Composition Partnership

• Università degli Studi di Napoli Federico II, Italy (Coordinator and Host Institution)

• Athens University of Economics and Business, Greece (Sending Institution)

• Technische Universität Dortmund, Germany (Sending Institution)

• University of Twente, The Netherlands (Sending Institution)

• University College Dublin, Ireland (Sending Institution)

• University of Economics in Bratislava, Slovakia (Sending Institution)

• Babeș-Bolyai University, Cluj-Napoca, Romania (Sending Institution)

Topic of the program

This program provides advanced training in statistical and machine learning techniques for sustainable finance. A key component is the Hybrid Approach for the Analysis of Complex Data Structures, where participants will learn to combine traditional statistical methods with modern computational tools to address sustainability-related financial challenges.

The course focuses on the integration of Environmental, Social, and Governance (ESG) factors into financial decision-making, risk management, and investment strategies. Participants will explore hybrid methods to analyze complex data from various sources, including ESG metrics, financial time series, and corporate reports, using tools like R and Python.

Learning Outcomes

By the end of the program, participants will:

1. Apply hybrid approaches to analyze complex data structures in sustainable finance.

2. Build statistical and machine learning models to assess ESG factors and their financial implications.

3. Understand and integrate sustainability metrics into financial decision-making.

4. Perform advanced data wrangling, cleaning, and analysis for financial datasets.

5. Utilize dashboards and reporting frameworks to present actionable insights.

Schedule description

1. Description of the physical component

Dates: 07.09.2026 to 11.09.2026

Location: Naples, University of Naples Federico II

Structure:

September 7, 2026

      Sustainable Finance: Spatial Statistics and Machine Learning for Climate and ESG Risk [1]

Instructors:

      Adriano Morales, University of Twente, The Netherlands

      Abdulaziz Yusuf Ali, University of Twente, The Netherlands

Short description

This course introduces participants to sustainable finance through the lens of spatial data, spatial statistics, and machine learning. It begins with an overview of climate finance and spatial finance, emphasizing how asset locations, environmental hazards, and geospatial information can be combined to assess physical and transition risks. Participants will then explore key spatial statistical methods, including geostatistics, Bayesian modelling, and spatial point processes, with applications to housing prices and climate risk, deforestation and asset exposure, hazard-related losses, and transport emissions.

Materials > Download    

-----------------------------------------------------------------------------

September 8, 2026

      Sustainable Finance: Spatial Statistics and Machine Learning for Climate and ESG Risk [2]

Instructors:

Adriano Morales, University of Twente, The Netherlands

Abdulaziz Yusuf Ali, University of Twente, The Netherlands

Short description

The second part of the course focuses on ESG and financial materiality, showing how spatial data can support the identification, measurement, and interpretation of financially relevant sustainability risks. Machine learning methods for spatial classification and clustering will also be introduced, with examples related to deforestation and mining mapping, rooftop damage classification, environmental risk mapping, and broader asset-level exposure assessment. The course combines conceptual lectures with hands-on laboratory sessions in which participants apply spatial and machine learning tools to develop risk maps and classification outputs.

Evening session

Laboratory lectures – supervised tutorial, individual and team work.

Materials > Download      

-----------------------------------------------------------------------------

September 9, 2026

      Methods for Dimension Reduction and Clustering for ESG Analysis

Instructor:

Dimitris Karlis, Athens University of Economics and Business, Greece

Short description

This course introduces the basic ideas and principles of dimension reduction. It focuses on Principal Components Analysis, including the underlying methodology, the choice of the number of components, variants of the method, and applications with real data.

Evening session

Laboratory lectures – supervised tutorial, individual and team work.

Materials > Download    

-----------------------------------------------------------------------------

September 10, 2026

Sustainability Analytics and Carbon Footprint Estimation in Sports Events

Instructor:

Professor Ioannis Ntzoufras, Department of Statistics, Athens University of Economics and Business, Greece

Short description

This lecture introduces the fundamental principles and methods for analysing survey data in the context of sports sustainability. Using data collected from spectators attending a match between the Greek National Football Team and the National Team of Ireland, participants will explore how survey analytics can be used to understand mobility patterns, transportation choices, and environmental attitudes among football fans.

The course demonstrates how survey data can provide valuable insights into spectator behaviour and its environmental impact. Particular emphasis will be placed on estimating the carbon footprint associated with match attendance, with a focus on travel-related emissions and the role of sustainable mobility practices.

All analyses will be conducted using R. Starting from descriptive and exploratory analytics, participants will learn how to summarise and visualise fans’ attitudes towards sustainability, identify key mobility trends, and quantify the environmental impact of spectator travel. The course will then progress to more advanced analytical approaches for estimating the overall carbon footprint of the event and assessing factors associated with environmentally responsible behaviours.

By the end of the lecture, participants will have gained practical experience in managing and analysing survey data, interpreting sustainability-related indicators, and producing evidence-based assessments of the environmental impact of major sporting events.

Evening session

Laboratory lectures – supervised tutorial, individual and team work.

Materials > Download      

-----------------------------------------------------------------------------

September 11, 2026

       Machine Learning and AI in Sustainable Finance

Instructor:

Associate Professor Liana Stanca, Babeș-Bolyai University / FinTech LivingLab

Short description

This lecture introduces the fundamental principles and practical methods of applying Machine Learning and Artificial Intelligence in the context of sustainable finance and ESG-driven decision-making. Using real-world financial and sustainability datasets, participants will explore how data-driven approaches can be used to understand ESG performance, climate risk exposure, and sustainability-related financial indicators across firms and markets.

The course demonstrates how machine learning models and visual analytics can support evidence-based sustainability assessment in finance. Particular emphasis is placed on the integration of predictive modelling and interactive data visualization for ESG analysis, including the development of dynamic dashboards for exploring sustainability metrics, carbon exposure, and financial performance relationships.

All analyses will be conducted in R/Shiny or Python, depending on the implementation, combining exploratory data analysis, machine learning techniques, and interactive visualization tools such as Shiny, Plotly, or Dash. Starting from descriptive analytics and data visualization, participants will learn how to summarize and interpret ESG indicators, identify sustainability patterns across firms, and visualize key financial and environmental relationships through interactive dashboards.

The course then progresses toward supervised and unsupervised learning methods for ESG risk classification, sustainability profiling of companies, and clustering of firms based on environmental and governance characteristics. A dedicated component is included on explainable AI techniques to ensure transparency and interpretability of machine learning outputs in financial decision contexts.

By the end of the lecture, participants will have gained hands-on experience in building and interpreting machine learning models for sustainable finance, designing interactive dashboards for ESG data exploration, and producing data-driven insights that support sustainable investment and risk assessment decisions.

Evening session

Laboratory lectures – supervised tutorial, individual and team work.

Materials > Download      

2. Description of the virtual component 

The programme includes online sessions delivered via Microsoft Teams. These virtual meetings will be used for lectures, discussions, supervision, and interaction with participants.

Link to the virtual sessions: Microsoft Teams classroom 

Structure:

August 26, 2026

• 10:00 CEST– Welcome and introductions by the academic partners to greet the students

• 10:20–12:00 CEST– Lecture: Hybrid Data Analysis for Sustainability

This lecture introduces the concept of hybrid data analysis in the context of sustainability-related decision-making. It explores how quantitative and qualitative data, structured and unstructured information, and different analytical approaches can be combined to address complex environmental, social, and economic challenges. Participants will gain insight into key sustainability data sources, common methodological approaches, and the role of expert knowledge in hybrid analytical frameworks.  

Materials > Download       

Marcos R. Machado, University of Twente, The Netherlands

September 2, 2026

• 10:00–12:00 CEST– Lecture: Data Preprocessing Treatment & Applications in Sustainability

This course provides an introduction to the theoretical foundations and practical applications of data preprocessing in data mining and analytics. Participants will examine the main stages of preprocessing, including data cleaning, transformation, reduction, and other preparation techniques that enhance the quality and usability of data for subsequent analysis. The course also explores different categories of preprocessing methods and demonstrates their implementation through Python and Jupyter Notebooks. A sustainability-related application case is included to illustrate how preprocessing techniques can be applied in practice to support meaningful and robust data-driven insights. 

Materials > Download

Marcos R. Machado, University of Twente, The Netherlands

Wouter van Heeswijk, University of Twente, The Netherland

September 25, 2026

• 10:00–12:00 CEST– Lecture: Quarto for Reproducible Documents and Dashboards with R

Materials > Download

Alfonso Iodice D’Enza University of Naples Federico II, Italy

Key Features

• Level: Master’s and PhD students.

• ECTS: 3

• Language: English

• Online Support: Weekly mentorship from sustainability and finance experts.

Hybrid Approach for Complex Data Structures

This course focuses on blending traditional statistical approaches (e.g., regression, dimension reduction) with machine learning and AI methods (e.g., clustering, predictive analytics) to address the multifaceted challenges in sustainable finance. Emphasis is placed on handling large-scale, heterogeneous datasets, developing scalable models, and deriving actionable insights for ESG evaluation and decision-making.

Practical information

- Level of students: Master's and PhD students

- Number of ECTS: 3 

- Main language of instruction/training: English

- Venue of Activities (City, Institution): Naples, Department of Political Sciences, University of Naples Federico II (Statistics Laboratory and G4 room)

In addition, a tutor will be available for each participant during the training period and a dedicated programme manager provided by BIP Faculty will be available virtually. These figures will act as support in the learning and skills development phase.

Program Committee

• Maria Iannario, Università degli Studi di Napoli Federico II, Italy (Coordinator and Host Institution)

• Ioannis Ntzoufras, Athens University of Economics and Business, Greece (Sending Institution)

• Andreas Groll, Technische Universität Dortmund, Germany (Sending Institution)

• Joerg Osterrieder, University of Twente, Netherlands (Sending Institution)

• Nial Friel, University College Dublin, Ireland (Sending Institution)

• Jana Peliová, University of Economics in Bratislava, Slovakia (Sending Institution)

• Codruta Mare, Babes-Bolyai University

Conference Secretariat

MARIA GIOVANNA PORZIO, University of Naples Federico II

Social Event Session: Guided Tour of Hidden Naples – Organized by Insolitaguida

Dates:  Thursday at 16:00 (4:00 PM)

As part of the physical component in Naples, participants will have the opportunity to take part in a guided walking tour organized by Insolitaguida, dedicated to the discovery of some of the most fascinating legends and hidden treasures of the city.

The tour will include a visit to the Fontana di Spinacorona, one of Naples’ most distinctive fountains, and to the Church of San Giovanni Maggiore, which houses the memorial stone associated with the legendary siren Parthenope, the mythical founder of Naples.

The itinerary will conclude at the Church of Santa Maria la Nova, where, according to a fascinating local tradition, the tomb of Dracula, Vlad III of Wallachia, may be located.

The cost of the guided tour is €20 per person, including entrance fees and audio headsets.

To participate, students should email info@insolitaguida.it with the subject line: ASMSF2026 – Guided Tour

For additional information about the association organizing the event, please visit the following link: Social Event