B5181 - MODELLI E ANALISI PER LA GESTIONE AMBIENTALE

Learning outcomes

At the end of the course, the student will be able to represent a system and have a basic knowledge of how to model the dynamics of the relationships between the elements of a system. Students will be familiar with the basics of environmental economics and the environmental policy categories of environmental management.

Course contents

The course introduces models and analytical methods for the representation, interpretation and management of environmental systems, with particular attention to the interactions among biophysical, socio-economic and institutional components.

The first part addresses the epistemological and ontological foundations of the concepts of system and model. Topics include the relationship between reality and representation, the definition of system boundaries and scales, causal relationships, emergent properties, linear and non-linear behaviour, and the interpretative and predictive limits of models. These concepts will be applied to different types of environmental systems, including examples from physics, chemistry, biology and ecology.

A central part of the course focuses on the dynamic modelling of environmental systems. The main tools of system dynamics will be introduced, including causal loop diagrams, stocks and flows, feedbacks and delays, together with the elements of ordinary differential equations required to describe and interpret the temporal evolution of systems. Activities will include exercises and applications using software tools for dynamic modelling and simulation.

The course then extends the concept of modelling to socio-economic and institutional systems relevant to environmental management. Topics will include, at different levels of detail, supply-and-demand models and the formation of value and price, externalities, the economic valuation of non-market resources and services, public goods and common goods, property rights and their management. Different modelling paradigms for socio-economic systems, including agent-based models, will also be introduced, with emphasis on their purposes, assumptions and differences from aggregate modelling approaches.

The final part addresses a range of environmental policy instruments and systems, focusing on market-based instruments as well as emissions trading systems and certification and qualification instruments related to carbon markets.

Generative artificial intelligence will be used throughout the course both as an object of critical reflection and as a tool supporting learning, modelling and project activities, with particular attention to information verification, transparency of use and students’ critical assessment capabilities.

The level of detail devoted to individual topics and the selection of case studies will also depend on the exercises and laboratory activities developed during the course.

Readings/Bibliography

The materials required for exam preparation, including lecture notes, presentations, exercises and additional teaching resources, will be made available through the Virtuale platform. For each part of the course, the materials required for exam preparation will be clearly identified.

During the course, textbooks, scientific articles and other references useful for further study of the topics covered will also be indicated.

Any additional readings and materials intended for further study but not required for the examination will be identified separately.

Teaching methods

Teaching activities include lectures, practical exercises and applied activities. Different learning approaches will be used, including case-study analysis, guided discussions, and individual and group activities.

Lectures will introduce the conceptual and methodological foundations of the course and support the development of connections among its different topics. Practical exercises and applied activities will consolidate the concepts introduced during the course and apply them to the representation, analysis and interpretation of environmental systems.

For dynamic systems modelling, exercises using software tools for modelling and simulation will be carried out in class, also using students’ own devices.

Generative artificial intelligence may be used in selected teaching activities as a tool supporting learning, analysis and modelling. Particular attention will be devoted to verifying generated results, ensuring transparency in the use of these tools, and developing students’ ability to critically assess their limitations and reliability.

Attendance, particularly during practical exercises and applied activities, is recommended in order to achieve a full understanding and application of the methods covered in the course.

Assessment methods

Assessment is based on an oral examination covering the entire course programme.

During the first examination session, students will also be offered two opportunities to take an optional written test on dynamic systems modelling. The purpose of the written test is to distribute the overall study workload and it is not a prerequisite for access to the examination sessions.

The written test assesses the student’s ability to represent a system, interpret its dynamic relationships, and apply the fundamental tools of system dynamics together with the mathematical elements required for their formalisation.

Students who successfully complete the written test will not be required to undergo a further analytical assessment of the same contents during the oral examination. In this case, the written test accounts for 40% and the oral examination for 60% of the final grade. Dynamic modelling skills may nevertheless be used during the oral examination to establish connections with other parts of the course.

Students who do not take or do not pass the written test will instead take an oral examination covering the entire course programme; in this case, the final grade will be entirely determined by the oral examination.

The oral examination assesses knowledge of the course topics and, in particular, the ability to interpret and connect models, environmental, socio-economic and institutional systems, and environmental policy instruments. Assessment takes into account the correctness of the analysis, appropriate use of models and concepts, ability to establish connections among different topics, critical discussion of model assumptions and limitations, and command of technical terminology.

The final grade will be assigned according to the following criteria:

18–19: limited preparation; application and analytical skills requiring guidance; generally appropriate language.

20–24: adequate but incomplete preparation; ability to independently apply methods and concepts to relatively simple problems; appropriate language.

25–29: broad preparation; ability to independently select and use appropriate models and interpretative tools; good critical analysis skills and command of technical terminology.

30–30 with honours: substantially comprehensive preparation; ability to independently integrate and compare different representations and models and discuss their assumptions, limitations and implications; full command of technical terminology and strong critical argumentation skills.

As regards assessment, the use of generative AI is prohibited. Any use constitutes a breach of academic integrity.

Students with specific learning disabilities or temporary or permanent disabilities are advised to contact the relevant University office in good time. The office will propose any appropriate adjustments, which must in any case be submitted to the instructor for approval at least 15 days in advance and will be assessed in relation to the learning outcomes of the course.

Teaching tools

Teaching materials used during the course, including presentations, lecture notes, exercises, applied examples and additional resources, will be made available through the Virtuale platform.

Software tools for the representation, modelling and analysis of dynamic systems will be used for modelling and simulation activities. Operational information concerning the software adopted and access procedures will be provided during the course and made available on Virtuale.

Generative artificial intelligence may be used as a tool supporting individual study, further exploration, synthesis, self-assessment, and selected analysis and modelling activities. Its use will be accompanied by methodological guidance aimed at verifying information, ensuring transparency in the use of the tools, and critically assessing the results produced.

Office hours

See the website of Diego Marazza