Francesca Ghinami (p), Sandro Montresor, Stefano Usai

Lluís Viñé

Identifying green, digital, and twin patents is crucial for assessing innovation trends and evaluating policy effectiveness in sustainability and technology. The literature offers various approaches, from code-based classification to machine learning techniques, each with distinct strengths and limitations. However, the absence of a universally accepted methodology for identifying such patents undermines the consistency and comparability of findings across studies. In this article, we examine existing methods, highlighting their advantages and constraints, and propose a methodological framework to address current limitations, enabling a more robust and scalable identification of twin patents. We introduce a fully automated, hybrid and unsupervised patent landscaping approach that systematically identifies relevant digital-sustainable "twin" patents through an open-source, replicable, and consistent methodology. This method integrates a rule-based seed identification—leveraging various combinations of keywords, IPCs, and CPC codes—with a bidirectional citation expansion, along with a pre-trained PaECTER BERT model for classification, ensuring a precise, replicable and efficient selection process with low computational costs. We employ the Patstat 2024 Autumn database, and test this method on a challenging category of patents, containing both a digital and a sustainable technological component. Preliminary results reveals minimal to no overlap between patents identified as twin by each of the existing methods, underscoring the challenges of capturing a cohesive set of relevant sustainable-digital innovations. While robustness, accuracy and precision tests are still ongoing, the first results seems to indicate that this hybrid approach effectively improves both precision and comprehensiveness in twin patent identification.

Lluís Viñé (p), Laura Armayones

İsra Hatipoğlu

The twin transition—green and digital—is a key priority for the European Commission, yet it presents challenges related to alignment and equity. Policies may unintentionally create advantaged and disadvantaged groups, underscoring the need for a just transition to ensure no one is left behind. The FITTER-EU project addresses this challenge by developing an inclusive scenario-building methodology to anticipate future socio-technical transformations across four sectors: building and housing, transport and mobility, energy, and agriculture. The project focuses on six European countries (Spain, Germany, Hungary, Italy, Ireland, and Portugal) and aims to forecast three alternative scenarios for 2050, identifying key drivers, risks and impacted populations.
This study applies the Multi-Level Perspective (MLP) framework, which differentiates between niches (emerging innovations), regimes (established systems) and landscapes (macro-level influences). Through this approach, exploratory scenarios are developed to evaluate sectoral transition pathways. The methodology integrates a PESTLE analysis (Political, Economic, Social, Technological, Legal, and Environmental factors), 30 semi-structured expert interviews, and three iterations of a Delphi survey with stakeholders, including policymakers, civil society representatives and sectoral experts.
Findings highlight the potential of MLP in scenario-building and emphasize the role of participatory foresight in understanding sectoral transitions. The study identifies critical transition drivers and their socio-economic implications, helping policymakers anticipate vulnerabilities and develop mitigation strategies for disadvantaged groups. The final set of scenarios serves as a foundation for policy adaptation, stakeholder engagement and cross-sectoral dialogue.
This research contributes to foresight methodologies by refining scenario-building approaches for inclusive and evidence-based policymaking. Future research will explore integrating quantitative modeling and dynamic indicators to further enhance scenario analysis.

Mehmet Ronael, Pınar Gökçe, İsra Hatipoğlu (p), Cihan Mert Sabah

Daniela Storti

Urban areas worldwide face a pressing challenge: balancing expansion with resilience against seismic activity. Given its location on a fault line and history of earthquakes, Gemlik, a district in Bursa, Turkey, serves as an ideal case study for addressing this issue. This research employs advanced geospatial methodologies, particularly a Weighted Overlay Analysis using Geographic Information Systems (GIS), to enhance earthquake-resistant urban planning.

To achieve this, the study introduces an innovative method that integrates the Analytical Hierarchy Process (AHP) with AI-driven simulations. A key contribution of this research is the use of AI-driven role-playing entities to represent diverse human archetypes—such as farmers, white-collar professionals, and developers—within the AHP framework. Unlike traditional approaches that rely on human participants, these AI entities provide varied perspectives, enhancing the objectivity and efficiency of the decision-making process. Through a rigorous analytical model, they evaluate key environmental and physical factors, including elevation, slope, aspect, land use, soil classification, erosion levels, geological structure, proximity to transportation networks, and access to urban amenities. By assuming distinct roles, the AI-driven entities assign variable weights to these factors in a manner similar to human experts.

This study has two primary objectives. First, it seeks to identify optimal locations for future urban development in Gemlik that minimize earthquake risks, thereby enhancing the city’s resilience. Second, it serves as a pioneering pilot project, demonstrating the feasibility and effectiveness of AI integration in urban planning. By combining the precision of AI-driven analysis with the nuanced perspectives of simulated human stakeholders, this research presents a novel framework for tackling complex urban development challenges in an era of rapid technological advancement and urbanization pressures.

Daniela Storti (p), Stefania Leone, Andrea Orio

Francesca Ghinami

Inner areas (remote rural areas) are characterized by low population density, distance from major services, and often a strong dependence on agriculture. However, these regions face significant challenges, such as population aging and abandonment of agricultural lands. Youth participation to social innovation processes is crucial for the sustainability of these areas.
This paper aims to explore the motivations, challenges, and opportunities for young people who choose to live in Italy’s Inner Areas, with a particular focus on their perceptions of agriculture, natural resources, and the environment. Furthermore, it seeks to suggest policy actions to support youth in these remote rural areas, promoting sustainable development and improving their quality of life.
Our methodology is based on a mixed-methods approach, combining qualitative and quantitative techniques, and a path of action-research.
The first part of the contribution analyzes theoretical perspectives on Remaining and Mobility and evidence from previous studies on youth conditions in remote rural areas (Farruglia D., 2016, Leone S. e Orio A., 2024; Membretti A. e al., 2023), to answer two main research questions:
- RQ1: Are there any relationships between the reasons to remain in the Inner areas for some young people and their representation of environmental source?
- RQ2: What factors are significant for the realization of personal and community-related projects by actives young people who remain in the internal areas?
The second part of our work focuses on public policies aimed at better supporting young people in remote rural areas. To this end, we will present the first results of an ongoing project, Scuola Giovani Pastori (The School for Young Shepherds), an action research initiative targeted at young people in remote rural areas of Italy, promoted by the Council for Agricultural Research and Economics (CREA) and the Association Riabitare l’Italia.
The school is based on participatory training methods and networking, involving local farmers, researchers, and stakeholders. It focuses on shepherding skills and offers experiential training on key topics such as regenerative grazing and the artisanal processing of raw materials, while also supporting entrepreneurship development. The aim is to enrich the training experience and foster new, lasting connections among participants. Through active engagement, the project seeks to promote community empowerment.
This paper provides an in-depth analysis of the dynamics influencing youth activism in remote rural areas. Policy recommendations will guide institutions in designing effective support programs for young people, contributing to the economic and social sustainability of these regions.