Dr. Youcef Chakali, Dr. Fateh Zenati, Dr. Mustapha Nouri (p), Dr. Mahfoud Tahlaiti (p)

The concept of Urban Mining proposes a transformative vision by considering the built environment as a regional resource mine. It views urban areas not only as places of consumption, but also as strategic reservoirs of raw materials that can be recovered at the end of a building’s life. However, the lack of short- and medium-term visibility regarding the type and quantity of recoverable materials remains a major limitation. Therefore, a combined analysis of material stocks and inflows and outflows represents a key challenge for understanding and managing the metabolism of socio-economic systems. In this context, a bottom-up, building-by-building methodology based on an artificial intelligence model was developed and applied to the Pays de la Loire region in France. This study aims to identify the main material stocks and flows resulting from the deconstruction of residential buildings in order to provide a detailed understanding of the resources available in the territory. The study led to the development of five artificial neural network (ANN) models to predict the envelope materials of residential buildings including wall type, wall thickness, floor type, and roof type. The French National Building Database (BDNB) was used as the main data source. The results show good accuracy, with high correlation coefficients (R > 0.90 for some models) and mean absolute percentage errors (MAPE) below 20%. These models were then used to fill in missing data for 488 953 residential buildings in the regional database. Based on this completed dataset, the total stock of materials in building envelopes was estimated at 92.36 million tonnes (Mt). Concrete accounts for the largest share (74%), followed by stone and mixed aggregates (14%), brick (7%), wood (2%), and tiles (1%). Finally, construction and demolition waste was estimated for the 2024–2030 period using deconstruction scenarios. In 2025, waste generation is estimated at 254 kt, of which 41% originates from Nantes Métropole. Single-family houses account for 79% of the waste, and 65% comes from buildings constructed before 1945. Concrete represents more than 75% of the generated waste. These results make it possible to anticipate waste flows and to adapt resource management and recycling strategies accordingly.

Prof. Gunther Maier (p), Prof. Sabine Sedlacek

This paper presents an empirical analysis of reproducibility of published regional science articles. We investigate articles published in four main regional science journals over a period of five years.
In our analysis we focus on one of the most basic requirements for reproducibility, the availability of the data. We analyze the selected articles and assign each to one of four categories. Category A contains articles where the data can be directly accessed by the reader based on the information provided. Only 3% of the Regional Science articles we investigated fall into this category. On the other end of the spectrum, category D contains those articles, where the reader cannot get access the data at all (because of data privacy, lack of information, author's unwillingness to share, etc.). 28% of the Regional Science articles we investigated fall into this category.
In our paper, we will explain, why we find this result to be alarming. We will compare the results to those of a comparable analysis for Real Estate journals. The detailed results over all the categories broken down by journal and year of publication provide some hints for how to improve the situation. Regional Science is in a more favorable position to achieve a major improvement than Real Estate. We will discuss some possible strategies for journals and organizations like ERSA to advance reproducibility in published Regional Science research.

Ms. Margarita Iliadou (p), Dr. Spyros Niavis

Citizen Science (CS), defined as the active involvement of citizens in scientific research addressing societal challenges, has emerged as a transformative approach in contemporary urban and regional development. By enabling public participation in data collection and analysis, CS expands the knowledge base available to researchers and policymakers while fostering collaboration between volunteers and academic institutions. Unlike traditional science communication initiatives, CS generates original data and tangible scientific outputs, thereby contributing directly to evidence-based policy design.
This paper examines how CS initiatives promote local development by strengthening collaborative governance, enhancing data-driven planning, and empowering communities. Drawing on literature and selected case studies, it highlights the capacity of CS to support planning processes related to land use, environmental monitoring, biodiversity conservation, and social-ecological resilience. Through structured evaluation frameworks that assess impacts across society, governance, economy, environment, and science, CS demonstrates multidimensional benefits extending beyond knowledge production. These include increased environmental literacy, skill development, civic participation, and strengthened community cohesion.
Within urban and regional contexts, CS contributes to more inclusive and adaptive development strategies by integrating local knowledge into formal planning mechanisms. It supports sustainable policy implementation, enhances trust between institutions and citizens, and fosters social justice by enabling communities to articulate local needs and monitor environmental risks. Technological advancements associated with smart city frameworks further expand the scope of CS, facilitating real-time data generation and participatory design processes.
Despite its promise, the institutional integration of CS faces structural challenges, including data quality assurance, digital inequalities, and the need for sustained financial and organizational support. Addressing these barriers requires standardized protocols, transparent communication, and long-term policy commitment.
Overall, CS represents an innovative participatory methodology that bridges scientific research and local governance. By aligning community-generated knowledge with regional policy objectives, it offers a viable pathway toward resilient, sustainable, and socially inclusive urban and regional development.

Prof. Silvia Sousa (p), Prof. Orlando Belo

People’s perceptions of labour market policies play a critical role in shaping their political sustainability and effectiveness. Yet these perceptions may be territorially uneven and shaped by recent policy reforms. The evaluation of these perceptions, as well as opinions or even the expression of sentiments, is an important analytical dimension to assess the substantive effectiveness, operational efficiency and social legitimacy of labour market policies and can be deepened through the application of sentiment analysis techniques that enable the extraction, classification and automated interpretation of sentiment and evaluative patterns present in the means of expression of such perceptions. This study introduces a regional dimension to the analysis of attitudes toward labour market interventions, using Portugal as a case study,.Drawing on a specialized corpora extracted from several social media platforms, regional news outlets, and parliamentary debates, we apply natural language processing techniques to detect sentiment polarity and patterns, while geolocating opinions across Portuguese regions of the continental territory. Using lexicon-based approaches, we identify and classify evaluations of key policy instruments. The analysis situates these perceptions within the context of major labour policy changes in Portugal over the last few years. After 2011, austerity-linked reforms tightened employment protection and altered unemployment benefit rules; in the mid-2010s and beyond, successive governments introduced measures aimed at rebalancing worker protections, increasing minimum wages, and strengthening activation programs to reduce long-term unemployment. The recent proposal aiming at changing the labour law has already generated strong controversy, leading to general strikes. Nevertheless, it is expected that regional specific economic structures—such as export-oriented clusters around Porto versus rural areas in the interior with persistent job scarcity—shape dominant narratives, even though results are still preliminary to lead to assertive conclusions. By embedding computational text analysis within a spatial framework informed by Portuguese (regional) socioeconomic indicators, this study offers a scalable methodology for monitoring geographically differentiated public opinion. The preliminary findings suggest that labour market reforms in Portugal may be interpreted not only through ideological lenses but also through the lived economic realities of specific regions, underscoring the need for place-sensitive policy design and communication. In this paper, we present and discuss how we evaluated people’s perceptions of labour market policies, using specialized lexicons for the identification and characterization of their sentiments, describing the selection criteria, the operationalization process and how we were able to analyse systematically emotional patterns for evaluating the impact of changing labour market policies.