Application of circularity tools for the sustainability evaluation of the urbanization process (case study; city of Seville)

Cities worldwide face unprecedented challenges in achieving the Sustainable Development Goals (SDGs), particularly in the transition toward circular economy principles that emphasize resource efficiency, resilience, and inclusivity. Despite growing recognition of the circular economy as a pathway for sustainable urban transformation, existing quantitative assessment methodologies remain limited by incomplete indicator sets, uncertainty regarding coverage of sustainability objectives, and the absence of systematic evaluation models capable of capturing multi-scalar dynamics. To address these gaps, this study proposes a comprehensive AI-driven framework that combines Natural Language Processing (NLP), Multi-Criteria Decision-Making (MCDM), advanced machine learning techniques, and Geographic Information Systems (GIS) to develop a robust city circularity evaluation system. The methodology unfolds in three phases: (1) Indicator Identification – a systematic literature review and semantic analysis, supported by NLP and clustering algorithms, led to the classification of 241 circular city indicators, of which 75% are multidimensional across economic, environmental, and social domains; (2) Quantitative Assessment – integration of dimensionality reduction, factor analysis, and neural autoencoder techniques consolidated 123 distinct metrics into 59 final indicators, enabling the development of a pioneering Ideal Circularity Score (ICS) of 19.573 for standardized benchmarking; and (3) Spatial Intelligence – a GIS-based plugin translates assessment results into interactive maps, incorporating satellite imagery, climate datasets, and interpolation techniques to provide dynamic spatial insights. Validation through the case study of Seville, Spain (2003–2023), illustrates the framework’s effectiveness. Results demonstrate a circularity score of 6.55 in 2016 (33.5% of the ideal score 19.57) and 7.25 in 2023 (37.05% of the ideal benchmark) and reveal significant urban sprawl accompanied by a rise in land surface temperature of up to 6.4°C in newly developed areas. Findings highlight strong inverse correlations between uncontrolled urbanization and circular economy objectives, underscoring the urgent need for climate-responsive planning. By scaling from parcel-level analysis to city-wide assessments, the framework identifies vulnerable zones, evaluates temporal shifts in circularity, and generates future-oriented scenarios based on the intensity, direction, and magnitude of transformations. This research contributes the first holistic, AI-supported methodology for urban circularity assessment, offering policymakers a replicable, evidence-based tool to advance circular, resilient, and sustainable urban futures across diverse global contexts.

Author dc:creator

• Falah, Nadia

Advisors dc:contributor.advisor

• Marrero Meléndez, Madelyn
• Solís-Guzmán, Jaime

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/11441/186857

OAI identifier oai:identifier

oai:idus.us.es:11441/186857