Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Enhanced Cooling Efficiency of Urban Trees on Hotter Summer Days in 70 Cities of China

Increasing the urban tree cover percentage(TCP) is widely recognized as an efficient way to mitigate the urban heat island effect. The cooling efficiency of urban trees can be either enhanced or attenuated on hotter days, depending on the physiological response of urban trees to rising ambient temperature. However, the response of urban trees' cooling efficiency to rising urban temperature remains poorly quantified for China's cities. In this study, we quantify the response of urban trees' cooling efficiency to rising urban temperature at noontime [~1330 LT(local time), LT=UTC+8] in 17summers(June, July, and August) from 2003–19 in 70 economically developed cities of China based on satellite observations. The results show that urban trees have stronger cooling efficiency with increasing temperature, suggesting additional cooling benefits provided by urban trees on hotter days. The enhanced cooling efficiency values of urban trees range from 0.002 to 0.055℃ %-1 per 1℃ increase in temperature across the selected cities, with larger values for the lowTCP-level cities. The response is also regulated by background temperature and precipitation, as the additional cooling benefit tends to be larger in warmer and wetter cities at the same TCP level. The positive response of urban trees' cooling efficiency to rising urban temperature is explained mainly by the stronger evapotranspiration of urban trees on hotter days.These results have important implications for alleviating urban heat risk by utilizing urban trees, particularly considering that extreme hot days are becoming more frequent in cities under global warming.

Factors Influencing the Spatial Variability of Air Temperature Urban Heat Island Intensity in Chinese Cities

Few studies have investigated the spatial patterns of the air temperature urban heat island(AUHI)and its controlling factors.In this study,the data generated by an urban climate model were used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers.A total of 355 urban clusters were used.We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying its formation.The results show that the midday AUHI is negatively correlated with climate wetness(humid:0.34 K;semi-humid:0.50 K;semi-arid:0.73 K).The annual mean midnight AUHI does not show discernible spatial patterns,but is generally stronger than the midday AUHI.The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid(0.32±0.09 K)and the semi-arid(0.36±0.11 K)climate zones.The release of anthropogenic heat from urban land is the dominant contributor to the midnight AUHI in all three climate zones.The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations.A spatial covariance analysis revealed that this vegetation influence is manifested mainly through its regulation of heat storage in rural land.

Do companies influenced by smart cities pay more attention to green governance?Mechanism and optimal spatial correlation

Smart cities are a way for China to construct an innovative and environmentally conscious nation.The paper examines the impact of smart cities on corporate green governance and provides a theoretical foundation for formulating and executing smart city policy in China.Based on panel data from Chinese A-share listed companies in Shanghai and Shenzhen from 2008 to 2020,this study constructs a multiperiod double-difference model to examine the influence of smart cities on corporate green governance.Additionally,it uses a spatial double-difference model to investigate the spatial spillover effect of smart cities on neighboring areas.The findings indicate that smart cities effectively enhance corporate green governance.Analyzing the influencing mechanisms reveals that resource allocation efficiency,technological innovation,management environmental awareness,and regional environmental enforcement efforts act as mediators.Furthermore,the study reveals that the impact of smart cities on promoting corporate green governance is more pronounced in regions with lower levels of marketization and resource-based cities.Moreover,the research explores the spatial spillover effects of smart cities,with an effective radius of approximately 350 km.The optimal spatial correlation zone for green governance of businesses in neighboring areas in relation to smart cities is within a range of 250-350 km.This is manifested by the significant promotion of green governance in neighboring area businesses facilitated by smart cities.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

A Study on the Symbol-Based Narrative in the International Communication of Cities

Cultural symbols,a manifestation of cities’cultural resources,are not only signs that frame concepts but also forms that express meanings.Exploring the international communication of cities from the perspective of symbols,this paper analyzes in depth how cities create their cultural symbols in the dynamic process of international communication in an era of symbol-based digital media,and how they develop their narratives and explain meanings through the dissemination of symbols when telling their stories to international audiences,thus enhancing the efficiency and effectiveness of their international communication efforts.

Defining Cities by Water: Addressing Capital Misallocation in a Race to Conserve Resources

As part of its efforts to promote a sustainable and high-quality development,China has pledged to reduce water consumption and create a water-efficient society.On the basis of identifying the institutional root causes of excessive capital allocation and excessive water consumption in China’s water-intensive industrial sectors,this study elaborates how the national water-efficient cities assessment contributes to optimized capital allocation.Our research shows that national water-efficient cities assessment has motivated local governments to compete for water efficiency.To conserve water,local governments regulated the entry and exit of water-intensive enterprises,discouraged excessive investments in water-intensive sectors,and phased out obsolete water-intensive capacities within their jurisdictions.This approach has resulted in mutually beneficial outcomes,including improved allocation of capital,enhanced water efficiency,and reduced emissions.This paper offers policy recommendations for establishing a water-efficient society throughout the 14^(th) Five-Year Plan(2021-2025)period by presenting empirical evidence on the policy effects of resource efficiency evaluation.

Green transformation paths of resource-based cities in China from the configuration perspective

Green transformation is an unavoidable choice for resource-based cities(RBCs)that face resource depletion and environmental pollution.Existing research has focused primarily on specific RBCs,making it challenging to apply green transformation strategies universally across cities.The fuzzy set qualitative comparative analysis(fsQCA)is a combination of qualitative and quantitative analyses that can handle multiple concurrent causality problems and determine how different conditions combine into configurations and generate an outcome.Thus,to address this gap,in this study,we established a research framework for green transformation and utilized the fsQCA to examine the configurations of 113 RBCs in China.By incorporating the element of time,this study explored the dynamic evolution of solutions in 2013,2016,and 2019.The main findings indicate that individual elements do not constitute the necessary conditions for improving the green transformation efficiency(GTE),and the systematic combination of multiple conditions is an effective path for realizing the improvement of the GTE in RBCs.Green transformation paths of RBCs exhibit the same destination through different paths.Additionally,the combination of system environment elements and system structure elements is both complementary and alternative.Differences in RBCs have led to various factor combinations and development paths,but there are some similarities in the key elements of the factor combinations at different stages.Economic environment,government support,and technological innovation are key factors that universally enhance the GTE in RBCs.These insights can assist city managers in formulating policies to drive green transformation and contribute to a better theoretical understanding of green transformation paths in RBCs.

Commuting Space of Primary and Secondary Schools in the Context of Child Friendly Cities:A Case Study of Bajiao Street of Shijingshan District

With the continuous promotion of the construction of child friendly cities,the school commuting space is an important component of the construction of child friendly roads.Based on the background of child friendly cities,the commuting space of 11 primary and secondary schools in Bajiao Street is analyzed through literature analysis and field research methods.Firstly,the relevant literature on school commuting space is sorted out,and the characteristics of school commuting space are summarized,including transportation,landscape,culture,leisure,and security.Secondly,the characteristics of commuting space of primary and secondary schools in Bajiao Street are analyzed from three aspects:in front of the school gate,path space,and node space.This paper aims to provide reference and guidance for the future construction of children’s walking school commuting and promote the construction of a child friendly city.

Impact of coupling coordination development of port and city environments on urban economic competitiveness:Evidence from the coastal port cities in eastern China

Promoting the coupling coordination development of port and its hinterland city environments is an important way to improve urban economic competitiveness.Based on relevant data of 13 coastal port cities in eastern China from 2000 to 2018,this study explores the coupling coordination development of port and city environments and its impact on urban economic competitiveness by constructing the coupling coordination degree model and the panel threshold model.The research results show that:(1)In terms of the coupling coordination development of port and city environments,most coastal ports and their hinterland cities are in a state of moderate or serious disorder.Overall,the degree of coupling coordination of port and city environments needs to be further improved;(2)The coupling coordination degree of port and city environments has a significant impact on urban economic competitiveness,and this effect gradually increases with the development of the ports and the urban economy.Among the variables that impact the urban economic competitiveness,fixed assets investment and foreign trade are significant factors that can enhance urban economic competitiveness.(3)At present,there is a“U-shaped”relationship between the coupling coordination degree of port-city environments and the urban economic competitiveness.This relationship lies on the right side of the inflection point of the“U-shaped”curve.Therefore,following the concept of assigning priority to ecological development,expanding fixed assets investment and actively developing foreign trade can further enhance the urban economic competitiveness.

Influence of Lightning Disaster on Cities and Lightning Defense Measures

Under the influence of special terrain and climatic conditions,lightning weather in Ulanqab City is more frequent from June to August,and lightning disaster has become one of the important tasks of disaster prevention and mitigation.In this paper,based on the characteristics of lightning weather in Ulanqab City,the impact of lightning disaster on the city was analyzed,and lightning protection measures,such as strengthening supervision of lightning protection safety,establishing a long-term lightning protection mechanism,doing a good job in lightning protection construction and detection in key places,and further improving public awareness of lightning protection,were put forward to reduce and avoid urban lightning disasters as much as possible and ensure urban safety.

Application Strategies of Spatial Refinement Design in Ancient Cities Supported by Digital Te-Zhaoyu Ancient City in Qixian County as an Example

Digital technology provides a method of quantitative investigation and data analysis for contemporary landscape spatial analysis,and related research is moving from image recognition to digital algorithmic analysis,providing a more scientific and macroscopic way of research.The key to refinement design is to refine the spatial design process and the spatial improvement strategy system.Taking the ancient city of Zhaoyu in Qixian County,Shanxi Province as an example,(1)based on obtaining the integrated data of the ancient city through the drone tilt photography,the style and landscape of the ancient city are modeled;(2)the point cloud data with spatial information is imported into the point cloud analysis platform and the data analysis is carried out from the overall macroscopic style of the ancient city to the refinement level,which results in the formation of a more intuitive landscape design scheme,thus improving the precision and practicability of the landscape design;(3)Based on spatial big data,it starts from the spatial aggregation level,spatial distribution characteristics and other evaluation index system to achieve the refinement analysis of the site.Digital technology and methods are used throughout the process to explore the refined design path.

Comparative Study of Evaluation Indicators for Learning Cities and the Implications

With the development of society,more and more cities are participating in the initiative to build learning cities.Constructing an evaluation indicator system for learning cities to monitor the progress and promote their growth has become increasingly important.This paper analyzes the preliminary framework of the UNESCO Global Learning City Index and R3L+Quality Framework.The comparison is made from the aspects of design philosophy,criteria of indicator,and the cycle of evaluation process.The findings suggest that the construction of an evaluation indicator system should be focused more on the diversity of learning city development,the construction of an evaluation process cycle,and the significance of building cooperative networks.

Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

Development of urban underground space in coastal cities in China:A review

The development of urban underground space is a critical way to alleviate the lack of urban resources,and improve environmental conditions as well as residents'quality of life,which has important practical significance.As the most densely populated and economically developed areas,coastal cities face many challenges in underground space development owing to their unique environmental geological conditions.This study examined the current situation of underground space development,academic research,technology application,evaluation methods,planning,and legislation in coastal cities in China.On this basis,it analyzed the future development trends,and presented reasonable suggestions for the critical scientific and technical problems faced by the development of underground space in coastal cities.The key suggestions are as follows:it is necessary to define and clarify the critical geological issues,distributions,and hazards that affect underground space development and major engineering construction in coastal cities;the land-sea integrated detection technology for sea crossing bridges and submarine tunnels needs to be studied;in evaluating the suitability of underground space in coastal cities,the land and sea area evaluation factors should be integrated;and the evaluation system of landsea integration needs to be established.

Spatiotemporal Differences and Classification Regulation of Urbanization Impact on Agricultural Development in Shrinking Areas:A Case Study of 15 Shrinking Cities in Three Provinces of Northeast China

Studying the impact of urbanization on agricultural development in shrinking areas is important for maintaining food security and promoted agricultural development in China.Based on the measurement results of the shrinking cities in the three provinces of Northeast China,this paper selects 15 shrinking cities as the research object,and constructs a multi-dimensional index system to explore the impact of the urbanization level of the shrinking areas on the agricultural development in the region since 2007–2019,analyzes the influencing factors and their differences by using the geographically-weighted regression model and Geodetector,and proposes a targeted regulation strategy.The results show that:1)overall,there is a negative correlation between the urbanization level and the agricultural development level in the contracted areas of the three northeastern provinces.The urbanization level in these areas has a certain negative impact on the overall level of agricultural development;2)regarding the time dimension,the impact of urbanization level on the agricultural development level in the contracted areas of the three northeastern provinces gradually increases over time;3)regarding the spatial pattern,the overall impact of shrinking urbanization levels in the three provinces of Northeast China on the agricultural development shows a significant distribution pattern of high in the east and low in the west;4)the total population and natural population growth rate at the end of the year were the main factors influencing a certain level of urbanization on agricultural development in the shrinking cities while population density and the urban fixed asset investment rate were the secondary factors;and 5)the main reasons why the level of agricultural development in different cities was affected by the level of urbanization were different.However,they can be categorized into areas of population loss and spatial construction,which can be further divided into area of population loss in the northeast,areas of negative population growth in the west,and areas of urban spatial change in the south.According to the causes of the impact,this paper adopted targeted regulation strategies and formulated relevant policies and solutions that cater to local conditions.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

In the smart city paradigm, the deployment of Internet of Things(IoT) services and solutions requires extensive communication and computingresources to place and process IoT applications in real time, which consumesa lot of energy and increases operational costs. Usually, IoT applications areplaced in the cloud to provide high-quality services and scalable resources.However, the existing cloud-based approach should consider the above constraintsto efficiently place and process IoT applications. In this paper, anefficient optimization approach for placing IoT applications in a multi-layerfog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into accountIoT application requirements, available resource capacities, and geographicallocations of servers, which would help optimize IoT application placementdecisions, considering multiple objectives such as data transmission, powerconsumption, and cost. Simulation experiments were conducted with variousIoT applications (e.g., augmented reality, infotainment, healthcare, andcompute-intensive) to simulate realistic scenarios. The results showed thatthe proposed approach outperformed the existing cloud-based approach interms of reducing data transmission by 64% and the associated processingand networking power consumption costs by up to 78%. Finally, a heuristicapproach was developed to validate and imitate the presented approach. Itshowed comparable outcomes to the proposed model, with the gap betweenthem reach to a maximum of 5.4% of the total power consumption.

An Optimized Offloaded Task Execution for Smart Cities Applications

Wireless nodes are one of the main components in different applications that are offered in a smart city.These wireless nodes are responsible to execute multiple tasks with different priority levels.As the wireless nodes have limited processing capacity,they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity.Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications.This execution delay is reduced by placing fog computing nodes near these application nodes.A fog node has limited processing capacity and is sometimes unable to execute all the requested tasks.In this work,an optimal task offloading scheme that comprises two algorithms is proposed for the fog nodes to optimally execute the time-sensitive offloaded tasks.The first algorithm describes the task processing criteria for local computation of tasks at the fog nodes and remote computation at the cloud server.The second algorithm allows fog nodes to optimally scrutinize the most sensitive tasks within their task capacity.The results show that the proposed task execution scheme significantly reduces the execution time and most of the time-sensitive tasks are executed.

Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.