The “15-minute city” (15minC) model has gained global attention as a framework for promoting walkability and sustainable urban mobility. While its adoption has grown, challenges remain in adapting the model to diverse urban contexts worldwide. This paper critically examines the 15minC model, its theoretical limitations, and the need for more context- and user-based approaches to ensure sustainable urban mobility and well-being outcomes across varying morphological, climatic, and cultural settings. The article then introduces the concept of CityTime, which redefines proximity as borderless and adaptable, measuring the minutes needed to walk to customizable ecosystems of urban functions. Using a GIS-based approach, it evaluates the accessibility of points of interest (POIs) to balance their quantitative (proximity) and qualitative (diversity) distribution, proposing new metrics. The approach is tested in Dakar, Rio de Janeiro, and Milan—cities that vary significantly in size, density, and geographic location, representing both the Global North and Global South. Within each city, three distinct areas are selected using a multidimensional clustering method based on POI distributions. These case studies demonstrate the flexibility of CityTime across diverse morphological and socio-economic conditions and its potential to democratize the 15minC ideals, creating more equitable and context-sensitive urban environments globally.

Rapid urbanization has intensified the systemic inequities of resources and infrastructure distribution in informal settlements, particularly in the Global South. Digital Twin Modeling (DTM), as an effective data-driven representation, enables real-time analysis, scenario simulation, and design optimization, making it a promising tool to support urban resilience. This study introduces the Integrated Modification Methodology (IMM), developed by Politecnico di Milano (Italy), to explore how DTM can be systematically structured and transformed into an active instrument, linking theories with practical application. Focusing on Paranoá (Brasília), a case study developed under the NBSouth project in collaboration with the Politecnico di Milano and the University of Brasília, this research integrates advanced spatial mapping with comprehensive key performance indicators (KPIs) analysis to address developmental and environmental challenges during the regeneration process. Key metrics—Green Space Diversity, Ecosystem Service Proximity, and Green Space Continuity—were analyzed by a Geographic Information System (GIS) platform on 30 m by 30 m sampling grids. Additional KPIs across urban structural, environmental, and mobility layers were calculated to support the decision-making process for strategic mapping. This study contributes to theoretical advancements in DTM and broader discourse on urban regeneration under climate stress, offering a systemic and practical approach for multi-dimensional digitalization of urban structure and performance, supporting a more adaptive, data-based, and transferable planning process in the Global South.

Urban green spaces are essential for mitigating the heat island effect, supporting ecosystem services, and maintaining biodiversity. The distribution, fragmentation, and connection of the green spaces significantly impact the behavior of species in cities, serving as key indicators of environmental resilience and ecological benefits. However, current studies, as well as planning standards, often prioritize green spaces independently through their coverage or density, overlooking the importance of continuity and its impact on thermal regulation and accessibility. In this research, urban “hidden green corridors” refer to the unrecognized but functionally significant pathways that link fragmented green spaces through ecological behaviors, which enhance both biological and human habitats. This research focuses on developing an agent-based simulation (ABS) model based on the Physarealm plugin in Rhino, which can assess the effectiveness of these hidden corridors in different urban settings by integrating geographic information systems (GIS) and space syntax. Based on three case studies in Italy (Lambrate District, Bolognina, and Ispra), the simulation results are further interpreted through the AI agentic workflow “SOFIA”, developed by IMM Design Lab, Politecnico di Milano, and compared using manual analysis as well as mainstream large language models (ChatGPT 4.0 Web). The findings indicate that the “hidden green corridors” are essential for urban heat reduction, enhancement of urban biodiversity, and strengthening ecological flows.

We are rapidly approaching the midpoint of the period set by the UN Agenda 2030 for transforming our activities on the planet Earth. Yet, many of the global issues we have begun with stand still while some of them took a much more severe course. The Sustainable Development Goals (SDGs) are unambiguous. What is unclear is the way to propel our socio-economic organizations into the desired performances. Regarding performance control, the built environment is arguably the most critical system. Every year, we add more urbanized land to the face of the planet without clearly envisioning its environmental effects and social outgrowth. As a result, we frequently find ourselves struggling with unpleasant consequences and unpredicted side effects. In our body of knowledge, there is a missing link between urban structure and performance. This paper offers a systemic breakdown of urban structural parameters and a coherent reading of its performance indicators intending to create a City Information Modelling (CIM) tool. The objective is to formulate the reciprocal relationship between the components of the built environment (as the designer's toolbox) and the system's outcome. To do so, this article moves from the Integrated Modification Methodology (IMM), exploring it through the lens of an Entity-Relationship (ER) schema, delving into its systemic approach to model the built environment as a Complex Adaptive System (CAS). IMM elucidates the intricate structure/performance relationships inherent in the complexity of the built environment. Building upon the successful IMM application in numerous urban transformation projects and retrofitting procedures, our focus is now on streamlining operational processes and accelerating workflow efficiency. Specifically, we seek to optimize the rapid assessment of correlations between structural changes and performance outcomes within the IMM framework. This work aims to operationalize the Agenda 2030, enabling a practical methodological interpretation of targets and indicators in SDG11.

Urban green spaces play a critical role in mitigating heat stress and enhancing urban livability, in line with the objectives and expectations of the United Nations Sustainable Development Goals 10 (Reduced Inequalities) and 11 (Sustainable Cities and Communities). This study employs Physarealm (Grasshopper), a lightweight agent-based simulation (ABS) model, to dynamically simulate pedestrian behaviors for different mobility groups. Together with Space Syntax, the results—time-extended movement and interaction patterns—are conceptualized as a relational configuration of green space provision (supply), pedestrian activity intensity (demand), and thermal exposure (environmental resistance). Three contrasting urban areas in northern Italy (Lambrate, Bolognina, and Ispra) are selected as case studies. The results demonstrate that urban inequality cannot be sufficiently explained by the inadequacy of single components, but emerges from imbalanced relational configurations of supply, demand, and environmental resistance. In May, 100% and 95% of traversed cells in Lambrate and Bolognina fall within the high-heat-stress range (>32 °C), compared with 59% in Ispra. Correspondingly, average green provision within the 5 min walking range is 5.4% in Lambrate, 7.2% in Bolognina, and 37% in Ispra. By uncovering relational mismatch patterns that are often overlooked in conventional urban analyses, this study enables a multi-dimensional diagnosis of imbalances. By positioning ABS as a front-end process generator and Space Syntax as a structural interpretation step, it demonstrates how dynamic behavioral processes can be reorganized into network-scale diagnostic representations. The study supports a climate-sensitive and human-centered diagnosis of walkability and green space accessibility, while contributing a transferable analytical approach for identifying relational inequality patterns within open urban data science contexts.