Environment and Global Economy

Teacher(s)

Willems Bert;

Language

French

Learning outcomes

At the end of this learning unit, the student is able to :
1

Content

! This course will be taught exceptionally in English in 2025-2026
This course provides students with an introduction in environmental economics. 
The goal is to (1) provide students with a good analytical framework to study environmental problems, and (2) touch upon some specific policy issues. Those topics might change year by year. 
This course will be taught in English (lectures and learning material) with french welcome in class. 
Topics for the course will include among others

• Introduction to environmental economics.
• Externalities, public goods, role of the government, pigouvian taxes, tradeable emission permits
• Optimal level of pollution and the efficient reduction of emissions. 
• Economics of finite resources (Hotelling Rule). 
• Introduction to climate change economics. 
• Introduction to cost-benefit-studies.

Prerequisites: Good understanding of introductory (micro)economics is required for this course. Concept such as marginal cost, consumer surplus, and market clearing are assumed to well-understood. 

Evaluation methods

Grading Structure

• Participation (40 percent)
  The participation grade consists of in-class presentations and a homework assignment.
  
  • Students are expected to attend classes, listen to presentations of other students, and actively provide constructive feedback and comments.
    
  • Attendance is not strictly mandatory, but since participation counts for 40 percent of the grade, structural or repeated absences may lower the participation grade, as students miss opportunities to present, engage, and contribute.
    
  • Students should review their timetable carefully before enrolling to avoid scheduling conflicts with other courses.
    
  • The participation grade is final and cannot be retaken.
    
• Written Exam (60 percent)
  The written exam takes place at the end of the course.
  
  • A passing grade on the written exam is required to pass the course overall.
    
  • In the resit, the final grade will be based entirely (100 percent) on the written exam, as there is no option to retake the participation components.
    
  • If a resit is necessary, the format may be adapted, for example, the resit may be conducted as an oral exam.
    

Use of AI Tools

AI tools may be used for assignments and preparation unless explicitly stated otherwise for a specific task.

• Students must clearly state in their submission which AI tool was used, for what purpose, and briefly describe their own contribution.
  

Responsibilities When Using AI

Students remain fully responsible for their work. They must:

• Understand and verify all results, calculations, and arguments in their submission
  
• Be able to present and explain their work, including any AI-generated parts, during discussions or presentations
  
• Read and understand all references and source materials cited in their work
  
• Check the correctness of all derivations, code, and factual claims
  
• Avoid entering personal or confidential information into AI systems
  

Late Submission Policy

Late submissions of assignments will result in a grade deduction, depending on how late the submission is.

• Assignments submitted several days late may not be accepted, unless prior arrangements have been made with the instructor.
  
• Exceptions are only possible in documented cases of illness or other serious circumstances.
  

Free Riding Policy

All group members are expected to contribute actively and fairly to group assignments.

• Free riding will result in a full grade deduction on the assignment for the student concerned.
  
• Instances of free riding should be reported in Moodle.
  

Other information

Moodle will be used for this course, where the slides and background material will be placed. 

Faculty or entity

> CLSM