The construction sector accounts for over 30% of global material extraction and up to 40% of solid waste, making Construction and Demolition Waste (CDW) a major sustainability challenge. This study applies the Digital Circularity Alignment Model (DCAM), supported by evidence from the RECONMATIC project, to compare policy and technological transition pathways toward circular construction in the European Union, the United Kingdom, and China. The analysis examines interactions between regulatory instruments and digital infrastructures, including Building Information Modelling (BIM) and Digital Product Passports. Results reveal a global shift toward mandatory lifecycle transparency, where digital tools function as compliance infrastructures. Crucially, the comparative review identifies distinct governance drivers: EU supranational regulation, UK procurement-driven frameworks, and Chinese state-led macroeconomic planning. Despite these differences in governance approaches, progress remains constrained by fragmented data ecosystems, limited interoperability, and uneven institutional capacity. The findings show that the principal barrier to scaling circularity is not technological availability but the misalignment between policy frameworks, data standards, and implementation mechanisms. Achieving a zero-waste construction sector by 2050 therefore requires interoperable data ecosystems and stronger policy–technology alignment.

Over the past few decades, the rapid growth of cities has evolved into a significant social, demographic, and architectural phenomenon, highlighting the vital importance of urban planning in fostering sustainable development. In this context, machine learning has emerged as a game-changing discipline, utilizing advanced algorithms to reshape traditional approaches to urban data management and analysis.This study combines Geographic Information Systems (GIS), Deep Learning techniques, and verified data from the General Directorate of the Spanish Cadastre to perform a comprehensive analysis of the urban environment through façade images in Murcia, one of Spain’s most dynamic metropolitan areas.Leveraging the clustering analysis of the studied variables, an automated binary classification model for façade images was developed using the pretrained EfficientNetB0 architecture in Python. To enhance interpretability, heat maps were generated to visualize the regions the model focuses on during classification. These heat maps reveal the critical features of the facades that guide the model’s decisions, providing valuable insights into the key factor influencing the classification process.The results were integrated into ArcGIS PRO, using the cadastral reference of the properties as a key attribute for a detailed spatial analysis. This approach revealed two significant areas linked to the metropolitan growth of Murcia, laying a strong foundation for future urban studies in the region.

Durante la última década, el municipio de Guadalajara, Jalisco (México) enfrenta los retos de una urbanización acelerada y dispersa en la que resalta la fuerte demanda de viviendas asequibles que garanticen el bienestar de la población que en la urbe habita. No obstante, el municipio carece de un criterio de valoración objetivo. Razón por la que la investigación se centra en realizar el análisis estadístico clásico y geoestadístico para evaluar el mercado inmobiliario local. La aplicación de la estadística en el ámbito de la valoración inmobiliaria pretende el análisis de datos provenientes de una muestra representativa de viviendas del municipio que permita explicar las correlaciones y dependencias de las mismas con la finalidad de estimar su valor. Por tanto, el análisis valorativo de las viviendas particulares del municipio de Guadalajara parte de la recopilación y homogenización de la información espacial disponible del Catastro y del Instituto Nacional de Estadística y Geografía (INEGI) categorizando variables, a nivel de manzana, que influyen en el precio de las viviendas. Tras obtener estas variables, se utilizaron dos técnicas estadísticas para su análisis. El modelo de Regresión Lineal Múltiple de los datos que identifica las variables que influyen en el valor del inmueble permitiendo comprender la relación entre distintas variables y el valor inmobiliario, así como predecir o estimar los precios de las viviendas. La segunda técnica aplicada fue el análisis estadístico espacial mediante el Kriging Ordinario, que permitió estimar valores desconocidos o no muestreados considerando no solo la proximidad de los puntos conocidos, sino también la relación espacial entre ellos, generando un mapa de las estimaciones de los valores inmobiliarios en toda la zona de estudio. La combinación de estos métodos nos permite abordar de manera integral la complejidad de las relaciones espaciales y multivariadas del mercado inmobiliario en la zona de estudio.

This study examines the international regulatory and strategic landscape driving the twin transition—digital and circular—within the construction sector, with a specific focus on the management and valorisation of Construction and Demolition Waste (CDW). The analysis concentrates on the regulatory frameworks of the European Union (EU), the United Kingdom, and China, highlighting how policies and standards seek to address data fragmentation and the uneven level of digital maturity across the sector. The study assesses alignment with key EU policy instruments, including the European Green Deal, the Circular Economy Action Plan (CEAP), the proposed Construction Products Regulation (CPR), and the Digital Product Passport (DPP). It also explores the integration of technical standards such as BIM EN ISO 19650 and EN ISO 20887 for Design for Disassembly. In parallel, strategic initiatives in the United Kingdom and China are examined, notably the Build Back Greener strategy and the 14th Five-Year Plan for the Development of the Circular Economy. The findings identify common opportunities related to the adoption of digital technologies for material traceability and selective deconstruction. Overall, the global regulatory framework is increasingly oriented towards mandatory life cycle data (LCA) and digital transparency, establishing a strategic foundation to accelerate the transition towards a zero-waste construction sector by 2050.

The expansion of cities in recent decades represents a significant social, demographic, and architectural phenomenon, underscoring the critical role of urban planning in achieving a balanced and sustainable approach to territorial development. Machine learning, a branch of artificial intelligence (AI), employs algorithms to extract insights from data, uncover patterns, and predict future outcomes. In the urban domain, while satellite imagery has long been the preferred choice for territorial analysis, facade images have recently emerged as a valuable resource. These images offer rich visual information, making them indispensable for studies ranging from identifying structural vulnerabilities to assessing urban vitality. This study combines Geographic Information Systems (GIS) and Deep Learning techniques with verified data from the General Directorate of Cadastre in Spain to analyze the urban environment using facade images from the city of Murcia, one of Spain’s most dynamic metropolitan areas. This study presents the development of an automated facade image classification model, implemented in Python using the pre-trained EfficentNetB0 architecture. This model, trained with cross-validation techniques, performs a binary image classification as part of a clustering analysis applied to the variables under investigation. The results were integrated into ArcGIS PRO, leveraging the cadastral references of the properties as key attributes for detailed spatial analysis. This approach enabled the identification of two significant areas linked to Murcia’s metropolitan evolution. In conclusion, the model successfully achieves the research objectives and serves as a foundation for more in-depth urban studies focused on the city of Murcia.