The operational municipal solid waste management policy focuses on identifying waste generation behavior across multiple levels in mega cities, a case study
The results of an effort to create a thorough framework that efficiently arranges and combines quantitative data characterizing the performance of Urban Waste Management Systems (UWMS) across a range of scales and dimensions are presented in this study. The theory of metabolic networks and the Multi...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:uabarcelona_::d3516e6bc51fa9db5c0f702bb066e664 |
| Acceso en línea: | https://ddd.uab.cat/record/327334 https://dx.doi.org/urn:doi:10.1007/s43621-025-01685-w |
| Access Level: | acceso abierto |
| Palabra clave: | Impact indicator Municipal waste management Tehran city Waste metabolic networks SDG 7 - Affordable and Clean Energy SDG 11 - Sustainable Cities and Communities SDG 12 - Responsible Consumption and Production |
| Sumario: | The results of an effort to create a thorough framework that efficiently arranges and combines quantitative data characterizing the performance of Urban Waste Management Systems (UWMS) across a range of scales and dimensions are presented in this study. The theory of metabolic networks and the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) serve as the foundation for the developed framework. According to this conceptualization, the UWMS functions as an organ within a socio-ecological system, controlling urban metabolism and providing resources and capacity for regional sinks within the larger waste management system. This study demonstrates how impact indicators other than income, like district size, financial types, and economic activity, can be identified as explanatory variables when employing Python software and the K-means method to analyze MSW generation behavior across multiple organizational levels. The findings reveal significant disparities in waste generation across Tehran, the capital city of Iran. Waste density varied from 25 to 861 kg/ha, with particularly high values in three emerging districts. Developing areas exhibited higher waste production due to increased consumption facilities for different social elements. Moreover, developed regions produce more waste per person, attributed to higher input (consumption) and consequently higher waste generation. Furthermore, a waste estimation analysis was conducted to systematically assess and compute the volume and types of waste generated within specific regions. These insights contribute to optimizing waste management strategies, offering data-driven guidance for policymakers and urban planners to enhance sustainability and efficiency in waste management systems. |
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