Sustainable Urban Management Research Articles

Energy-Efficient and Comprehensive Garbage Bin Overflow Detection Model Based on Spiking Neural Networks

With urbanization and population growth, waste management has become a pressing issue. Intelligent detection systems using deep learning algorithms to monitor garbage bin overflow in real time have emerged as a key solution. However, these systems often face challenges such as lack of dataset diversity and high energy consumption due to the extensive use of IoT devices. To address these challenges, we developed the Garbage Bin Status (GBS) dataset, which includes 16,771 images. Among them, 8408 images were generated using the Stable Diffusion model, depicting garbage bins under diverse weather and lighting scenarios. This enriched dataset enhances the generalization of garbage bin overflow detection models across various environmental conditions. We also created an energy-efficient model called HERD-YOLO based on Spiking Neural Networks. HERD-YOLO reduces energy consumption by 89.2% compared to artificial neural networks and outperforms the state-of-the-art EMS-YOLO in both energy efficiency and detection performance. This makes HERD-YOLO a promising solution for sustainable and efficient urban waste management, contributing to a better urban environment.

Bio-chelation for sustainable heavy metal remediation in municipal solid waste compost: A critical review of chelation technologies.

Municipal solid waste (MSW) compost is a promising solution for sustainable urban waste management, widely used as a soil amendment and for carbon sequestration. However, heavy metals in MSW compost pose risks to ecosystems, food safety and human health. This review critically examines three decades of research (1994-2024) on heavy metal contamination in MSW compost and household hazardous waste (HHW), identifying gaps in managing these pollutants, particularly regarding hazardous waste co-disposal. It evaluates existing remediation strategies for heavy metal removal, with a focus on chemical-assisted leaching using chelating agents. Key treatment parameters-such as chelating agent concentration, pH, contact time, liquid/solid ratio, temperature and flow rate-are analysed in both batch and continuous modes. The study advocates for biodegradable chelating agents as an effective approach to enhancing MSW compost quality, with applications in landfill reclamation and agriculture. Emphasizing the need for eco-friendly heavy metal mitigation, the review underscores the importance of safe urban composting practices. The findings contribute to the circular economy and Sustainable Development Goals by promoting sustainable and safe MSW compost applications, fostering environmental protection and public health and guiding research and industry toward scalable, marketable remediation solutions.

Municipal Solid Waste Management Using Machine Learning: A Case Study in Sheger City, Koye Sub-city, Ethiopia

Municipal Solid Waste Management is an increasingly critical challenge in urban areas, intensified by rapid urbanization, population growth, and evolving consumption patterns. This study investigates the application of machine learning techniques to predict municipal solid waste generation in Sheger City, Koye Sub-city, Ethiopia, using data from 2009 to 2023. Three machine learning models, ARIMA, RF, and LSTM, were employed to forecast waste generation trends for the period 2024–2028, considering various socio-economic and demographic factors. Among the models, LSTM demonstrated the highest accuracy, with MSE of 1.62 × 10⁸ tonnes, MAE of 9,500 tonnes, and R² of 0.93. These results outperformed ARIMA (MSE = 3.84 × 10⁸ tonnes², MAE = 15,200 tonnes, R² = 0.85) and RF (MSE = 2.91 × 10⁸ tonnes², MAE = 12,800 tonnes, R² = 0.89). The forecasts predict an 8.5% increase in total waste generation, from 3,852,150 tonnes in 2023 to 4,177,500 tonnes by 2028. Notable growth is expected in high-volume waste streams, including food waste (13.5% increase) and plastic waste (8.9% increase). These findings highlight the urgent need for enhanced waste management strategies, including expanded recycling programs and policy interventions. This study provides a robust framework for leveraging machine learning models to guide waste management decisions, contributing to more sustainable urban waste management practices in rapidly growing cities.

How Vegetation Structure Shapes the Soundscape: Acoustic Community Partitioning and Its Implications for Urban Forestry Management

Urban green spaces are critical yet understudied areas where anthropogenic and biological sounds interact. This study investigates how vegetation structure mediates the acoustic partitioning of urban soundscapes and informs sustainable forestry management. Through the principal component analysis (PCA) of 1–11 kHz frequency bands, we identified anthropogenic sounds (1–2 kHz) and biological sounds (2–11 kHz). Within bio-acoustic communities, PCA further revealed three positively correlated sub-clusters (2–4 kHz, 5–6 kHz, and 6–11 kHz), suggesting cooperative niche partitioning among avian, amphibian, and insect vocalizations. Linear mixed models highlighted vegetation’s dual role: mature tree stands (explaining 19.9% variance) and complex vertical structures (leaf-height diversity: 12.2%) significantly enhanced biological soundscapes (R2m = 0.43) while suppressing anthropogenic noise through canopy stratification (32.3% variance explained). Based on our findings, we suggest that an acoustic data-driven framework—comprising (1) the preservation of mature stands with multi-layered canopies to enhance bioacoustic resilience, (2) strategic planting of mid-story vegetation to disrupt low-frequency noise propagation, and (3) real-time soundscape monitoring to balance biophony and anthropophony allocation—can contribute to promoting sustainable urban forestry management.

A Community-Driven Approach to Urban Waste Management: Insights from the Kenanga Waste Bank, Bogor, Indonesia

Urban waste management remains a pressing challenge in Indonesia, with Bogor City being among the highest waste-producing areas. Community-based waste banks offer a promising solution by promoting waste reduction, recycling, and economic incentives. This study examines the effectiveness of the Kenanga Waste Bank, a leading community initiative in RW 1 (Rukun Warga, or neighborhood unit), Babakan Subdistrict, Bogor City. Using a mixed-methods approach, the research combines surveys of 35 waste bank members, in-depth interviews, and observations to assess managerial performance, community participation, and socio-environmental benefits. Data analysis employed Spearman rank correlation and chi-square tests to explore key relationships between participation levels, governance structures, and financial sustainability. The findings reveal strong operational and institutional management but highlight gaps in waste-sorting enforcement and participation in decision-making. While economic and environmental benefits are recognized, financial sustainability remains a concern due to fluctuations in the recyclable market. Strengthening participatory governance, diversifying revenue sources, and enforcing waste-sorting practices are critical for long-term success. This study offers practical recommendations for policymakers, waste bank operators, and community leaders to enhance participation, improve governance, and integrate waste banks into formal waste management systems. By addressing these key challenges, waste banks can serve as scalable models for sustainable urban waste management in Indonesia.

A novel method for urban pavement ride quality monitoring based on smartphone sensing technology and generalised extreme value theory

ABSTRACT Modern smartphones equipped with various sensors provide a cost-effective solution for urban pavement ride quality monitoring. In this field, road-induced vehicle vibrations collected by smartphone accelerometer are quantified following ISO 2631-1 as occupant-perceived driving comfort intensity, serving as a guideline for evaluating ride quality. However, existing methods often ignore the heterogeneous effects of vehicle suspensions and speeds on driving comfort intensity, challenging the objectivity of ride quality monitoring results. Therefore, this study advocates a multi-vehicle collaborative monitoring scheme and proposes a tailored two-stage method. In the first stage, multi-vehicle vibrations along the same path are collected via smartphone and transformed into multi-source driving comfort intensity (MSDCI) information. The second stage introduces a probability fusion method based on Generalised Extreme Value (GEV) theory to model MSDCI information as an uncertain event, further categorised into six levels of driving uncomfortable events to jointly characterise ride quality and develop a warning metric. The method was validated through a real-world case, showing that: (1) the GEV-based method is robust in fusing MSDCI information; (2) pavement locations with severe ride quality problems can be identified by the warning metric. The proposed method contributes to sustainable urban pavement management and will be promoted by integrating crowdsourcing technology .

A bi-objective data-driven chance-constrained optimization for sustainable urban medical waste management

A bi-objective data-driven chance-constrained optimization for sustainable urban medical waste management

A Comprehensive Big Data Governance Framework for Smart Cities: Leveraging IoT, Scalable Analytics, and Participatory Models for Sustainable Urban Management

Smart cities represent a transformative approach to urban management, leveraging advanced technologies and big data to address challenges such as overcrowded transportation systems, inefficient resource allocation, and environmental degradation. However, the vast and dynamic influx of data generated by smart city infrastructures demands robust governance frameworks to ensure efficiency, security, and equity. This paper examines the critical role of big data governance in enabling sustainable smart city ecosystems, focusing on policy frameworks, data privacy safeguards, and participatory governance models. By analysing existing frameworks and identifying gaps, it proposes an integrated governance model that addresses the ethical, legal, and technical dimensions of managing heterogeneous data sources. Furthermore, the paper highlights technical advancements, such as distributed storage systems and advanced analytics, and evaluates their integration within holistic governance structures. The findings underscore the necessity of scalable and secure solutions for data storage, processing, and sharing, ultimately advancing the transparency and sustainability of smart city initiatives.

Methods for the Estimation of Bird Diversity in Urban Areas

Urbanization has significantly altered natural habitats, leading to shifts in bird diversity. Accurately estimating bird diversity in urban areas is crucial for conservation efforts, urban planning and ecological research. Various methodologies, including point counts, transect surveys, mist netting, acoustic monitoring and citizen science programmes, have been developed to assess avian diversity. Each method has its own set of advantages, limitations and applicability depending on habitat type, observer expertise and environmental conditions. This review synthesizes findings from several sources, offering insights into the best practices for bird diversity estimation in urban landscapes. Understanding these methods is essential for policymakers, conservationists and researchers working towards sustainable urban biodiversity management. This review critically examines these techniques, highlighting their advantages, limitations and applicability in diverse urban settings with a particular focus on their use in India. Additionally, the reliability and effectiveness of each method is discussed. The integration of multiple methods is recommended to improve data accuracy and to ensure a comprehensive assessment of avian biodiversity.

Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine

Urban areas are experiencing rapid transformations, driven by population growth, economic development, and policy changes. Understanding and monitoring these dynamic changes is crucial for sustainable urban planning and management. This study leverages machine learning and Google Earth Engine to investigate urban dynamics and its interactions with biophysical conditions in the Kaduna River Basin (KRB), Nigeria. This study utilized a dataset of 192 points, initially extracted from Google Earth Engine, to analyze urban transitions between 1987 and 2020, incorporating biophysical and environmental variables such as population density, precipitation, and surface temperature. The dataset was processed in R using the CARET package, with missing data imputed via K-nearest neighbors (KNN), categorical variables transformed using One-Hot Encoding, and numerical variables rescaled for consistency. A tenfold cross-validation approach was used to train and validate machine learning models, including random forest, support vector machine, KNN, and multivariate adaptive regression splines, ensuring optimal model performance. Model evaluation metrics such as overall accuracy, kappa, F1 score, and area under the curve confirmed the reliability of the models in identifying the biophysical factors influencing urban changes. The findings revealed overall accuracy of 0.80, 0.73, 0.71, and 0.72 and kappa statistics of 0.60, 0.46, 0.42, and 0.45 for random forest (RF), multivariate adaptive regression splines, support vector machine, and KNN, respectively, with RF emerging as the most accurate model (80%) for predicting urban change patterns in KRB. Land cover changes reveal rapid urban expansion (154.81%), declining water bodies (− 95.79%), and vegetation growth (174%). Machine learning models identify population density and water stress index as key urban change drivers, with climate factors like temperature and precipitation playing crucial roles. The results of this study offer valuable insights into the processes driving urban transformation and present a robust methodology for monitoring and predicting future urban development. This study aids in the creation of strategies for sustainable urban growth and the mitigation of adverse environmental impacts.

Scaling of urban environmental performance in Chinese cities

Modeling how green performance attributes scale over cities of different size reveals how an urban system performs environmentally as a whole, establishes population-adjusted limits for expected urban management targets, identifies cities doing better or worse than expected for their size, and is a starting point for systematically explaining such variation from the mean. This seems a useful contribution to devising strategies for sustainable urban development and environmental management. However, existing studies have focused solely on the scaling of limited pollutants, hindering a comprehensive understanding of urban environmental performance scaling. Our study investigates scaling patterns of urban environmental attributes covering the full cycle of generation-treatment-outcomes on environmental quality of key types of urban pollution and the economic-technical support capacity of cities. We examine 28 environmental performance indicators in seven categories for Chinese cities in 2020. Our findings reveal that 27 indicators show good alignment with urban scaling theory. Larger cities tend to perform better than smaller ones, exhibiting lower per capita pollution generation and carbon emissions, higher environmental investment, stronger technological innovation capabilities, and more efficient waste treatment and recycling. We also find systematic geographical effects, with cities in northeastern and Midwestern China underperforming compared to the national average for their size. Gross domestic product per capita has the most far-reaching impact on variation in population-adjusted urban environmental performance. The study reveals regularities in the relationships between city size and environmental performance, which will improve the sophistication of differentiated pollution control strategies aimed at mitigating human-environment conflicts arising from rapid urbanization.

A “Foundation-Function-Structure” Framework for Multiple Scenario Assessment of Land Change-Induced Dynamics in Regional Ecosystem Quality

Understanding the changes in ecosystem quality caused by land use changes is critical for sustainable urban development and environmental management. This study investigates the spatial-temporal evolution of ecosystem quality in Wuhan from 2000 to 2020 and forecasts future trends under multiple land use scenarios for 2030. Using a “foundation-function-structure” assessment framework, we integrate system dynamics (SD), the Patch-generating Land Use Simulation (PLUS) model, and a neural network-based ecosystem quality inversion model to analyze land use transitions and their ecological impacts. The results indicate that rapid urban expansion has significantly contributed to the decline of cropland and forest areas, while impervious surfaces have increased, leading to notable ecological degradation. Simulations for 2030 under three scenarios—ecological protection, natural development, and economic priority—demonstrate that the ecological protection scenario yields the highest ecosystem quality, preserving landscape connectivity and mitigating degradation risks. In contrast, the economic priority scenario results in extensive urban expansion, exacerbating ecological stress. Under the ecological protection scenario from 2020 to 2023, the decline in ecosystem quality was primarily due to the expansion of urban fringes and the erosion of forest and grassland areas. The increase in ecosystem quality was mainly attributed to the transformation of early urban edge conflict zones into stable urban edge interior areas and the integration of fragmented ecological land patches. These findings highlight the need for strategic land use planning to balance economic growth and environmental conservation. This study provides a robust methodological framework for assessing and predicting ecosystem quality changes, offering valuable insights for policymakers and urban planners striving for sustainable development.

The last urban frontier—assessing hotspots of urban change associated with LCLUC in Africa

Urbanization is one of the leading drivers of Land Cover Land Use Change (LCLUC) globally, and African countries are at the forefront of urban expansion trends, specifically in small and medium sized cities. Multiresolution spatial datasets can be used to guide sustainable urban management and assess progress towards the U.N. Sustainable Development Goals (SDGs), specifically SDG 11.3.1 indicator (the relationship between land consumption rate and population growth rate) to track urban change. We present a two-tiered land imaging approach identifying urban change hotspots in three African countries between 2016 and 2020 and characterize urban expansion in three secondary cities that have an SDG 11.3.1 indicator ratio greater that two (Mekelle, Ethiopia; Polokwane, South Africa, Benin City, Nigeria). This ratio indicates that land consumption outpaces population growth where patterns of urban expansion include leapfrog development, infill, and corridors revealing a dynamic urban expansion that outpaces administrative boundaries. We propose a “pixels to people” approach that defines not only urban form but includes urban function in secondary cities at multiple spatial scales where fine resolution depictions and local engagement create more robust, comprehensive datasets for urban planning.

Optimization method of collaborative ecological water supplement and water purification plants strategies for improving water quality of urban river network.

Optimization method of collaborative ecological water supplement and water purification plants strategies for improving water quality of urban river network.

Spatiotemporal Dynamics of PM2.5-Related Premature Deaths and the Role of Greening Improvement in Sustainable Urban Health Governance

Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction in PM2.5 concentrations in recent years, the health burden caused by PM2.5 pollution has not decreased as expected. Therefore, a comprehensive analysis of the health burden caused by PM2.5 is necessary for more effective air quality management. This study makes an innovative contribution by integrating the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI), providing a comprehensive framework to assess the health impacts of green space coverage, promoting healthy urban environments and sustainable development. Using Nanjing, China, as a case study, we constructed a health impact assessment system based on PM2.5 concentrations and quantitatively analyzed the spatiotemporal evolution of premature deaths caused by PM2.5 from 2000 to 2020. Using Multiscale Geographically Weighted Regression (MGWR), we explored the impact of greening improvement on premature deaths attributed to PM2.5 and proposed relevant sustainable governance strategies. The results showed that (1) premature deaths caused by PM2.5 in Nanjing could be divided into two stages: 2000–2015 and 2015–2020. During the second stage, deaths due to respiratory and cardiovascular diseases decreased by 3105 and 1714, respectively. (2) The spatial variation process was slow, with the overall evolution direction predominantly from the southeast to northwest, and the spatial distribution center gradually shifted southward. On a global scale, the Moran’s I index increased from 0.247251 and 0.240792 in 2000 to 0.472201 and 0.468193 in 2020. The hotspot analysis revealed that high–high correlations slowly gathered toward central Nanjing, while the proportion of cold spots increased. (3) The MGWR results indicated a significant negative correlation between changes in green spaces and PM2.5-related premature deaths, especially in densely vegetated areas. This study comprehensively considered the spatiotemporal changes in PM2.5-related premature deaths and examined the health benefits of green space improvement, providing valuable references for promoting healthy and sustainable urban environmental governance and air quality management.

Role of Plants and Urban Soils in Carbon Stock: Status, Modulators, and Sustainable Management Practices.

Urban soils are vital components of urban ecosystems, significantly influenced by anthropogenic activities and environmental factors. Despite misconceptions about their quality, urban soils play a pivotal role in carbon (C) cycling and storage, impacting global emissions and sequestration. However, challenges such as soil contamination, land use changes, and urban expansion pose significant threats to soil quality and C storage capacity. Over the last two decades, there has been an increasing interest in the C storage potential of soils as part of climate change mitigation strategies. In this review, a bibliometric analysis covering the last twenty years (2004-2024) was performed to offer insights into global research trends, mainly in urban soils of the Mediterranean region. This paper also identifies research gaps and proposes essential solutions for mitigating the negative impacts of urbanization on soil biodiversity and functions. Key modulators, including plants, microbes, and soil features, are highlighted for their role in C dynamics, emphasizing the importance of effective soil and vegetation management to enhance C sequestration and ecosystem services. Strategies such as reintroducing nature into urban areas and applying organic amendments are promising in improving soil quality and microbial diversity. Further research and awareness are essential to maximize the effectiveness of these strategies, ensuring sustainable urban soil management and climate resilience.

A systematic review of multi-scale digital modelling in sustainable urban design and management

A systematic review of multi-scale digital modelling in sustainable urban design and management

Multi-scale urban ecosystem service changes and their impact mechanisms on human well-being.

Multi-scale urban ecosystem service changes and their impact mechanisms on human well-being.

Urban forestry for sustainable urban management: A study from Hetauda Sub-metropolitan City, Nepal

Developing sustainable urban areas in such a way that every citizens can enjoy a high quality of life and environment is one of the pressing environmental challenges we face today. This study was conducted to identify the major urban tree species and people’s perceptions towards urban forestry for sustainable urban development in Hetauda Sub-metropolitan City, Nepal. Mixed method of field assessment and questionnaire survey with urban households (N=125) was used to collect information regarding urban forestry by applying simple random sampling. Data were analyzed using descriptive and Likert scale rating through SPSS. The results showed that 46 different tree species were recorded in the study area with most frequent being Saraca asoca, Mangifera indica and Ficus religiosa. Based on local people’s preference Sparaco asoca, Nyctanthes arbortristis, and Thusa spp. are top 3 species for urban plantation. People opined that management of urban forest is not so good and they responded that poor management of roadside trees is due to lack of people participation with weighted mean value of 2.10. More than 90% of the respondents agreed that urban trees are essential for sustainable urban planning. Furthermore, rapid urbanization, lack of public awareness, and limited institutional support are major challenges while community engagement, ecological resilience, and government initiatives and policies are major opportunities for urban forestry development in the city. Therefore, appropriate selection of tree species, community participation and awareness regarding urban forestry should be emphasized. Furthermore, urban forestry program requires its prioritization and integration in the local and national policies.

Public Services With Information Technologies in the Strategic Digital City Context: Curitiba, Brazil

Objectives: The research objective is to analyze the public services with information technology in the strategic digital city context. Theoretical Framework: Concepts based on the themes: strategic digital city; public services with information technology; and conceptual relationships. Method: The research methodology included a case study in the city of Curitiba, considering the research protocol with 1 construct and 3 variables. Results and Discussion: The results show that the city of Curitiba has 25 services with information technology, in which 20% were associated with thematic construction, 16% with thematic health, 12% with thematic municipal and urban services, 8% with thematic mobility and transit, and 4% with thematic trade, communication, education, sports, security and work. The conclusion highlights that a strategic digital city project, as an innovative urban management model, can assist in the planning of strategies, the design of public services and the integration of information technology resources, considering the participation of citizens, which corroborates for more adequate and sustainable urban management in cities. Research Implications: Contemporary cities, in addition to being considered physical spaces, require digital infrastructure for functioning, through the use of information technology resources, considering the social and environmental approaches. Originality/Value: The concept and model of strategic digital city projects are original and of adequate value to the citizens quality of life.