Sustainable Development Of Cities Research Articles

A fuzzy synthetic evaluation approach to assess the risks associated with municipal waste management: Implications for sustainability

The rapid growth of industry, urban areas, and populations has substantially increased waste production worldwide. The inadequacy of robust waste management systems and limited resources poses significant challenges for many emerging economies, including Bangladesh, resulting in environmental contamination. Despite numerous academic studies on sustainable waste management, there is a noticeable gap in the literature concerning identifying, evaluating, and modeling critical risks associated with adopting sustainable municipal waste management systems, particularly in emerging economies like Bangladesh. This study, therefore, aims to fill this gap by identifying, categorizing, and assessing the crucial risks in implementing sustainable municipal waste management systems. Through an extensive review of existing literature and validation with expert feedback, this study employs a fuzzy synthetic evaluation method-based framework to prioritize the identified risks. The study calculates the probability of occurrence and degree of impact values to provide decision-makers with insights into the severity of different risk categories, aiding in case-by-case attention. Scores assigned on a 1 to 5 scale range from 2.59 to 4.14 for the various risk categories. Notably, ”Industrial challenges related” and ”Community and infrastructure requirements related” emerge as the most significant risk categories among those analyzed. This study contributes to the existing literature by employing fuzzy synthetic evaluation, a methodology rarely used to determine critical risks in municipal waste management. Furthermore, it is poised to offer valuable insights to industrialists and waste management authorities, empowering them to develop responsive strategies and contribute to the sustainable development of cities and urban communities.

Quantifying the Impact of Carbon Reduction Interventions and Incentive Mechanisms in Campus Buildings: A Case Study from a Chinese University

With the development of sustainable cities, densely populated higher education institutions increasingly emphasize the sustainability of campuses and their impact on the environment. However, there is a lack of means to quantify emission reduction measures. This study aims to propose an evaluation framework that can quantify energy conservation and emission reduction measures and incentive policies. To this end, this study adopts a mixed methods approach, using questionnaires to assess the effectiveness of management and communication interventions and the impact of incentives on residents’ willingness to participate in emission reduction efforts. The survey results show that although the support for the intervention measures is slightly higher than the average, specific measures such as adjusting dormitory lights-out time and providing sports equipment show superior emission reduction potential. Universities could reduce carbon emissions by about 560 tons per year without incentives and just using interventions. However, when incentives and interventions are combined, the university’s annual emissions reductions are expected to increase to 800 to 1045 tons. Research also highlights the importance of understanding the relationship between occupant behavior, energy consumption, and building carbon emissions. By quantifying the impact of carbon reduction measures and incentives on the daily behaviors of residents, universities can more effectively implement sustainable campus strategies.

Research on the Urban Renewal Planning from the Perspective of Suturing: A Case Study of the Ancient City Suturing Plan in Luoyang, Henan Province

With the acceleration of urbanization in China, historical and cultural districts in ancient cities have experienced rapid development under the promotion of various policies, gradually forming a homogenized development model for ancient cities. The rapid development has brought about environmental pollution and distortion in the protection of ancient city culture, making the contradiction between ancient city protection and renewal planning increasingly prominent. This study extends the theory and techniques of spatial suturing to the macro scale of historical districts in ancient cities, exploring the suturing effects and practical applications of ancient city design planning at the level of street and alley spaces and historical contexts. It aims to promote the sustainable development of historical and cultural ancient cities, and achieve the coordinated development of cultural heritage protection and socio-economic progress.

Real-time early warning and the prediction of air pollutants for sustainable development in smart cities

Air quality forecasting is vital for sustainable development in smart cities and is essential to protect public health through early warning against air pollutants. This article presents a novel ensemble deep learning method able to capture spatiotemporal features on short-term time intervals and extract high data abstraction levels. This method uses a hierarchical framework with a new activation function called HT-Tanh to improve the prediction of air pollutant emissions in real time. As such, a residual neural network (ResNet) is integrated with a convolutional neural network (CNN) to deeply extract the temporal and spatial features from pollutant and meteorological data. The model network encodes the fully connected layer to fine-tune the CNN output and decodes the temporal prediction relations based on gate recurrent unit (GRU) to obtain more accurate results. The reliability of the proposed method is verified through a case study from Shantou City, China. The model performance is compared with the recently developed and traditional deep learning models. Results reveal that the proposed method has good precision in the multiscale air quality predictions. Compared with CNN-GRU, convolutional long short-term memory, and CNN models, the forecasting results of the proposed method are significantly improved. The presented method in this article can support the early warning systems in multiple regions for air pollution modeling studies.

Considering urban tourism in strategic spatial planning

This paper addresses how strategic spatial planning is considering urban tourism, a phenomenon that has an increasing impact on the development of cities. A research approach interlinking urban tourism and spatial planning is conceptualised based on responsiveness theory and applied to a case study of ten medium-sized Central European cities. Descriptive statistical analysis of the presence of tourism in cities and policy analysis of strategic spatial planning documents enable a comparison analysis of the level of consideration of tourism alongside screening of proposed tourism-related measures. The paper reveals that strategic spatial planning documents are not responsive enough to tourism growth, and there is a lack of strategic measures steering towards sustainable tourism development in cities.

GEODESY IN SOLVING PROBLEMS OF SUSTAINABLE CITY DEVELOPMENT: ANALYSIS OF URBAN TERRITORY PLANNING

The article focuses on the creation and implementation of a geodetic planning system aimed at ensuring the sustainable development of urban areas. The main emphasis is on the use of open and accessible software, integration of full-fledged GIS and DBMS components. The process of determining the most suitable GIS platform for GSP included a detailed analysis of various GIS systems taking into account their functionality, which allowed to choose QGIS as the optimal solution due to its high functionality in the field of geodata management, support for various data formats, user interface and dynamic development. The structure of the GSP is based on two main platforms: the QGIS GIS system for cartographic data presentation and analysis, and the Microsoft Access DBMS for performing all data operations. Interaction between these platforms is provided through the eVis module built into QGIS, which allows for efficient planning of urban area surveys and land use management. The main tasks solved with the help of GSP are the planning of primary surveys to identify target plantations, detailed taxation of plantations with the creation of samples according to specified parameters, obtaining point maps of the distribution of various types of vegetation, planning sample surveys with specific geospatial conditions, as well as geo-information and tax analysis in in solving the problems of sustainable development of cities. The implementation of GSP provides significant advantages for planning the sustainable development of urban areas, allowing more efficient use of land resources, optimizing urban management processes and intensifying scientific research in this field. The development and integration of modern digital technologies in the field of urban planning play a key role in achieving the goals of sustainable development. 

Building recognition and classification using deep learning in civil engineering projects

The recognition and regulation of buildings are essential aspects of urban management to prevent illegal constructions and maintain public safety and resources. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Traditional machine learning methods for building recognition often suffer from low accuracy and weak generalization capabilities due to their reliance on manually designed features. Therefore, the study of automatic, accurate building identification method is very necessary. Based on this, Introducing advanced algorithms like Faster R-CNN and DRNet signifies a significant step towards automating accurate building identification. The utilization of Faster R-CNN as a basic training model combined with DRNet demonstrates promising results in accurately recognizing buildings. The experimental analysis highlights the potential of the proposed method, achieving an impressive 82.1% mean Average Precision (mAP) for landmark buildings. Accurate prediction of building coordinates further strengthens the effectiveness of the proposed approach. Comparative analysis showcases the superiority of the proposed model in recognizing buildings not only in normal images but also in complex environmental settings. The successful implementation of advanced algorithms in building recognition contributes to more efficient urban management and development. Continued research in automatic building identification methods is crucial for addressing challenges in urban planning and management, ensuring sustainable city development.

Potential imbalanced differences of grain production in the sustainable development of county cities—a case study of Jiangsu Province

IntroductionGrain production at the regional level is related to the strategic development orientation of the country as a whole, and maintaining the stability of grain production is of great importance in ensuring the sustainable development of the food system. Studying the potential imbalance differences resulting from the spatial and temporal development of grain production and its driving factors will help to reduce the contradiction between the rapid development of the urban economy and the inappropriate use of arable land resources, thus coordinating regional agricultural development and further clarifying the direction and focus of future food security construction work.MethodsThis study focuses on the potential imbalance differences based on spatial and temporal development of grain production in Jiangsu Province during the period 1990–2020, and explores the spatial and temporal patterns and driving mechanisms of grain production spatial autocorrelation model and spatial error model are applied to analysis the spatial and temporal characteristics of grain production and security in Jiangsu Province and their influencing factors.ResultsThe results are as follows: (1) The total amount of food in Jiangsu Province increased by 17.36% during the 30-year period, but the growth rate was lower than the national average. (2) Regional differences are significant, with a significant decline in grain production in southern Jiangsu and growth in northern and central Jiangsu. (3) The center of gravity for grain production has shifted to northern Jiangsu. (4) Grain production is significantly correlated with socio-economic factors such as per capita ownership, sown area, irrigated area, and cultivated area.DiscussionTo ensure the sustainability of food production in the future, attention should be paid to the risk of imbalance in regional food production, coordination of socio-economic factors, promotion of efficient agricultural production, improvement of the relevant policy system and optimization of the food production system.

Analyzing green and sustainable land use in China's coal cities: Insights from industrial transformation.

Driven by the goal of achieving sustainable development and carbon neutrality. Addressing environmental pollution and remediating land damage have become critical challenges in resource-based cities and regions with low land use efficiency. As a response, this study focuses on the 23 provinces where China's coal resource-based cities are situated. Utilizing data from 2014 to 2020, this research employs the SBM-Undesirable model, which considers undesirable outputs in efficiency calculations, and the Tobit regression test. It aims to explore the spatio-temporal variations in industrial transformation within resource-based cities and its impact on the efficiency of green space utilization. Furthermore, it analyzes the characteristics and the extent of the influence of factors such as industrial structure adjustments on urban land use efficiency, maximizing the output of land as a factor of production. The results show that: (1) Over the 7-year period studied, China consistently made nationwide adjustments to land area and land use structure to meet the needs of urban development (2) The regression test results show that the industrial transformation of resource-based cities can promote the improvement of green space utilization efficiency. The positive influence coefficient is 0.064 and is significant at a 1% level. (3) Environmental regulation, government expenditure, international trade, and green cover play a positive role in promoting green land use. The study provides valuable insights for policymakers and urban planners seeking to foster sustainable development in resource-based cities.

A Survey and Analysis of Intelligent Forecasting and Decision-Making Evaluation of Urban Growth using Artificial Intelligence

A dense concentration of human-made features such as residences, businesses, highways, bridges, and trains characterize urban areas, which are developed regions that encircle cities and are home to most of the population's non-agricultural labourers. Monitoring and modeling urban development have become critical for long-term urban planning and decision-making. Urban growth prediction models are crucial for understanding the causes and implications of urban land use patterns and predicting upcoming growth of city based on the current scenario, ensuring sustainable city development. The Cellular Automata (CA) approach has been used to simulate the urban growth in a hypothetical region, based on principles governing cell spatial interaction and parameters for exploring alternative urban shapes. However, CA faces numerous uncertainties and more research is requited to enhance its adaptability to urban environments. In recent times, Artificial Intelligence models like Machine Learning (ML) and Deep Learning (DL) are being utilized for urban growth prediction, enhancing decision-making and overcoming uncertainty. In order to help with future urban development planning, these models are essential for accurate management and control of urban expansion. This article presents a comprehensive review of ML and DL models for urban prediction. The first step is a quick review of the many urban prediction models developed by various academics using ML and DL models. The next step is to provide a new way for reliably projecting where cities will expand in the future by comparing current frameworks and determining their shortcomings.

The Status and Development of Informal Housing in Guangzhou Urban Villages

Amid rapid urbanization, the housing market must be more robust to provide housing for vulnerable groups. In China, informal housing, represented by urban villages, provides a large amount of living space and alleviates the emergence of urban poverty and slums. However, due to the social and environmental problems of urban villages, there are certain contradictions with the sustainable development of cities. This paper takes Guangzhou as an example, looking at the trajectory and underlying mechanisms of informal housing growth in urban villages utilizing field surveys and questionnaire interviews. The study extracts the current housing conditions and development concerns in urban villages and tries to unearth the economic and social issues behind urbanization. In addition, this study discusses the inclusive development strategy of informal housing in urban villages from three aspects: the improvement of affordable housing, the protection of migrant populations' rights and interests, and the renovation and renewal of urban, which offer insight into managing informal housing and supporting sustainable development in developing countries.

Research on the Causes and Prevention Measures of Urban Waterlogging

Flood disaster is one of the major natural disasters in China, due to the influence of monsoon climate, seasonal distribution of precipitation and unbalanced spatial distribution, urban flood disasters in China are prone to occur frequently, endangering the safety of life and property of urban residents, and also affecting the safe operation and sustainable development of cities. Therefore, the prevention and control of urban waterlogging is not only a major livelihood project, but also a major development project. In view of the repeated failures of urban waterlogging prevention and control in China, this paper analyzes the causes of urban waterlogging from four aspects: climate change, urban expansion, drainage system, and emergency rescue, analyzes the mechanism of urban waterlogging from the perspective of disaster system theory, and finally summarizes the coping strategies of urban waterlogging based on four aspects: stormwater management, institutional mechanism, engineering construction, and emergency management, in order to provide reference and reference for urban waterlogging control.

Assessing the Adoption of Blockchain Technology in Smart Sustainable Cities: Insights from Construction Practitioners in Malaysia

ABSTRACT In response to rapid urbanization and associated challenges, city planners worldwide are increasingly turning to the development of smart sustainable cities (SSC). Blockchain technology has emerged as a disruptive force with the potential to facilitate this transformation. While existing research has explored the concept, features, and applications of blockchain within SSC, limited attention has been given to the perspectives of construction practitioners regarding its adoption. This study seeks to address this gap by assessing the acceptance level of construction practitioners toward blockchain technology in SSC. Through the distribution of questionnaires to construction practitioners, comprising 153 responses, this research reveals a generally high level of acceptability toward blockchain technology in SSC, with notable exceptions in property or asset transactions and data management. Furthermore, significant disparities in acceptance were observed across different age groups and educational backgrounds, with older generations and those with lower levels of education demonstrating lower levels of acceptance. These findings provide valuable insights for city planners and relevant authorities, enabling them to design informed strategies and roadmaps to ensure the successful integration of blockchain technology in SSC by considering the acceptance levels of construction practitioners.

The essence of the concept of social inclusion of the city and its significance in ensuring the national security of the state

Ukraine, as a country, is in the process of constant development and transformation, facing a number of important social, economic and security challenges. One of these challenges is ensuring equal opportunities and accessing the social resources for all sections of society. In this context, the concept of social inclusion comes to the fore to become an important tool for promoting the sustainable development of cities and the entire country. Social inclusion involves the creation of conditions under which every person, regardless of their origin, gender, age, physical condition or other personal characteristics, can take an active part in all spheres of society. The implementation of this approach to the development of cities is especially relevant in modern Ukraine, where there are numerous challenges in the form of lack of access to infrastructure, medical care, housing and other services for certain social groups. The implementation of the concept of social inclusion is now widely recognized as important and not just good practice. The recent impact of the global pandemic, the coronavirus disease 2019 (COVID-19), and the Russian-Ukrainian war have further emphasized the need to implement the concept of social inclusion in the development of Ukrainian cities. Promoting the development of social inclusion in cities requires a deep understanding of its essence, the factors affecting it, and the mechanisms of public administration involved. The work determined that the theory and methodology of public management of the development of social inclusion in cities in the science of public administration is not sufficiently formed. The existing scientific works of foreign and domestic scientists testify to the existence of different approaches to public management of the development of social inclusion. The essence of social inclusion was highlighted as the creation of a fair and accessible urban environment where all people, regardless of their origin or characteristics, can actively participate in the social, economic and cultural aspects of urban life. Social inclusion emphasizes the need to overcome structural barriers, discrimination and inequality to promote inclusive cities and ensure the social security of the state.

Driving factors and key paths of greenhouse gas and air pollutants synergistic control using hybrid multiple-criteria decision-making

Humanity has consumed a large amount of energy and resources to maintain the rapid development of the economy and society, causing greenhouse gas and air pollutants to rise continuously, generating enormous pressures for the sustainable development of many cities. It is economical to control greenhouse gas and air pollutants from the synergy perspective. To identify the key driving factors involved in synergistic control, this paper uses the pressure-state-response (PSR) model to design a performance evaluation model of greenhouse gas and air pollutants synergistic control (GASC) utilizing pressure, state, and response dimensions. The performance evaluation factor system of GASC comprises three primary aspects and 18 criteria. The analytic hierarchy process (AHP) was used to determine the weight of each factor in the evaluation system. The technique for order preference by similarity to an ideal solution (TOPSIS) method was used to calculate the ranking of the synergistic control effects of the four representative provinces in China. We use Importance-performance analysis (IPA) to analyze the performance of driving factors of synergistic control in the province with the lowest ranking from 2016 to 2020. The research shows that in Northeast China, represented by Liaoning province, the government’s response should include changing the support strategy for the new energy consumer, introducing synergistic control standards and policies, and making flexible adjustments to the supply chain. The research provides a scientific basis for the performance evaluation of GASC and decision-making support for lean response strategies.

Resilience Assessment of Historical and Cultural Cities from the Perspective of Urban Complex Adaptive Systems

Due to the increasingly complex global climatic environment and the rapid development of China’s urban construction, China’s historical and cultural cities are experiencing an external impact as well as internal fragility. Representing the capacity of the urban system to address impact and pressure, resilience can effectively guarantee the sustainable development of historical and cultural cities. A scientific and reasonable resilience assessment system can guide the resilience construction of historical and cultural cities in an effort to effectively counter the impact and pressure they face. Therefore, it is necessary to research the resilience of historical and cultural cities. On the basis of the complex adaptive system (CAS), and by applying multiple assessment indicators, this paper established a resilience assessment system for China’s historical and cultural cities, comprising 38 indicators in six dimensions, to analyze the characteristics and the influencing mechanisms of the resilience of the historical and cultural cities and to reveal the inherent logic underlying their complex presentation. Using six historical and cultural cities in east China as an example, the study applied the assessment system to assess and analyze the different resilience levels of the cities. The comprehensive resilience of Changzhou City obtained the highest score at 0.64, indicating a higher degree of resilience; the scores of Yantai City, Huzhou City, and Nantong City were 0.59, 0.54, and 0.50, respectively, representing moderate degrees of resilience; the scores of Zhongshan City and Quzhou City were 0.44 and 0.40, respectively, exhibiting a lower degree of resilience. Moreover, the factors that result in an unbalanced development of urban resilience were explored from the perspectives of economy, system, and culture. The paper contains some significance in guiding the development of the resilience of historical and cultural cities.

Empowering Smart Cities with AI and RPA: Strategies for Intelligent Urban Management and Sustainable Development

This research explores the transformative potential of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in empowering smart cities to achieve intelligent urban management and sustainable development. Through a comprehensive analysis of literature, case studies, and qualitative research methods, the paper identifies key strategies for leveraging AI and RPA to address urban challenges and promote sustainable urban development. The integration of AI and RPA technologies enables data-driven decision-making processes, streamlines administrative workflows, and enhances service delivery in smart cities. Furthermore, AI and RPA contribute to promoting sustainable development goals by optimizing resource utilization, improving environmental management practices, and enhancing resilience to climate change. However, the widespread adoption of AI and RPA in smart cities faces challenges related to privacy, data security, and equity, which must be carefully addressed to ensure responsible and equitable deployment of these technologies. By adopting comprehensive strategies, fostering collaboration between stakeholders, and embracing a culture of innovation, cities can harness the full potential of AI and RPA to build smarter, more resilient, and sustainable urban environments for all residents. This research provides valuable insights for policymakers, urban planners, and technology providers seeking to leverage AI and RPA to address urban challenges and promote sustainable development in smart cities.

Assessing Spatial Heterogeneity in Urban Park Vitality for a Sustainable Built Environment: A Case Study of Changsha

In the realm of sustainable city development, evaluating the spatial vitality of urban green spaces (UGS) has become increasingly pivotal for assessing public space quality. This study delves into the spatial heterogeneity of park vitality across diverse urban landscapes at a city scale, addressing limitations inherent in conventional approaches to understanding the dynamics of park vitality. Leveraging geotagged check-in data from 65 parks in the study case of Changsha City, a quantitative analysis was undertaken to assess spatial vitality. The investigation incorporated data concerning internal and external factors influencing park vitality, employing the Multi-scale Geographically Weighted Regression (MGWR) model to dissect nuanced spatial heterogeneity. The research uncovers notable spatial discrepancies in factors influencing park vitality across diverse urban areas, emphasizing the reliance on adjacent residential communities and internal commercial amenities provision. These dependencies correspond with economic development differences among urban locales, revealing distinct geographic trends. This study has a novel perspective and methodology for investigating urban park vitality, providing significant insights for urban green space planning and management. It emphasizes the necessity of acknowledging spatial diversity in urban park planning and design by incorporating the distinct socio-economic characteristics of each urban zone, which is crucial for both urban planners and policymakers.

Integrating Green Infrastructure Distribution and Green Corridor Mapping with Proposed Green Trail Area and Wildlife-Human Conflict Using Remote Sensing-GIS Approach

Most people now prefer to live in cities, and the urban population has grown significantly. The decrement of urban green corridors causes an essential need for livability in highly populated areas. The needs of population health and sustainable city development are put under prolonged stress by the shortage of publicly accessible green infrastructure and its placement in inappropriate areas. The inventory of green corridor areas and the distribution of significant infrastructure are not fully understood, thus encouraging conflicts between residents and wildlife and increasing flood risk in their vicinity. Optical satellite images were required to (1) develop a green corridor (GC) map, (2) investigate the green infrastructure (GI) demand with the existing land use, and (3) propose new green trails (GT) in the Kuantan district using a geospatial approach. NDVI and site suitability analysis were carried out on Landsat OLI and Sentinel-2 MSI images, which were integrated with GIS tools to achieve all the objectives. A GC map has been developed, and five new proposed areas for GI development have been identified. Based on the results, 5 proposed green trails (3 long GTs, 2 short GTs) with a total length of 79.3 km are scattered in the Kuantan district at the most suitable site, identified using a geospatial approach. This study supports SDG 15: Life on Land, where green corridors reinforce biological connectivity, provide conducive space for high-populated areas, and minimize the negative impact of urbanization on the designed areas of GC, GI and GT in the developing city of Kuantan.

Assessing the spatial distributions of public electric vehicle charging stations with emphasis on equity considerations in King County, Washington

This study assessed the availability and accessibility of public electric vehicle (EV) charging stations at the census tract level in King County, Washington, USA. A gravity model was used to measure the accessibility of stations in each tract while considering the competition among tracts for charging infrastructure. Unlike prior studies, we evaluate current distribution from various equity viewpoints, clearly defining these perspectives and investigating their implications for charging distribution. The findings revealed significantly higher accessibility of public stations in areas with a lower share of single-family homes, a higher share of commercial areas, and proximity to major roadways, irrespective of population and EV adoption rates. Moreover, we found that the current distribution is consistent with utilitarian and capabilities-based theories of equity. Spatial tools such as Moran's I and spatial regression were used to analyze the spatial disparity of the proposed accessibility measure, showing the existence of a highly clustered spatial distribution. This study offers valuable insights into integrating equity considerations in assessing accessibility to transportation facilities, especially EV charging stations. Such an approach can facilitate the promotion of EV adoption across diverse groups, a critical factor in achieving sustainable city development goals by mitigating carbon emissions and enhancing mobility.