Economics Of Innovation For Sustainable Development

The course aims to provide students with a comprehensive understanding of the main supervised and unsupervised learning methods. It covers key techniques such as linear and multiple regression, classification and discrimination methods, resampling techniques, variable selection, splines and generalized additive models, decision trees, ensemble methods, clustering, principal component analysis, and network analysis. Throughout the course, these methods are illustrated through practical examples and applications in the business sector.
In line with the stated learning objectives, at the end of the course the student will be able to:

• understand the theoretical foundations of supervised and unsupervised learning methods and the main statistical and machine learning models;
• apply linear regression and multiple regression for analysing the effect of multiple predictors;
• use classification methods to assign observations to predefined categories and discrimination methods to distinguish between different groups in data;
• apply resampling techniques for model validation and performance assessment;
• perform variable selection to identify the most relevant predictors;
• use splines and generalised additive models to model nonlinear relationships;
• build decision trees and ensemble methods;
• perform clustering for unsupervised grouping of data and apply principal component analysis for dimensionality reduction and feature extraction;
• use network analysis to study relationships and structures in complex data

During the course, students will engage in a combination of theoretical and practical activities designed to support the achievement of the stated learning objectives. The activities include:

• lectures introducing the theoretical foundations of supervised and unsupervised learning methods;
• lectures on linear and multiple regression, classification and discrimination methods;
• activities dedicated to resampling techniques and variable selection methods aimed at identifying the most relevant predictors;
• sessions on generalised additive models for modelling nonlinear relationships;
  implementation of decision trees and ensemble methods;
• activities on clustering techniques and principal component analysis for unsupervised learning, dimensionality reduction, and feature extraction;
• network analysis to explore relationships and structures in complex datasets.

It will be assessed based on the activities carried out in class.

Barabasi, AL., Pósfai, M. (2016). Network Science. Cambridge University Press.

James, Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor (2022) An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics. Cham: Springer.

This course aims to provide students with a comprehensive understanding of the economics of innovation within the context of the energy sector. Through a combination of lectures, research projects, and written assignments, students will examine the structural organization of energy markets and assess the transformative role of innovation. Particular emphasis will be placed on the theoretical foundations of innovation in modern growth theory and its implications for the development and expansion of renewable energy markets.
Students will acquire the ability to critically read and interpret scholarly articles on innovation and the energy sector. They will also develop the skills necessary to present a focused research project on an assigned topic.

The theoretical foundations provided by the course—particularly regarding models of innovation and the energy sector—together with the practical competencies gained through assignments, will equip students to apply theoretical insights to real-world examples and to evaluate how policy recommendations should be formulated and communicated. Furthermore, the inclusion of case studies and applied work will offer valuable preparation for those seeking to pursue careers within the energy industry.

WEEK1

CLASS 1: Introduction

IEA: WEO, 2023, 2024

Textbook FS: chapter1

CLASS 2: Economic analysis of energy investments

Textbook FS: chapter 4

CLASS 3: Innovation in energy systems and the role of finance + Debate on “finance and innovation”

IEA: The state of energy innovation, 2026

WEEK 2

CLASS 4: Innovation in practice: Introduction to energy system optimization models and TEMOA

CLASS 5: Net 0 economies and simulation models

• On the History and Future of 100% Renewable Energy Systems Research, Christian Breyer et.al., IEEA Access.
• Energy systems in scenarios at net-zero CO2 emissions, Julianne DeAngelo , Inês Azevedo2, John Bistline3, Leon Clarke4, Gunnar Luderer 5, Edward Byers & Steven J. Davis, Nature Communications, 2021
• The meaning of net zero and how to get it right, Frankhauser et.al., Nature Climate Change, 2022

CLASS 6: Electricity markets and renewable energy

Textbook CF chapter 10

WEEK 3

CLASS 7: Innovation in practice : Representation of energy system and the TEMOA-Italy example

CLASS 8: Support policies for renewables and their implications: market and non market instruments

Textbook FS: chapter 7+8

Richard Schmalensee ,Evaluating Policies to Increase the Generation of Electricity from Renewable Energy, MIT-CEPR working papers, 2011

Boringher, Alexander Cuntz, Dietmar Harhoff, Emmanuel Asane-Otoo: The impact of German Feed-in-Tariff scheme on innovation, Energy Economics, 2017

CLASS 9: Innovation and the energy sector : an introduction

David Popp, Environmental Policy and Innovation: A Decade of Research, NBER Working Paper, 2019

David Popp, Jacquelyn Pless, Ivan Haščič, Nick Johnstone Innovation and Entrepreneurship in the Energy Sector, chapter of the volume The Role of Innovation and Entrepreneurship in Economic Growth, NBER, 2020

Optional:

Anna Bergek , Christian Berggren, KITE Research Group, The impact of environmental policy instruments on innovation: A review of energy and automotive industry studies, Ecological Economics, 2014

Richard Newell, The Energy Innovation System: A Historical Perspective

WEEK 4

CLASS 10: Innovation in practice: Data preprocessing in TEMOA + Hands-on exercises

CLASS 11-12: Endogenous growth models: clean vs dirt innovation & debate

Barro and Sala-I-Martin: Economic Growth

Chapter 4 (parts) & Chapter 6

Daron Acemoglu, Philippe Aghion, Leonardo Bursztyn, and David Hemous: The Environment and Directed Technical Change, The American Economic Review, 2012

Philippe Aghion, Cameron Hepburn, Alexander Teytelboym, Dimitri Zanghelis: Path dependence, innovation and the economics of climate change, working paper, 2014

WEEK 5

CLASS13: Innovation in practice: Data postprocessing in TEMOA + Hands-on exercises

CLASS 14-CLASS 15: Empirics : Energy prices, directed technological change and clean innovation

David Popp, Induced innovation and energy prices, The American Economic Review, 2002

Nick Johnstone , Ivan Hašcic, David Popp : Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts, Environmental and Resource Economics, 2010

Carolyn Fisher, Richard Newell: Environmental and technology policies for climate mitigation, Journal of Environmental Economics and Management, 2008

Carolyn Fischer, Louis Preonas, Richard G. Newell , Environmental and Technology Policy Options in the Electricity Sector: Are We Deploying Too Many? Journal of the Association of Environmental and Resources Economics, 2017

WEEK 6

CLASS 16: Innovation in practice: Simple scenario analysis in TEMOA + Hands-on exercises

CLASS 17-19 : Case studies (innovation, gas and solar power, automation)

Gregory F. Nemet, How solar Energy Became Cheap, A Model for Low Carbon Innovation, Routledge, 2009

Daron Acemoglu, Philippe Aghion, Lint Barrage, David Hémous: Climate Change, Directed Innovation, and Energy Transition: The Long-run Consequences of the Shale Gas Revolution, MIT Working Paper, 2024

WEEK 7

CLASS 20: Recap and questions

References for TEMOA:

Readings for the 5 classes on hands-on application of innovation to energy systems:

Hunter, Kevin, Sreepathi, Sarat, and DeCarolis, Joseph F.. (2013) "Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)." Energy Economics

DeCarolis, Joseph F. et al. (2017) “Formalizing best practice for energy system optimization modelling.” Applied Energy

Colucci, Gianvito, Lerede, Daniele, Nicoli, Matteo, and Savoldi, Laura. (2023) "A dynamic accounting method for CO2 emissions to assess the penetration of low-carbon fuels: application to the TEMOA-Italy energy system optimization model." Applied Energy

Nicoli, Matteo, Colucci, Gianvito, Di Cosmo, Valeria, Lerede, Daniele, and Savoldi, Laura. (2025) "Evaluating the impact of hurdle rates on the Italian energy transition through TEMOA." Applied Energy

Vai, Alessio, Colucci, Gianvito, Nicoli, Matteo, and Savoldi, Laura. (2025) "May the availability of critical raw materials affect the security of energy systems? An analysis for risk-aware energy planning with TEMOA-Italy." Materials Today Energy

Student assignments and presentations will account for 30% of the final grade. 10% of the final grade will be attributed based on participation / interaction during classes. The remaining 60% will be based on a written examination administered at the conclusion of the course

1.understanding of how a company works
2.understanding of company strategies
3.understanding AI and business and industry dynamics
4.understanding green transition business and industry dynamics
After the course, the learner will be able to

1. understand the basic mechanisms of companies

2. analyse strategies and business models

3. identify any strategic actions that drive potential change

4. analyse the business models of both the incumbent and start-up ai sector

5. present the concepts learnt.

module 1: PRELIMINARY CONCEPTS

how a company works
historical perspective on competitive strategies
industry analysis
target market
Business model

module 2: EMERGING STRATEGIES

Blue ocean strategy
Resource-based View
Emergent Strategy

Module 3: BUSINESS MODELS

New business models
Trends
Practical cases

module 3: ARTIFICIAL INTELLIGENCE

General Concept

sector analysis

business model analysis

The exam will be a written test consisting of a series of cross-checked questions in test mode and a series of open questions

Course Learning Objectives

The course aims to provide students with the fundamental principles of computer programming, with particular emphasis on the use of the Python programming language to effectively address and solve simple mathematical and interdisciplinary problems.

These problems are considered both at a theoretical level and at a practical level, in order to encourage students to use Python not only as a programming language but also as a supportive tool for studying other courses within the Economics of Innovation for Sustainable Development degree programme.
Learning Outcomes

By the end of the course, students will be able to:

• Understand the foundational logic of programming for data analysis;
• Retreive, organize, analyze and visualize traditional tabular data appropriately;
• Collect, preprocess and analyze unstructured textual data with a range of techniques;
• Read, understand and critically appraise python code written by others.

The course will be organized in two main parts:

1. Foundations of Coding for Data Analysis

The aim of this first part will be to familiarize with the logic of computational thinking and to learn the basic details of implementing a pipeline of data analysis in practice. We eill deal with:

• Foundational data structures
• Control flow (conditionals, loops, etc)
• Functions and objects
• I/O operations
• Data wrangling and visualization libraries (e.g. NumPy, Pandas, Matplotlib, etc)

2. Case studies

Each session of this second part will deal with a practical case study which will be analysed and coded from scratch during the class, with examples ranging from simple descriptive analysis of tabular data to classification models and Natural Language Processing.

Examinations are held in person and consist of an oral examination during which students will be exposed to exercises like:

• Writing/completing a piece of code in real time to solve a simple task;
• Reading and explaining a simple piece of code step by step, detailing the output;
• Assessing a piece of code and identifying edge cases and/or bugs.

Learning Materials and References

Course Materials

Lecture notes and exercise sheets prepared by the instructor will be available on the course Moodle page.

Students are expected to consult the Moodle platform regularly.

Voto Finale

Students will be taught the theoretical and practical foundations of public policies for research and innovation within economics. The objective is to gain a deep understanding of public interventions on major economic issues, allowing active participation in the debate regarding the adopted policies. Furthermore, students will be able to analyze and discuss the structural and cyclical public finance issues in industrialized economies, focusing on Italy.
Knowledge and understanding: By the end of the course, students will have acquired the basic skills necessary to recognize the main factors influencing the public sector's decisions and modes of intervention regarding possible corrections to market failures. The acquired knowledge will enable them to delve into the motivations for public intervention in the main areas of public spending and to evaluate public expenditure and revenue quantitatively. This also includes analyzing the effect of taxes and public production on the decisions of consumers and businesses and the market equilibrium. Additionally, students will be able to define the consequences of public intervention on economic growth, specific decision-making variables characterizing firms, and the economy's capacity for innovation, both in terms of processes and products.

Autonomy of judgment: The skills acquired during the course will enable students to develop a critical capacity in analyzing the motivations and decisions of public officials, with a specific focus on the role of the government. This analysis extends to regulatory activities and choices made in major areas of public expenditure. Regarding tax imposition, students will be able to understand and evaluate the main reasons behind preferences among consumption, income, and wealth taxes, as well as the consequences for consumer and business investment choices.

Communication skills: Students will be introduced to the nuances of economic language in the context of public intervention, gaining mastery of the sector's specific terminology. This will enable them to use appropriate and precise vocabulary in professional and academic contexts where they will be called to operate. Additionally, students can make connections between the content, including implicit elements, found in exam texts and materials provided by the instructor through the course webpage.

Ability to learn: The objective is to provide students with the foundations to develop an approach that guides them in critically evaluating economic analyses and judgments conveyed by both traditional and digital media. This includes correctly interpreting information related to various forms of economic activity and specific categories of public intervention. Furthermore, the knowledge acquired, along with the analytical categories and methodology for studying public economics, will serve as a fruitful basis for achieving further learning milestones in public policy.

The introductory themes of public economics will be addressed. The course will begin with a quantitative characterization of public economic activity in Italy, analyzing public finance balances over time. This analysis will provide the context to understand the scope and evolution of public intervention. Subsequently, theories that justify public operator intervention in a market economy will be explored. The objective is to examine the reasons, forms, limits, and outcomes of public intervention, focusing on market failures and potential corrections.

The two fundamental theorems of welfare economics will be investigated along with the efficient provision of public goods and the nature of externalities.

A portion of the course will then be dedicated to studying the three main sectors of public expenditure: healthcare, social security, and education. In this section, students will analyze how public decisions are made to allocate resources in these essential sectors. Economic dynamics, public policies, and major challenges in each area will be examined. The goal is to understand the impact of public spending policies on society and the economy, and to assess the effectiveness of public interventions in technological development, research, and innovation in promoting growth, employment, and productivity.

Attention will then turn to an introduction to tax analysis. Different types of taxes will be explored, and their implications critically discussed, with a focus on the effects of taxes on economic agents’ behaviour and business investment decisions. Students will examine how fiscal policies influence economic efficiency and equity and how they can be used to achieve economic policy objectives.

The program will explore Italy's public research and innovation system. In particular, it will be discussed how a strong foundation in national scientific education and research is a fundamental prerequisite for the capacity for innovation in new science-based industries. As universities play a central role in national innovation systems, especially in Europe, any reforms concerning tertiary education systems have significant implications for national and regional innovation systems. The possibility of a compromise between university missions, especially academic excellence and local knowledge spillovers useful for economic growth, will be evaluated. The goal is to examine the regional knowledge networks and modes of engagement between universities and the business community that have increasingly been encouraged by public policies to identify a new set of activities through which higher education institutions interact with their communities - knowledge transfer - in addition to the two traditional roles of teaching and research.

The final part will explore the most effective policies for stimulating innovation, such as taxation, government research grants, policies to increase the supply of human capital focused on innovation, immigration policy, and government incentives for entrepreneurship.

Main topics:

• Elements of Italian Public Finance
• Tools of normative analysis (Welfare economics; First and second fundamental theorem of welfare economics; Market failure)
• Education: why is publicly provided and financed
• Government and the market for healthcare
• Social security, private insurance and market failures
• Introduction to taxation, tax incidence and economic efficiency
• Regional innovation systems and the role of universities in regional economic development and innovation
• Public policies for innovation

The learning outcomes will be assessed through a written examination.

The written examination will consist of a dissertation on a topic discussed during the course. The topic of the dissertation will be agreed upon in advance with the instructor. Students will be required to develop a well-structured and critical essay demonstrating their understanding of the selected topic, their ability to engage with the theoretical and empirical issues addressed in class, and their capacity to integrate and critically discuss the course materials.

The dissertation will be evaluated on the basis of:

knowledge and understanding of the course content;

ability to critically analyse and discuss the selected topic;

ability to develop a coherent and well-structured argument;

appropriate use of relevant concepts, terminology, and references discussed during the course;

clarity, organization, and quality of written expression.

The examination will be graded on a scale of 0 to 30, with 30 cum laude awarded for outstanding performance.

Slides, readings, and other training aids that will be provided by the teacher (uploaded to the teaching webpage) will be discussed during classes.

Reading list (1):

• Barra, C., Maietta, O.W., and Zotti, R. (2017). Innovation and university-firm R&D collaboration in the European food and drink industry. Journal of Agricultural Economics, vol. 68, 3, 749-780.
• Barra, C., Maietta, O.W., and Zotti, R. (2019). Academic excellence, local knowledge spillovers and innovation in Europe. Regional Studies, 53:7, 1058-1069.
• Bonaccorsi, A., Haddaway, H., Cicero, T., and Ul-Hassan, S. (2017). The solitude of stars. An analysis of the distributed excellence model of European universities. Journal of Informetrics, 11, 435-; 454.
• Maietta, O. W. (2015). Determinants of university-;firm R&D collaboration and its impact on innovation: A perspective from a low-tech industry. Research Policy, 44, 1341-;1359.
• Barra, C., Maietta, O.W., and Zotti, R (2021). The effects of university academic research on firm’s propensity to innovate at local level: evidence from Europe. The Journal of Technology Transfer (2021) 46:483-;530.
• Bonaccorsi, A. (2017). Addressing the disenchantment: universities and regional development in peripheral regions. Journal of Economic Policy Reform, 20, 293-;320.
• Goldstein, H. A., and Renault, C. S. (2004). Contributions of universities to regional economic development: A quasi-experimental approach. Regional Studies, 38, 733-;746.
• Laursen, K., Reichstein, T., and Salter, A. (2011). Exploring the effect of geographical proximity and university quality on university-industry collaboration in the United Kingdom. Regional Studies, 45, 507-;523
• Leten, B., Landoni, P., and Van Looy, B. (2014). Science and graduates: How do firms benefit from the proximity of universities? Research Policy, 43, 1398-;1413.

Reading list (2):

• Agasisti, Barra and Zotti (2019). Research, knowledge transfer, and innovation: The effect of Italian universities’ efficiency on local economic development 2006−2012, Journal of Regional Science, 59:819-;849.
• Amendola, A., Barra, C., and Zotti, R. (2023). Universities, local economic productivity and quality of institutions: evidence from Italy. International Regional Science Review, 46(2) 179-;209.
• Amendola, A., Barra, C., and Zotti, R. (2020). Does graduate human capital production increase local economic development? An instrumental variable approach. Journal of Regional Science, 60 (5): 31-36.
• Anderson, R., Quigley, J. M., and Wilhelmson, M. (2004). University decentralization as re- gional policy: the Swedish experiment. Journal of Economic Geography, 4 (4): 371-388
• Cermeño, A. L. (2019). Do universities generate spatial spillovers? Evidence from U. S. counties between 1930 and 2010. Journal of Economic Geography 19 (6): 1173-1210.
• Kantor, S., and Whalley, A. (2014). Knowledge spillovers from research universities: Evidence from endowment value shocks. The Review of Economics and Statistics, 96 (1): 171-188.
• Kantor, S., and Whalley, A. (2019). Research proximity and productivity: long-term evidence from agriculture. Journal of Political Economy, 127 (2): 819-854.
• Valero, A., and Van Reenen, J. (2019). The economic impact of universities: Evidence from across the globe. Economics of Education Review, 68: 53-67
• Andrews , M. J. (2023). How do institutions of higher education affect local invention? Evidence from the establishment of U.S. colleges. American Economic Journal: Economic Policy, 15, 2, 1-;41.

The course aims to provide the instruments for developing a deep understanding of the principles and practices of innovation networks and complexity, focusing on the tools to critically assess innovation network strategies and their impacts on economic, environmental, and social dimensions. The course will explore the importance of networks in addressing global challenges, analyzing the role of technological advancements, sustainability, global value chains (GVCs), and policy in shaping these dynamics.

• KNOWLEDGE AND UNDERSTANDING

By the end of the course, students should understand the fundamental role of innovation networks and complexity, the typologies of innovation networks and their sources, their effects and the sustainability-innovation nexus from a network perspective.

• APPLYING KNOWLEDGE AND UNDERSTANDING

By the end of the course, students should be able to understand and recognize the nature and aims of network for innovation, identify innovation network strategies and management practices, identify relevant features of network and value chains, recognize and apply network analysis instruments and measures, and analyse input-output relationships.

• INDEPENDENT JUDGEMENT

By the end of the course, students will be able to discuss and assess determinants and success of innovation networks, to develop a critical knowledge of the role and impact of networks for innovation in real contexts and on the industrial and innovative network dynamics.

• COMMUNICATION SKILLS

By the end of the course, students will be able to understand and manage the terminology and the main concepts concerning innovation networks and complexity.

• LEARNING SKILLS

By the end of the course, students will learn how to autonomously study and manage innovative networks, with the opportunity to frequently verify the proper understanding of class' main topics.

The course is articulated around three main themes. The first introduces the network and the complexity theory with a discussion on main concepts and definitions, and the specificities of the economic neworks. The second theme deals with the determinants and effects of innovation networks, also exploring global value chains, input-output relationships. Lastly, topics related to network measurement and dynamics will be explored, with a focus on policy and regulations, addressed through lectures and students' individual works and presentations.

The final examination consists of two parts: the evaluation of individual work presentation, and a written exam with open-ended questions. The final evaluation will be the average score obtained in the two parts.

Teaching material (handouts, articles and supporting material) will be provided by the course convenor on the Moodle platform.

This course has the ambition to provide students with an introduction to the plurality of schools of thought in economics that have considered the embeddedness of the economic system within a broader ecological system, and the requirements of sustainability as central concerns. After revisiting the historical origins of so-called ecological economics, the course distinguishes between the alternative theoretical, methodological, and ontological foundations of the various research programs that currently populate the sub-discipline, ranging from those located at the (mainstream) core of economics to those situated at the (heterodox) periphery. The course then illustrates how some of the most innovative approaches—i.e., those that most convincingly have questioned the assumptions of the orthodox perspective in economics and the resulting economic model—can help address the challenges posed by planetary boundaries, including the related need to rethink prosperity and incorporate social concerns into ecological economics.
Upon completion, students:

will be able to recognize the most important debates within the discipline of economics concerning environmental sustainability, and will have acquired knowledge of the appropriate terminology and the theoretical foundations of such debates, including their historical evolution (knowledge and understanding);

will demonstrate awareness of the competition between alternative schools of thought, and will be able to critically discuss the various research programs in economics that investigate the relationship between the economy and the broader ecological system, tracing them back to key differences in their theoretical foundations (applying knowledge and understanding);

will be able to critically grasp the main theoretical assumptions behind policies inspired by the so-called growth paradigm, as well as those that, conversely, primarily aim at sustainability (making judgements);

will be able to make independent judgements regarding the evolution of ecological economics and the specific relevance that its various strands may have in rethinking prosperity (making judgements);

will demonstrate that they have acquired, and are able to apply, the necessary competencies—such as the ability to understand both the theoretical and applied aspects of the assigned readings, independent judgement, and communication skills (communication skills)—to conduct further independent research in a variety of contexts, whether professional or academic (learning skills).

• Economics at an epoch of fragmentation
• The core-periphery-structure of the discipline
• The plurality of schools of thought
• Mainstream economics and mainstream pluralism
• The origins of ecological economics
• The early history of economists' environmental concerns
• Georgescu-Roegen’s work
• Steady-state economics and limits to growth
• The rise and development of ecological economics
• Economics "in a full world"
• The troubles of economic growthmanship
• Individual behavior and the social dimension
• Today's problems
• Prosperity and (without) growth
• Measurement of economic performance: the troubles of GDP, and possible alternatives
• Anthropocene, "Planetary boundaries" and Circular economy
• The present and the future of the discipline
• Environmental economics, "New environmental pragmatists", and "Social ecological economics"
• Economics and (vs.) ecological economics

Assessment in this course is based on an oral exam covering the full range of topics addressed during the course. It requires a reasoned and careful reading of the reference documents indicated here. The exam aims to verify the acquisition of:

the ability to understand the topics and approaches discussed in the course, along with related information;

the ability to analyze concepts using appropriate terminology (particularly technical terms);

the competence to identify connections between concepts.

To access the exam, students must register online via the Esse3 system of the University of Turin and present a valid identification card. Evaluation is expressed on a 30-point scale (maximum score: 30/30); a minimum score of 18/30 is required to pass.

Given the high value this alternative can offer to the academic community, attending students may, and are in fact encouraged to, replace the oral exam with an individual public presentation in English (using PowerPoint or similar tools), lasting approximately 20 minutes and followed by a general discussion. The topic must be previously discussed with and approved by the instructor.

Presentations must be directly related to the course's core content and demonstrate the student’s mastery of the relevant literature. They should apply the theoretical and analytical tools, as well as the historical-methodological perspectives, introduced during the course. Presentations are expected to take place on the same date(s) as the scheduled exam and may benefit from comments and feedback provided by both peers and the instructor.

Mandatory readings for both attending and non-attending students:

• Tim Jackson, Prosperity without Growth. Foundations for the Economy of Tomorrow, Routledge, London and New York, 2017
• Herman E. Daly and Joshua Farley (Eds), Ecological economics: Principles and Applications, 2nd ed., Island Press, Washington-Covelo-London, 2011, parts I and II (chapters 1-7).

Further recommended readings will be made available during the course.

Il corso ha l’obiettivo di fornire agli studenti le conoscenze e le competenze necessarie per:

• Comprendere il concetto di impatto sociale e la sua rilevanza per le organizzazioni private, pubbliche e ibride.
• Sviluppare e implementare metodi e strumenti per misurare e riportare l’impatto sociale.
• Analizzare e valutare i report di impatto sociale esistenti, identificandone punti di forza e aree di miglioramento.
• Promuovere una cultura della trasparenza e della rendicontazione dell’impatto sociale nelle organizzazioni.
  Al termine del corso, gli studenti saranno in grado di:
• Definire e spiegare il concetto di impatto sociale e la sua importanza per vari tipi di organizzazioni.
• Utilizzare metodi quantitativi e qualitativi per misurare l’impatto sociale.
• Redigere un report di impatto sociale chiaro e comprensibile.
• Valutare criticamente i report di impatto sociale e fornire raccomandazioni per il miglioramento.
• Applicare le conoscenze acquisite per promuovere pratiche di rendicontazione dell’impatto sociale in contesti organizzativi reali

Modulo 1: Introduzione all'Impatto SocialeDefinizione e importanza dell’impatto sociale
Tipologie di organizzazioni: private, pubbliche e ibride
Stakeholder e loro ruolo nell’impatto sociale
Modulo 2: Metodi di Misurazione dell’Impatto SocialeMetodi quantitativi: indicatori di performance, metriche finanziarie e non finanziarie
Metodi qualitativi: case study, interviste, focus group
Strumenti e framework: SROI (Social Return on Investment), GRI (Global Reporting Initiative), IRIS (Impact Reporting and Investment Standards)
Modulo 3: Redazione del Report di Impatto SocialeStruttura e contenuto di un report di impatto sociale
Best practice nella redazione dei report
Casi di studio e analisi di report esistenti
Modulo 4: Valutazione dei Report di Impatto SocialeCriteri di valutazione: chiarezza, trasparenza, rilevanza
Analisi critica di report di impatto sociale esistenti
Feedback e raccomandazioni
Modulo 5: Promuovere la Cultura della RendicontazioneStrategie per promuovere la trasparenza nelle organizzazioni
Coinvolgimento degli stakeholder
Sfide e opportunità nella rendicontazione dell’impatto sociale

Project work: Redazione e presentazione di un report di impatto sociale su un’organizzazione reale o simulata

Modulo di Statistica

Coerentemente con gli obiettivi formativi del Corso di Laurea Magistrale in Economics of Innovation for Sustainable Development, l’insegnamento si propone di fornire agli studenti le conoscenze teoriche, applicative e di condivisione della statistica descrittiva e inferenziale. Le capacità acquisite consentiranno di effettuare in autonomia analisi di dati sia in ambito accademico che lavorativo. Gli obiettivi raggiunti saranno una base conoscitiva per i corsi più avanzati.

Modulo di Econometria

L'obiettivo del corso è quello di fornire gli strumenti di base dell'econometria. Lo scopo ultimo del corso è la definizione di cos'è un effetto causale e l'ananlsi delle difficoltà di una sua identificazione.
Modulo di Statistica

Al termine del modulo e coerentemente con gli obiettivi formativi previsti dal corso di "Statistics and econometrics" e dal corso di studi, lo studente

• conoscerà gli strumenti teorici della statistica;
• sarà in grado di usare la terminologia scientifica di base ai metodi studiati;
• sarà in grado di applicare gli strumenti teorici affrontati, selezionandoli opportunamente in ragione degli obiettivi prescelti e dell’informazione disponibile;
• sarà in grado di interpretare e comunicare (almeno in forma scritta) i risultati delle analisi statistiche prodotte o condivise;
• conoscerà e padroneggerà le competenze necessarie per affrontare insegnamenti avanzati.

Modulo di Econometria

CONOSCENZA E CAPACITÀ DI COMPRENSIONE
Comprendere testi scientifici che riportano output di regressione multipla;

CAPACITÀ DI APPLICARE CONOSCENZA E COMPRENSIONE

• Pianificare e realizzare un semplice progetto di ricerca, dalla domanda di ricerca alle conclusioni, alle eventuali raccomandazioni di policy (basi);
• acquisizione di conoscenze utili per una eventuale redazione di tesi di laurea a carattere quantitativo

AUTONOMIA DI GIUDIZIO
(Limitata) Capacità di valutare articoli scientifici che utilizzano strumenti econometrici di base, evidenziandone punti di forza e limiti.

CAPACITÀ DI APPRENDIMENTO
Capacità di sviluppare in autonomia lo studio di metodi econometrici non affrontati nel corso.

Modulo di Statistica

Il programma è il medesimo per tutti gli studenti indipendentemente dalla frequenza alle lezioni frontali.

• Introduzione alla Statistica
• Introduzione alla metodologia statistica (1). Statistica descrittiva e statistica inferenziale (4). Il ruolo dei computer e del software nella statistica (6)
• Statistica campionaria, di misura e descrittiva
• Variabili e loro misurazione (11). Randomizzazione (14). Variabilità del campionamento e potenziale bias (17). Altri metodi di campionamento probabilistico (21)
• Descrivere i dati con tabelle e grafici (29). Descrivere il centro dei dati (35). Descrivere la variabilità dei dati (41). Misure di posizione (46). Statistica descrittiva bivariata (51). Statistiche campione e parametri della popolazione (55)
• Distribuzioni di probabilità
• Introduzione alla probabilità (67). Distribuzioni di probabilità per variabili discrete e continue (69). La distribuzione di probabilità normale (72). Le distribuzioni campionarie descrivono come variano le statistiche (80). Distribuzioni campionarie delle medie campionarie (85). Revisione: popolazione, dati campionari e distribuzioni campionarie (91)
• Inferenza statistica: test di stima e significatività
• Stima puntuale e intervallare (103). Intervallo di confidenza per una proporzione (106). Intervallo di confidenza per una media (113). Scelta della dimensione del campione (120)
• Le cinque parti di un test di significatività (140). Test di significatività per la media (143). Test di significatività per la proporzione (152). Decisioni e tipi di errori nei test (155)
• Confronto di gruppi (179). Dati categorici: confronto di due proporzioni (182). Dati quantitativi: confronto tra due medie (187)
• Analisi dell'associazione tra variabili categoriali
• Tabelle di contingenza (215). Test di indipendenza del chi quadrato (218). Residui: rilevamento del modello di associazione (225). Misurazione dell'associazione nelle tabelle di contingenza (227)

Modulo di Econometria

Il corso fornirà strumenti per analizzare dati di tipo cross-sezionali, seguendo un approccio non sperimentale. Fornirà una conoscenza di livello intermedio del modello di regressione lineare e dello stimatore OLS. Verranno fornite le basi dei modelli per variabili dipendenti discrete e opossibilmente di altri modelli di base quali le variabili strumentali. In seguito riportiamo alcuni topics che saranno affrontati:

• The nature of econometrics and economic data
• The simple regression model
• Multiple regression analysis: Estimation
• Multiple regression analysis: Inference
• Multiple regression analysis: OLS Asymptotics
• Multiple regression analysis: Further Issues
• Multiple regression analysis with qualitative info
• Heteroskedasticity
• Limited Dependent Variable models
• Instrumental variables

Modulo di Statistica

L'esame si svolge in forma scritta.

La prova finale consiste in una analisi statistica, su dati reali, che sarà svolta in laboratorio. Oltre alla parte applicativa saranno richiesti commenti teorici e interpretativi dei risultati ottenuti, e risoluzione di esercizi.

Per motivi organizzativi, gli studenti prenotati all'esame che non intendono sostenere la prova in quell'appello sono caldamente invitati a cancellare la propria iscrizione almeno 3 giorni prima dell'esame. Oltre i 3 giorni si consiglia di inviare un'email a [email protected]

Modulo di Econometria

Esame costituito da due parti, da svolgere in laboratorio informatico.

• quiz di Moodle con domande a risposta multipla.
• esame scritto mirato a valutare la capacità di interpretazione di output di regressione

Il voto finale del corso sarà dato dalla media del voti ottenuti nei due moduli

Modulo di Statistica

E' possibile utilizzare lo stesso testo anche in edizioni precedenti.

Studenti in possesso di testi differenti possono confrontarsi con la docente per valutare la possibilità di utilizzo degli stessi. Il corso sarà comunque definito sul libro di testo consigliato.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

The course aims to equip students with the practical skills to analyse the twin transition (the joint green and digital transformation of the economy) using real-world innovation data. Working in Python (Google Colab), students will learn to query bibliometric databases, construct patent indicators, and trace the science-technology link through citation analysis. The course explores how scientific knowledge and technological invention interact across regions and sectors, with a focus on the European context.
KNOWLEDGE AND UNDERSTANDING
By the end of the course, students should understand the conceptual foundations of the twin transition, the role of directed technical change and path dependence in shaping green and digital innovation, the structure of bibliometric and patent data, and the methods used to measure innovation at the regional level.

APPLYING KNOWLEDGE AND UNDERSTANDING
By the end of the course, students should be able to query the OpenAlex API to build publication datasets, compute bibliometric indicators (trends, geography, impact, thematic structure), work with OECD REGPAT patent data to construct green and digital technology indicators, and trace knowledge flows from science to technology through non-patent literature citations.

INDEPENDENT JUDGEMENT
By the end of the course, students will be able to critically evaluate the strengths and limitations of publication and patent data as proxies for innovation, formulate an original research question inspired by the academic literature, and interpret empirical findings in the context of the twin transition policy debate.

COMMUNICATION SKILLS
By the end of the course, students will be able to present data-driven research findings clearly and effectively, produce well-documented analytical notebooks that combine code, visualisation, and written interpretation, and engage in constructive discussion of methodological choices.

LEARNING SKILLS
By the end of the course, students will be able to independently query open data sources, construct analytical pipelines, and apply bibliometric and patent analysis methods to new research questions beyond the course material.

The course is articulated around three main blocks. The first introduces the twin transition concept, its policy origins, and the theoretical framework (directed technical change, path dependence, knowledge recombination), followed by a hands-on introduction to the data landscape and Python. The second block covers bibliometric analysis using OpenAlex (querying the API, computing publication indicators, mapping knowledge structures) and patent analysis using OECD REGPAT (green and digital classification, specialisation indices, geographic patterns). The third block addresses the science-technology link through NPL citation analysis and culminates in group project workshops and final presentations.

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The final evaluation consists of two parts: a presentation (50%) and a written report in the form of a Colab notebook (50%). The final grade will be based on the combined score of both components.

Teaching material (handouts, articles and supporting material) will be provided by the course convenor.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

The course provides students with the analytical tools to study innovation, technological change, and the green transition through the lens of complexity economics. Students will learn to work with real patent data to measure technological specialisation, map technology spaces, and compute complexity indices. The course combines theoretical foundations with hands-on data analysis in Python (Google Colab).
KNOWLEDGE AND UNDERSTANDING
By the end of the course, students should understand the theoretical foundations of complexity economics of innovation, the concepts of revealed technological advantage, relatedness, relatedness density, economic complexity indices, knowledge complexity, and path dependence. Students should understand how these concepts connect to policy frameworks including smart specialisation and the green transition.

APPLYING KNOWLEDGE AND UNDERSTANDING
By the end of the course, students should be able to work with patent data in Python, compute Revealed Technological Advantage using the Balassa index, construct technology space networks based on co-occurrence, calculate relatedness density to explore diversification, implement complexity indexes.

INDEPENDENT JUDGEMENT
By the end of the course, students will be able to critically assess a region's technological profile, evaluate diversification opportunities and constraints, identify capability gaps in green technology domains, and formulate evidence-based policy recommendations.

COMMUNICATION SKILLS
By the end of the course, students will be able to present a complete "complexity portrait" of a country or region, communicate analytical findings effectively through data visualisations, and defend methodological choices and policy implications in oral discussion.

LEARNING SKILLS
By the end of the course, students will be able to independently construct and execute a data analysis pipeline in Python, critically engage with the economic complexity literature, and apply the course tools to new countries, regions, or technology domains beyond those covered in class.

The course covers the key concepts and measurement tools of complexity economics of innovation and applies them to policy questions including smart specialisation and the green transition. Theoretical foundations from evolutionary and complexity economics are introduced as needed. Theory is combined throughout with hands-on lab work in Google Colab using real patent data, culminating in a group project in which students build a "complexity portrait" of a country or EU region.

The final evaluation consists of two parts: a presentation (50%) and a written report in the form of a Colab notebook (50%). The final grade will be based on the combined score of both components.

Teaching material (handouts, articles and supporting material) will be provided by the course convenor.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.

Tipo di esame: Orale. Valutazione: Voto Finale.