Green Logistics (5 cr)

Evaluation scale

H-5

Content scheduling

1st lecture on Thu 5th September, 11:15 to 14:00 at LEM_B173.

The lecture times and locations are presented at Lukkari Timetable System.

If you cannot participate the first lecture, please contact the teacher before that. In the case the maximum number of course attendants is exceeded, the students who have been absent the first lectures without any pre-notification and/or not enrolled the course before the first lecture will be discharged

Objective

Green logistics is the process of minimizing damage to the environment due to the logistic operations of an organization. As concern for the environment rises, companies must take more account of the external costs of logistics associated mainly with climate change, air pollution, noise etc.
The student is able to
- take into account environmental issues in logistics
- calculate the emissions of transports

Content

• environmental effects of logistics
• eco balance of logistics
• green logistics as part of the supply chain management
• responsible logistics
• recycling and waste disposal logistics
• the emissions and energy consumption of transportation
• City logistics

Materials

Material presented and shared in Itslearning

Teaching methods

Lectures, Exercises

Exam schedules

No exam :)

Pedagogic approaches and sustainable development

The course learning objectives are directly related to sustainability! No reason to more explain!

Student workload

Lectures 42 h
Exercises 40 h
Independent studies 51 h

Total 133 h (equal to 5 cr.)

Evaluation methods and criteria

Evaluation 0 to 5.

The course evaluation is only based on exercises. One exercise has 10 to 35 % factor of final grade. To pass the course with certain grade, the following percentage of total points of exercises must be achieved:

Grade 1 = at least 40 %
Grade 3 = at least 60 %
Grade 5 = at least 85 %

The tasks are designed so that artificial intelligence is not used to complete them. The teacher does not have a system to identify the fraudulent use of artificial intelligence or the skills. If a student collects his credits with the help of artificial intelligence, but finds himself in trouble in his later studies or in working life because he has not really learned the subject, the responsibility for this lies with the student - not the teacher or the educational institution.

Failed (0)

The student has achieved less than 40 % of the course maximum points. Exercises are failed due to plagiarism, other cheating or missing notations to references.

Assessment criteria, satisfactory (1-2)

The student has achieved 40 to 59 % of the course maximum points. Exercises are done without any cheating, and there are notations to references in the exercises.

Assessment criteria, good (3-4)

The student has achieved 60 to 84 % of the course maximum points. Exercises are done without any cheating, and there are proper notations to the references in the exercises.

Assessment criteria, excellent (5)

The student has gained at least 85 % of the course maximum points. All the exercises are submitted with very good or excellent grade. The exercises indicate excellent information combination and analysis skills.

Further information

ItsLearning