slides, research papers and book chapters, besides datasets and code.
Because this course is part of a joint degree between Leiden University and TU Delft, students (also guest and exchange) have to be enrolled to both universities.
All students have to enroll for the course via Brightspace (before the start of the course) and for the exam via uSis, Leiden University. (see: https://www.student.universiteitleiden.nl/en/administrative-matters/registration--de-registration/course-and-exam-enrolment/course-and-exam-enrolment/science/industrial-ecology-msc?cf=science&cd=industrial-ecology-msc#tab-2).
Students who are not enrolled for the MSc Industrial Ecology, have to ask permission from the study advisor of Industrial Ecology (firstname.lastname@example.org) to join this course, at least one month before start of the course.
Exchange students can only enroll for this course if their home university has an Exchange agreement with both Leiden University and TU Delft. Exchange students have to ask permission from the studyadvisor of
Industrial Ecology (email@example.com) as soon as possible, preferably six months before the start of the course.
You should bring your own laptop to the classes with Python installed
including the Pandas and Numpy libraries (e.g., the Anaconda suite).
More information and the description of the course is published in the e-studyguide of TU Delft.