Urban Science

Urban retail environments are social and economic manifestations of a city, enhancing economic growth and social cohesion. However, they increasingly face challenges from economic downturns, changing consumer preferences, and spatial dynamics, making their ability to adapt and remain viable a critical concern. In this context, retail resilience refers to the capacity of urban retail environments to absorb disturbances, adapt to change, and sustain their economic and social functions over time. Despite growing interest in urban resilience, the operationalization of retail resilience through spatially explicit and measurable indicators remains limited, as many assessments focus on city or regional scales and overlook variations at the neighborhood level. Thus, this paper aims to develop a geospatial multi-criteria model yielding a composite Urban Retail Resilience Index (URRI) to analyze and interpret retail resilience in Saida’s urban retail environment through an adaptive cycle lens. The URRI combines indicators related to diversity, spatial proximity, and socioeconomic conditions, and is applied using two weighting scenarios—baseline and stakeholder-based weights—to test the model’s robustness and reflect local priorities. The results reveal distinct spatial variations in retail resilience across the study area, enabling the identification of hotspots for interventions and highlighting the role of accessibility and diversity in shaping the adaptive capacity. These findings confirm that Saida’s retail resilience is closely linked to walkability and socio-cultural characteristics. The proposed geospatial multi-criteria model provides a robust and replicable framework for assessing retail resilience, offering practical insights for urban planners and policymakers.

Rapid industrialization and urbanization have significantly accelerated carbon dioxide emissions, intensifying climate mitigation challenges. Accurate micro-scale assessment of urban carbon emissions is imperative for formulating effective reduction policies in China; however, current efforts are often constrained by a lack of high-resolution data, limiting the ability to capture fine-grained spatial heterogeneity. To address this gap, this study integrates the 1 m resolution national land cover product (SinoLC-1) with OpenStreetMap (OSM) networks and point of interest (POI) data to delineate urban functional zones in Beijing. We subsequently developed a method to estimate and spatially allocate carbon emissions at a 1 m resolution across these zones, categorized by industrial sector. Results for 2020 indicate that carbon sequestration by forests, water bodies, and grasslands totaled approximately 624,900 tons, while total emissions from cultivated land and energy consumption in built-up areas reached 107,692,300 tons. Built-up land was identified as the primary carbon source, whereas forestland and water bodies functioned as key sinks. Notably, the tertiary industry accounted for the largest share of energy-related emissions (41.80%), driven primarily by electricity and kerosene consumption. Spatially, emissions exhibited pronounced heterogeneity, with high-value clusters concentrated in the central urban core and specific suburban hubs. Cross-validation demonstrates that this functional-zone-based spatial allocation method significantly outperforms traditional nighttime light-based approaches in resolving micro-scale emission patterns. This high-resolution analysis improves the characterization of spatial variability in urban carbon cycles, offering robust data support for precision low-carbon planning and energy management.

Medium-sized cities are increasingly affected by processes of urban fragmentation and residential segregation, despite having traditionally been perceived as more socially cohesive and territorially balanced than large metropolitan areas. Acting as functional connectors between metropolitan hubs and rural regions, these cities are particularly vulnerable to unplanned suburban growth, housing market polarization and uneven access to urban opportunities. This study develops and applies a multidimensional Urban Territorial Index (UTI) to diagnose socio-spatial inequality in medium-sized cities, using Ciudad Real (central Spain) and its functional urban area as a case study. The UTI integrates six indicators across three analytical dimensions—socioeconomic, sociodemographic and housing—through a PCA-informed weighting scheme and GIS-based spatial analysis. The index is calculated at census-tract and neighborhood scales and is validated through internal consistency checks, external comparison with a local Human Development Index (r = 0.87; p < 0.001), and qualitative robustness assessments. Results reveal a pronounced core–periphery polarization: central and strategically located neighborhoods associated with key infrastructures (university, high-speed rail station and hospital) concentrate higher income levels, educational attainment and land values, while peripheral municipalities and disadvantaged neighborhoods exhibit higher unemployment, lower housing values and greater social vulnerability. The analysis also identifies population–housing mismatches linked to suburban expansion without equivalent functional integration. Beyond the local case, the study provides a transparent and replicable methodological framework tailored to medium-sized cities, where metropolitan-scale indices often fail to capture fine-grained socio-spatial disparities. The UTI offers a practical tool for comparative analysis, temporal monitoring and evidence-based urban policy, supporting more inclusive and territorially balanced development strategies in diverse institutional and geographical contexts.