City Scale Research Articles

Research on Digital Greening Management of Smart Gardens in Chenggong District, Kunming City

With the rapid urbanization in China, city scales have expanded, bringing numerous management challenges. The continuous expansion of urban green spaces highlights the difficulty of adapting traditional urban garden management to complex conditions. The solution lies in using AI and big data for smart city management, specifically termed 'Smart Gardens'. This system integrates modern information technology into daily urban garden management. Through platform management systems, it enables comprehensive planning, construction, maintenance, supervision, and service of urban green spaces. This ensures real-time data updates, visualized management, and dynamic services. This digital transformation is essential. This paper analyzes the current issues and future development strategies of digitalized garden management in Chenggong District, Kunming. Through literature review, field research, and data collection, it aims to enhance the green space management in Chenggong District and other regions.

Biomimetic Strategies for Sustainable Resilient Cities: Review across Scales and City Systems.

Biomimicry applications in different domains, from material science to technology, have proven to be promising in inspiring innovative solutions for present-day challenges. However, biomimetic applications in the built environment face several barriers including the absence of biological knowledge of architects and planners and the lack of an adequate common means to transfer biomimetic concepts into strategies applicable in the urban context. This review aims to create a multidimensional relational database of biomimetic strategies from successful precedent case studies in the built environment across different city systems and on different application scales. To achieve this, a thorough systematic search of the literature was implemented to map relevant biomimetic case studies, which are analyzed to extract biomimetic strategies that proved to be applicable and successful in an urban context. These strategies are then classified and documented in a relational database. This will provide a guide for architects and planners on how to transfer biomimetic strategies to strategies applicable in the urban context, thus bridging the gap of their lack of biological knowledge. The resulting matrix of strategies provides potential strategies across most of the different city systems and scales with few exceptions. This gap will be covered in a future work, currently in progress, to expand the database to include all city systems and scales.

Predicting the number of road accidents involving personal mobility aids on the example of Krasnodar krai

Introduction. Nowadays personal mobility aids (SIMs) are in active demand. According to the data of the mass media, the considered means of mobility are particularly popular in southern cities, such as the cities of Krasnoyarsk region, in which 190 accidents involving SIMs occurred during the period of 2018–2023. In order to assess the changing situation in the field of accidents involving SIMs in the scale of southern cities, a model for predicting the number of accidents involving the considered means of transportation within the framework of this study using the example of Krasnodar Krai was developed.Methods and Materials. In order to predict the number of traffic accidents, mathematical modelling methods have been used as a research method, in particular the science of econometrics has been applied, which is quite often used to evaluate statistical indicators from a mathematical point of view.Results. The authors have developed a model for predicting the number of accidents involving SIM for Krasnodar region with a reliability of 67%.Conclusion. It was found that the increasing nature of change in the considered quantities can be described using a multiplicative model consisting of three components – seasonal, trend and random. The calculation of all components enables to determine the type of model (), which can calculate the number of accidents involving SIM for the subject (Krasnodar Krai) under consideration.

Towards an Urban Planning Scenario Model System—A Tool for Exploring Urban Uncertainty: A Case Study of Diaozhen, China

The ‘Urban Interaction’ project aims to develop an urban planning model system at the scale of towns or small cities consisting of three modules: growth forecast, land-use decision, and evaluation. This paper presents the framework of the model system to identify and discuss the assumptions and theoretical basis of the model system. The model system will be driven by scenario planning theory and sustainable urban development principles. It will export land-use planning based on selected urban development scenarios and urban planning theories. This paper takes Diao Town in Jinan as an example. Applying GIS spatial analysis and hierarchical analysis, this paper determines the suitability of land use and the weights of different influencing factors, combined with the land-use conflict identification model, for land-use decision-making. Finally, the assessment module verifies whether the planning scheme complies with laws and regulations to achieve an active, reactive response to uncertainty. The paper discusses the ‘uncertainty’ of urban planning and proposes a creative, flexible, and timely planning platform that allows planners and other participants to model and visualize their scenarios.

Realizing the potential for circularity in Glasgow through the socio-spatial dimension of urban systems

ABSTRACT With urbanization trends impacting on levels of greenhouse gas emissions, environmental degradation, resource consumption and associated waste; the quest for stronger mitigation and adaptation approaches has put more pressure on solutions to be viewed at the city scale. A plethora of concepts has emerged under the umbrella of sustainable cities, aiming to make cities greener, smarter, resilient, eco-friendly, decarbonized, and more recently, ‘Circular’. This research aims to contextualize the alignment of the circular economy to socio-spatial issues by leveraging the discussions on the implications of circularity in urban planning and design. Drawing on a combination of methods: literature review, stakeholder interviews and the exploration of Glasgow as a case study; a synthetic view of an urban circularity framework is aided by the visualization of four urban systems (building and property assets, social-productive, energy and mobility, and natural and ecological) and initiatives affecting them (policies, plans, projects) enabling a socio-spatial perspective to circularity. Applied to Glasgow, the maps showcased current practices and their potential for moving towards a circular city, promoting inclusive, collaborative planning and stakeholder engagement not only on the macro scale (city) but at the meso (neighbourhood) and micro (buildings/sites/citizens) scales. The framework presents the potential for advancing urban metabolism in a way that is visual and relatable to urban decision-making, and encourages a more holistic interpretation of circularity in the built environment.

Understanding the drivers of PM2.5 concentrations in Chinese cities: A comprehensive study of anthropogenic and environmental factors

Understanding the factors that drive PM2.5 concentrations in cities with varying population and land areas is crucial for promoting sustainable urban population health. This knowledge is particularly important for countries where air pollution is a significant challenge. Most existing studies have investigated either anthropogenic or environmental factors in isolation, often in limited geographic contexts; however, this study fills this knowledge gap. We employed a multimethodological approach, using both multiple linear regression models and geographically weighted regression (GWR), to assess the combined and individual effects of these factors across different cities in China. The variables considered were urban built-up area, land consumption rate (LCR), population size, population growth rate (PGR), longitude, and latitude. Compared with other studies, this study provides a more comprehensive understanding of PM2.5 drivers. The findings of this study showed that PGR and population size are key factors affecting PM2.5 concentrations in smaller cities. In addition, the extent of urban built-up areas exerts significant influence in medium and large cities. Latitude was found to be a positive predictor for PM2.5 concentrations across all city sizes. Interestingly, the northeast, south, and southwest regions demonstrated lower PM2.5 levels than the central, east, north, and northwest regions. The GWR model underscored the importance of considering spatial heterogeneity in policy interventions. However, this research is not without limitations. For instance, international pollution transfers were not considered. Despite the limitation, this study advances the existing literature by providing an understanding of how both anthropogenic and environmental factors, in conjunction with city scale, shape PM2.5 concentrations. This integrated approach offers invaluable insights for tailoring more effective air pollution management strategies across cities of different sizes and characteristics.

How to measure peripherality at the city scale. A model proposal

How to measure peripherality at the city scale. A model proposal

Allocation of carbon emission responsibility among Chinese cities guided by economic welfare gains: Case study based on multi-regional input-output analysis

Ensuring equitable allocation of carbon emission responsibilities at the city level is crucial for effectively mitigating climate change. However, traditional allocation methods like production-based accounting (PBA) and consumption-based accounting (CBA) encounter double-counting problems at this scale. This study for the first time addressed the ignored externality of carbon emissions among bilateral trading partners at city scale, achieving equitable carbon emission responsibility reallocations using the economic welfare gain-based (EWG) method. Examining 313 cities in China using a multi-regional input-output model, the highest carbon emission responsibilities are identified in Baotou at 400.7 Mt., followed by Yunnan (189.9 Mt) and Dalian (166.7 Mt). In this case, the EWG method reallocates producer and consumer responsibilities in bilateral trades. Among the 313 cities, emission responsibilities from 198 are situated between PBA and CBA, avoiding one-sided assignment of emissions based solely on either principle. A comparative analysis of allocation methods in China further indicates a predominance of production responsibility, mirroring a similar trend between EWG and PBA; a significant portion of producer responsibilities are identified within EWG. The results of this work may offer a fairer and economically sound basis for allocating emission responsibilities among cities, thereby facilitating more effective emission reduction strategies.

Analyzing multiscale associations and couplings between integrated development and eco-environmental systems: A case study of the central plains urban agglomeration, China

In the Anthropocene, urban agglomeration is increasingly recognized as a crucial facilitator in advancing urbanization to more developed stages, requiring comprehension of the multiscale associations and couplings supporting urban agglomeration integrated development and eco-environmental systems. This study introduces a novelty research framework for analyzing the intricate associations and couplings between the degree of integrated development and eco-environmental quality in the Central Plains Urban Agglomeration (CPUA), which operates simultaneously at the city, county, and landscape scales. The results suggest that the CPUA degree of integration is moderately low, with areas exhibiting a high degree of integration comprising <1% and predominantly located within the sphere of influence of core cities, such as Zhengzhou. Regions with high eco-environmental quality are located in regions with natural advantages in the northwestern, southwestern, and southeastern regions. It is worth noting that a generally inverse coupling relationship was observed between the urban agglomeration degree of integration and eco-environmental quality, exhibiting varied characteristics across different spatial scales and regional contexts. Stakeholders should focus more on environmental management and policy-making at the county administrative level. This study proposes a multiscale approach for revealing the complex associations and couplings between integrated development and eco-environmental systems, offering valuable insights into the sustainable development of urban agglomerations that encompass natural, social, and economic dimensions.

Optimized green infrastructure planning at the city scale based on an interpretable machine learning model and multi-objective optimization algorithm: A case study of central Beijing, China

Green infrastructure (GI) has developed as a sustainable approach to the mitigation of urban floods. While machine learning (ML) models have exhibited advantages in urban flood simulation, their direct application to support the quantitative planning of GI at the city scale remains a challenge. To address this, an interpretable ML model based on support vector machine (SVM) and the Shapley additive explanations (SHAP) approach is integrated with the non-dominated sorting genetic algorithm-II (NSGA-II) in this study. The model is applied to the case of central Beijing, China, and demonstrates a robust performance with a high area under curve (AUC) value of 0.94. The results of the urban flood susceptibility assessment identify the urban-rural transition zone in the study area as being under a greater flood threat. Via model interpretation with SHAP, the dominant roles of GI and grey infrastructure (GrI) in preventing flood are revealed and the non-linear complementarity between the two is demonstrated to be more significant in study units with a GI proportion of less than 0.45. Supported by the NSGA-II-based optimization framework, optimal GI plans under different total implementations of GI are achieved, among which a solution with a 3.21% increase in the total GI area is selected as that with the best investment efficiency. The pattern of GI implementation is suggested to be dispersed and small-scale by model. This study provides a tool with broad application prospects, effectively integrating GI implementation with urban planning. The findings of this study not only provide important references for the determination of the priority areas of new ecological space in Beijing, but also provide areas that share similar characteristics with new insight into GI planning and the management of urban floods.

Multiscale spatiotemporal characteristics and influencing factors of ecosystem service value of groundwater in China

Ecosystem services are fundamental to human survival on Earth, but studies on ecosystem service value of groundwater (ESV-G) are rare. The multiscale characteristics and influencing factors of ESV-G in China from 2000 to 2020 were analyzed in this study. The results showed that ESV-G decreased first and then increased, the average ESV-G was 130.30 thousand yuan/km2, and ESV-G tended to shift towards middle level (second to fourth class). The Hu Line was the dividing line between the first class (more than half area) and the others. The AI and FRAC values indicated that the patches of ESV-G were more concentrated, with simpler shapes that were more amenable to governance at the province scale. Hot spots and cold spots were mainly located in the eastern and western parts of Hu Line, respectively. The ESV-G of the cold spots per unit area at the province scale was higher than that at the city scale, which indicated that the province scale had the potential for higher ESV-G per unit area and cost advantage. Precipitation and temperature were the main factors affecting ESV-G; the influence of human activities on ESV-G increased on a larger scale as time went by. Combination of precipitation and Digital Elevation Model (DEM) had the greatest influence on ESV-G among the combinational influencing factors. The province scale was the optimal scale to manage ESV-G. Climate change had led to the expansion of hot and cold spots of ESV-G, northern and southern areas should combine existing policies to carry out differentiated governance. This study extended the scope of ecosystem service value studies from land surface to underground, providing a scientific basis for the management of groundwater ecosystem.

Rational Exergy Management Model based metrics for minimum carbon dioxide emissions and decarbonization in Glasgow

Addressing climate change is an urgent issue that requires effective and sustainable solutions at multiple scales from the building to the district and city scales. This study focuses on the avoidable carbon dioxide emissions responsibilities due to exergy mismatches between supply and demand at multiple scales based on the Rational Exergy Management Model. Nine key metrics for decarbonization are presented with analyses of three case studies in Glasgow, including a university campus area, and urban emissions scenarios. The existing campus district system involves 42 MW boiler capacity for heat supply and 3.35 MW electric power and heat supply with a combined heat and power system that is responsible for 80.97 kg carbon dioxide per operating hour at design conditions. The system may shift towards carbon neutrality if the combined heat and power system capacity increases to at least 27.3 MW electric power and the boiler is eventually banned. A potential expansion of the district network with solar prosumer buildings indicates that the optimum number of Exergy Stars as a new rating is four out of five when embodiments for solar prosumer buildings are considered. Urban emissions scenarios for Glasgow indicate about 20 MtCO2eq of urban consumption-based emissions in 2020 that are analyzed up to 2030 and 2050 under different decarbonization scenarios. Remaining urban emissions are linked to inefficiencies where exergy mismatches cause emissions responsibilities in the energy system. In comparison to the decarbonization index in the urban emissions scenarios for Glasgow, the increased exergy match at 0.87 is closest to its 2045-year value in the renewable energy based green-growth scenario. The results are useful for guiding other urban areas to embark on an effective approach for mitigation and bring society to a better balance with the planet. Districts and cities can utilize these metrics to upscale climate mitigation and minimize emissions more rapidly.

Analysis of the Impact of Urban Infrastructure on Urbanization Processes at Different Levels from a Spatiotemporal Perspective

A comprehensive understanding of the heterogeneity of urbanization development at different levels and its influencing factors is crucial for promoting global urbanization and advancing China’s new urbanization. Using indicators related to urbanization development, a multidimensional index system was constructed based on five dimensions: population, economy, space, society, and ecology. Employing methods such as the Mann–Kendall test, Sen’s trend analysis, multiple linear regression, and spatial autocorrelation analysis, the spatiotemporal evolution characteristics of urbanization from 2000 to 2019 were analyzed comprehensively at national, economic zone, provincial, and prefectural city scales. The results indicate the following. (1) From 2000 to 2019, urbanization levels at all levels showed an overall upward trend, with the national urbanization rate increasing most rapidly at 5.39%. (2) Trend analysis reveals rapid and significant growth trends in urbanization at the national and economic zone scales, while urban-level changes exhibit greater diversity and spatiotemporal heterogeneity. (3) Spatial distribution patterns show that urbanization levels in the eastern coastal economic zones are significantly higher than those in the northeastern economic zones, highlighting pronounced regional disparities in development and agglomeration effects in economically advanced regions and provinces. (4) Regression analysis demonstrates that spatial urbanization significantly influences urbanization development in China, with urban infrastructure playing a crucial role across different levels.

Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals

Air pollution is an integral part of climatic, environmental, and socioeconomic current affairs and a cross-cutting component of certain United Nations Sustainable Development Goals (SDGs). Hence, reliable information on air pollution and human exposure is a crucial element in policy recommendations and decisions. At the same time, Earth Observation is steadily gaining confidence as a data input in the calculation of various SDG indicators. The current paper focuses on the usability of modern satellite remote sensing in the context of SDGs relevant to air quality. We introduce the socioeconomic importance of air quality and discuss the current uptake of geospatial information. The latest developments in Earth Observation provide measurements of finer spatial, temporal, and radiometric resolution products with increased global coverage, long-term continuation, and coherence in measurements. Leveraging on the two latest operational satellite technologies available, namely the Sentinel-5P and the Geostationary Environment Monitoring Spectrometer (GEMS) missions, we demonstrate two potential operational applications for quantifying air pollution at city and regional scales. Based on the two examples and by discussing the near-future anticipated geospatial capabilities, we showcase and advocate that the potential of satellite remote sensing as a, complementary to ground station networks, source of air pollution information is gaining confidence. As such, it can be an invaluable tool for quantifying global air pollution and deriving robust population exposure estimates.

Analysis of anthropogenic CO2 emission uncertainty and influencing factors at city scale in Yangtze River Delta region: One of the world's largest emission hotspots

Cities occupy only 3% of the earth's land area, yet they contribute over 70% of anthropogenic CO2 emissions. Accurately quantifying the corresponding uncertainty of CO2 emissions at the city scale is the first step in simulating global/regional greenhouse gas concentration and implementing effective emission reduction policies. As the world's largest emitter, China has committed to reaching its CO2 emissions peak by 2030, and cities are facing substantial pressure to reduce CO2 emissions. The Yangtze River Delta (YRD) region is treated as the largest urban agglomeration in China and globally. Despite the availability of multiple emission inventories, comprehensive assessments of city-level CO2 emissions uncertainty within this region are still lacking. This study focused on the YRD region, compared six inventories and used nighttime light intensity, GDP, population, and satellite-based xCO2 concentrations as proxy data to identify potential sources of emission bias. The findings are as follows: (1) The city-level CO2 emissions in the YRD region ranged from tens of Mt to approximately 600 Mt. From 2010 to 2019, emissions in the region increased slowly. However, emission intensity (calculated by dividing the CO2 emissions by the Gross Domestic Product) showed a declining trend. The relatively low proportion of point source emissions in EDGAR inventory is attributed to its reliance on the point source CARMA, which overlooks smaller point sources. (2) The average uncertainties for low emission (0–50 Mt), medium emission (50–100 Mt) and high emission (>100 Mt) cities were 51.1%, 34.0%, and 45.0%, respectively. The absolute errors of emissions showed strong positive correlations with proxy data. There exists a logarithmic relationship between them and uncertainty, which can assist in estimating emission uncertainty in other cities in the future. (3) The ratios of biological CO2 flux to the ten-year average of CO2 emissions varied between-6.79% and −0.02%, which are much smaller than the uncertainty of anthropogenic emissions, the comparisons indicate that recent large biases in estimating anthropogenic CO2 emissions hinder the evaluation of carbon neutrality ability.

The Urban Deployment Model: A Toolset for the Simulation and Performance Characterization of Radiation Detector Deployments in Urban Environments.

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

Spatial differentiation characteristics of the Hemiptera insects in China.

The Hemiptera insects are the largest incomplete metamorphosis insect group in Insecta and play a vital role in ecosystems and biodiversity. Previous studies on the spatial distribution of Hemiptera insects mainly focused on a specific region and insect, this study explored the spatial distribution characteristics of Hemiptera insects in China (national scale), and further clarified the dominant factors affecting their spatial distribution. We used spatial autocorrelation analysis, hot spot analysis, and standard ellipse to investigate the spatial distribution characteristics of Hemiptera insects in China. Furthermore, we used geographic detectors to identify the main factors affecting their spatial distribution under China's six agricultural natural divisions and explore the influencing mechanism of dominant factors. The results show that: (i) The spatial differentiation characteristics of Hemiptera insects in China are significant, and their distribution has obvious spatial agglomeration. The Hu Huanyong Line is an important dividing line for the spatial distribution of Hemiptera insects in China. From the city scale, the HH type (high-high cluster) is mainly distributed on both sides of the Hu Huanyong Line. (ii) The hot spots of Hemiptera insects are mainly distributed in southwest China, along the Qinling Mountains, the western side of the Wuyi Mountains, the Yinshan Mountains, the Liupanshan Mountains, the Xuefeng Mountains, the Nanling Mountains, and other mountainous areas. (iii) Under agricultural natural divisions, the influence of natural environmental factors on the spatial distribution of Hemiptera insects is obviously different. Temperature and precipitation are the dominant factors. Natural factors and socio-economic factors have formed a positive reinforcement interaction mode on the spatial distribution of Hemiptera insects. These can provide the decision-making basis for biodiversity conservation and efficient pest control.

Structural nonlinear seismic time-history response prediction of urban-scale reinforced concrete frames based on deep learning

Efficiently predicting the seismic response of urban building clusters is essential for preemptively identifying potential seismic hazards prior to an earthquake and optimizing resource allocation post-event. However, complete information of buildings at a city scale is generally un-accessible or non-existent. Existing methods struggle to reconcile low information demands, high computational accuracy, and computational efficiency. This paper proposes a fast prediction method for structural seismic time-history responses that combines deep learning methods with easy-getting structural parameters at an urban scale. An end-to-end network with adaptive multilevel fusion output is designed, which incorporates the autoencoder concept for predicting the structural seismic time-history responses based on ground motions records and five easy-getting structural parameters. The models are compared and optimized considering the training hyperparameters and network architecture, resulting in an optimized model with low complexity that provides valuable reference values for structural seismic response. Besides, the proposed model is applied to four actual buildings with different construction time, occupancy types, and floor sizes, demonstrating its good prediction performance and significant computational advantages comparing to the universally used MDOF method.

Evaluation of the Spatial Pattern of Fitness and Leisure Venues at the City Scale

Reasonable layout of fitness and leisure places is an important part of the implementation of the national fitness strategy. The analysis of the spatial pattern and influence mechanism of fitness and leisure places from the perspective of city has certain guiding significance for optimizing urban planning and construction and rationally allocating sports resources. This paper uses kernel density, buffer zone, inverse distance interpolation, Kriging interpolation, geographical detector and other geographical spatial analysis methods to analyze the spatial distribution pattern and distribution causes of five types of fitness and leisure places in counties (cities, districts) under the jurisdiction of Jinhua City. It is found that the overall spatial distribution characteristics of fitness and leisure places in Jinhua City are ' core-periphery ', and different forms of fitness and leisure places show different distribution characteristics. The spatial pattern of fitness and leisure places in Jinhua City has the characteristics of relying on traffic arteries and high-density population areas, and has a positive correlation with the spatial layout of housing prices. Jinhua fitness and leisure places generally have good traffic accessibility.

A system dynamics modelling assessment of water-energy-food resource demand futures at the city scale: Goma, Democratic Republic of Congo

Understanding future demands of water, energy, and food (WEF) resources is essential to achieve sustainable management of these resources. Based on a survey of 90 households, this study provides an analysis of household water, energy, and food security for the city of Goma, Democratic Republic of Congo, where there is a lack of any such assessment. Water supply is largely unimproved. Electricity supply is fairly reliable in some parts of the city, but not others. Most residents rely on charcoal burning for food preparation. Dietary Diversity Scores are low indicating stable, but low food security. Data collected were used to develop an integrated WEF system dynamics model that was applied to simulate household-level resource demand. To assess future resources demand, four scenarios developed by the Global Scenario Group were simulated. Findings reveal that the 'Great Transition' scenario exerts lower impact on resources demand, representing the most likely trajectory to achieve sustainable socio-economic development and management. However, achieving the conditions required for a ‘Great Transition’ could be challenging in Goma, which is characterised by low incomes, poor access to resources, and the threat of armed conflict. To achieve greater efficiency in resources utilisation and to prepare for the future, a number of recommendations are made, including energy supply diversification, altered agricultural practices to diversify diets, and expansion and improvement of water supply infrastructure. This work provides a basis for similar assessments in DRC and central Africa, highlighting the need for integrated resources management and assessment for regional opportunities toward sustainable development.