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In response to the international appeal of developing low-carbon economy and realizing carbon peaking and neutrality goals, China has been exploring the construction of the carbon emissions trading market for years. Without the guidance of advanced technology, there would not be a low-carbon economy in the first place. Based on panel data of 30 provinces and cities in China from 2005 to 2020, this paper takes Beijing—which is the first pilot city in China—as the only treated group and uses the Synthetic Control Method to study the impact of the pilot policy on low-carbon technology innovation. The results show that, firstly, the number of low-carbon technology patents in Beijing increases significantly after the implementation of the pilot policy, proving a positive influence on technology innovation. Secondly, the policy effect has a certain time lag and is sensitive to the shock from both domestic and foreign carbon market, but this effect is gradually stable over time. This paper confirms that technological innovation is the key means of promoting the development of the low-carbon economy and calls on various carbon trading markets to pay attention to the internal mechanism of promoting low-carbon technology innovation to stimulate the vitality of market entities. [ABSTRACT FROM AUTHOR]

In the knowledge economy era, innovation has become a key emphasis for urban competitions. This paper constructs a theoretical research framework that integrates the basic understandings, influencing factors and ensuing results of intercity innovative competition relations. On the basis of data from the general programs of the National Natural Science Foundation of China from 2005 to 2019, this paper constructs intercity innovative competition relations in China, analyses their spatial distribution and quantitative characteristics, and quantitatively investigates the impact of urban innovation capacity and multidimensional proximity (e.g., geographical proximity, institutional proximity and cognitive proximity) on intercity innovative competition relations through a negative binomial model. The study obtained the following findings: (1) In terms of the overall intercity innovative competition relations, the intensity of China's intercity innovative competition relations gradually increased from 2005 to 2019, with a spatial clustering towards cities with high administrative ranks (e.g., municipalities directly under the central government, sub-provincial cities and provincial capitals); Beijing is always at the centre of innovative competition relations, but its standing has slightly slipped in recent years. (2) From the perspective of disciplines, cities can become benchmarks in particular fields of innovative competitions by competing according to their disciplinary strengths; intercity innovative competition relations in China vary across various academic disciplines. (3) In terms of influencing factors, urban innovation capacity has significant positive effects on intercity innovative competition relations; geographical proximity, institutional proximity and cognitive proximity all have significant positive effects on innovative competition relations; and interactions occur between multidimensional proximities, including a complementary effect between geographical proximity and institutional proximity, a substitutive effect between cognitive proximity and geographical proximity, and a substitutive effect between cognitive proximity and institutional proximity. [ABSTRACT FROM AUTHOR]

Industrial heritage is regarded as an important stock of spatial resources in cities, which highlights its utilization value for urban regeneration in high-density urban areas. With the dramatic increase in the number of industrial heritage reuse projects, how to scientifically evaluate the satisfaction with their spatial reuse is a key part of the solution for the mutual balance between heritage preservation and urban renewal. This paper takes eight industrial heritage conversion and utilization projects in the high-density core area of Beijing's central city as examples; establishes an evaluation system for the satisfaction with the spatial reuse through six dimensions, namely, functional replacement, transportation accessibility, carrying capacity, public space, boundary form, and recognition of value; and uses the IPA method to evaluate the cases. This method is used to determine the degree of user satisfaction with the spatial reuse of industrial heritage in the core area of Beijing's central city and to summarize the advantages and problems of its reuse. The results of this study reveal a trend toward the "community-oriented" re-generation of industrial heritage in the core area of Beijing's central city, and this paper proposes recommendations for adaptive use to support high-quality urban regeneration work. [ABSTRACT FROM AUTHOR]

As development progresses over time and changes in the environment take place, carbon finance development has become a very interesting research topic for many scholars worldwide. This paper proposes a hybrid MCDM method that combines the distance solutions of the TOPSIS and VIKOR methods. The contribution of this paper combines TOPSIS and VIKOR, incorporating AISM models to obtain the development ranking of Chinese carbon finance pilot cities as related to the Chinese carbon finance development pilot provinces and cities. The conclusion notes that Guangdong and Beijing are the most preferred investment locations for carbon investors. In addition, technological innovation, financial sector development, and an active ETS market can effectively drive the level of local carbon finance. Additionally, the MCDM methodology in this paper provides new ideas in environmental and economic development assessment. [ABSTRACT FROM AUTHOR]

China is in the transition period of urbanization, and the imbalance of land use generated during the original rapid development of megacities still exists, resulting in the preservation of a large number of urban villages in the peripheral areas of cities. Unlike formal housing, these villages are heavily influenced by the confrontation and interweaving between top-down policies of local governments and bottom-up spontaneous construction by local villagers, resulting in a diverse and complex status quo. At the same time, self-built housing in urban villages has become a gathering place for a large number of low-income migrants due to its relatively low rental prices. In terms of the research of urban villages on the current situation of housing, the real needs of users and housing performance are mostly subjective judgments and conclusions. Therefore, the entry point of this paper is the leased space and the leasers, taking Laojuntang Village in Chaoyang District of Beijing as the research object and analyzing its current problems and the real needs of users. This paper adopts an integrated approach of fuzzy Kano model evaluation and building performance simulation. After analyzing the data and discussing the influencing factors, this paper identifies the specific problems and user needs that exist in leased spaces in Laojuntang Village: (1) The architectural performance of leased space is a Must-be Quality, which is the bottom-line requirement and must be improved. (2) The quality of the basic facilities and equipment of the leased space is mostly a One-dimensional Quality, Attractive Quality, and the degree of importance is lower than that of the building performance. (3) The external activity facilities of the leased space are mostly undifferentiated needs, for which the local users are not particularly prominent. (4) The scale and plot ratio of local housing is relatively high, and users are dissatisfied and complaining, which falls under the category of Reverse Quality. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area's inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH. [ABSTRACT FROM AUTHOR]

Analyzing the spatio-temporal relationship between socio-economics and land-use structure at the micro-scale is crucial for effective spatial governance in large cities. This paper focuses on Beijing, utilizing long time-series remote sensing images and multi-source data spanning 30 years. We employ spatio-temporal clustering based on kilometer grid cells and a community-scale multi-factor aggregation method to categorize the linkages and spatio-temporal matching of population, GDP, land development, and ecological protection at the community level in a problem-oriented approach. Results indicate significant changes in Beijing's population, GDP, and land use, with a 11.53% increase in land development intensity. We identify significant temporal and spatial disparities between population–GDP dynamics, population–land development trends, and GDP–land development patterns, underscoring the multifaceted challenges inherent in urban governance. Areas characterized by lagging population concentration, sluggish economic growth, rampant land development, and ecological fragility collectively encapsulate notable portions of Beijing's expansive urban terrain. Mismatches pose governance risks, with medium to high-risk communities comprising 18.08% of community units and high-risk types representing 4.27% in Beijing. These discrepancies pose formidable governance risks, with communities ranging from moderate to high-risk categories, necessitating tailored interventions to address their unique challenges. This systematic exploration of comprehensive governance issues within mega-cities promises to furnish decision-makers with invaluable insights, facilitating nuanced and strategic urban governance approaches tailored to the intricacies of urban dynamics and challenges. [ABSTRACT FROM AUTHOR]

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This paper attempts to answer the question: How can community micro-regeneration projects in Chinese cities be situated and contextualized as particular kinds of practices to build resilient communities? Amidst rapid urbanization and social transformation over the past four decades, historical areas in Chinese cities have faced neglect, exacerbating inequalities and marginalization. The emerging strategy of micro-regeneration, emphasizing public participation and co-production, addresses this issue by revitalizing small community spaces using local resources and empowering grassroots efforts. This research contextualizes micro-regeneration within the global discourse on community resilience, highlighting communities’ agency and collaborative strength in adapting to change. Through the analysis of co-productive micro-regeneration cases in Beijing and Shanghai, this study offers insights into resourcefulness and the transformative potential of resilience, shedding light on China’s urban regeneration shift toward co-production and heightened social focus. [ABSTRACT FROM AUTHOR]

This paper investigates the life experiences of creative expats and the associated impact of, and on, the built environment of their host localities. Grounded on participants' testimonies, we develop the concept of transient mentality as a potential mediating factor in-between such reciprocal relations. Employing an interview and survey-based research approach and drawing on grounded theory for data collection and analysis in Beijing, China, we found that the prevalence of a transient mentality among creative expats is influenced by the nature of their occupations, the fluidity of their social relationships, and the rapid transformations in the built environment. This transient mentality, in turn, affects the production of the cityscape through the consumption preferences of these expats. We argue that understanding such a transient mentality is crucial for urban planning and cultural policy, particularly in (emerging) global cities that work to brand themselves as international creative hubs. [ABSTRACT FROM AUTHOR]

This paper, set against the backdrop of expanding urban rail networks and dynamic urban development, focuses on the distribution and evolution of commercial Points of Interests (POIs) within the central urban rail transit areas of Beijing. The study examines data from four different years—2008, 2013, 2017, and 2020—to observe the temporal evolution of commercial entities. It identifies stable explanatory variables affecting the distribution and evolution of commercial POIs, which include rail transit accessibility, characteristics of the working and residential population distribution around stations, and the construction intensity in the vicinity of station areas. Through statistical analysis and model building, relatively stable linear regression equations were established, with R 2 values generally maintained above 0.5 (except for 2017). The study advances our understanding of the influence of rail transit on urban commercial spaces and how this influence shifts with temporal and urban developmental changes. It elucidates the correlation between changes in the number of businesses and spatial configuration, offering insights and information for urban planners and policy makers. This research also serves as a model for exploring the interplay between urban rail transit and commercial spaces in other major cities. [ABSTRACT FROM AUTHOR]

Given that PM2.5 concentration is not only related to local pollutants, but also affected by long-distance transmission of PM2.5 in adjacent areas, the key to improving the prediction accuracy of PM2.5 concentration is to comprehensively consider the effect of local influencing factors and the transmission effect of PM2.5 in adjacent areas. For this purpose, a novel seasonal discrete multivariable grey prediction model, encompassing spatial influencing factors, has been established. Firstly, we analyze the mechanism of spatial influencing factors and the reasonableness of using PM2.5 concentration in adjacent areas as the spatial influencing factors. Secondly, based on the spatial agglomeration characteristics of PM2.5 concentration, the K - means clustering algorithm is used to cluster adjacent cities with similar PM2.5 concentration, then the comprehensive value of PM2.5 concentration in each city cluster is calculated by weighted average combination. On this basis, a driving term of spatial influencing factors and a cosine trigonometric function term are introduced into the novel model to characterize the effect of spatial influencing factors on PM2.5 concentration and the seasonal fluctuation of itself, respectively. More importantly, the Genetic Algorithm Toolbox is employed to optimally determine the emerging parameters of this model, and the time response function of the novel model is calculated by mathematical induction method. Lastly, the new model is deemed valid through testing its PM2.5 concentration predictions for the cities of Beijing, Tianjin, and Baoding in the Beijing-TianjinHebei region. Based on the original observations from 2018Q1 to 2023Q2, the novel model is built for PM2.5 concentration prediction in 2023Q3 to 2024Q2 for the three cities. The findings imply that the newly developed model outperforms its competitors significantly and has the potential to serve as a robust tool for predicting PM2.5 concentration. [ABSTRACT FROM AUTHOR]

• This paper takes Beijing as a case to study regional economic resilience. • We systematically explore the effects of COVID-19 on industrial dynamics. • Individual-owned firms located in high-risk regions are more vulnerable. • The epidemic shock leads to cascading effects among the local industrial network. The COVID-19 epidemic has altered the economic geographies of cities. However, the academic focus on urban industries and their structural dynamics is far from enough. In this paper, we integrate literature from economic geography, business studies and network sciences, and design an adaptation framework to theorize urban economic resilience. Taking the COVID-19 outbreak in Xinfadi, Beijing during the summer of 2020 as a case study, we use Logit regressions and DID estimations to identify the impacts of the epidemic on local industrial dynamics. Our results support that the COVID-19 outbreak significantly increases the number of business exits. The negative impacts are concentrated in high-risk regions, pointing to specific industries and individual-owned firms. Furthermore, we use a machine learning method to visualize the dynamics of industry networks in a small-scale urban area, and then simulate the transmission paths of industry-failure. In doing so, we contribute to current economic resilience literature that firm-level adaptation and industrial dynamics complement regional adaptation theory, while the cascading effects in the industry network underpin the micro foundations of resilience formation. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities. • A Potential to Kinetic Energy (PKE) framework is introduced to investigate the relationship between green space attractiveness and urban movements. • Potential energy contains five sub-concepts: size, culture, attraction, proximity and location. • Kinetic energy represents urban movement that characterizes the motion of objects resulting from potential energy conversion. • Empirical analysis of Beijing shows the significant role of relative green space attractiveness in shaping urban movement patterns. • The PKE framework supports urban planners to evaluate green space attractiveness, examine the impact of landscape change, and promote inclusive urban environments. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

As urbanization speeds up, the concept of healthy cities is receiving more focus. This article compares Chongzuo and Nanning in Guangxi with Beijing to assess the development gaps in cities in Guangxi. An indicator system for healthy cities was designed from six dimensions—healthy economy, healthy population, healthy healthcare, healthy environment, healthy facilities, and healthy transportation—and 26 secondary indicators, which were selected from 2005 to 2022, and an improved factor analysis was used to synthesize a healthy city index (HCI). The number of factors was determined by combining characteristic roots and the variance contribution rate, and the HCI was weighted using the entropy-weighted Topsis method. A comprehensive evaluation of the urban health status of these cities was conducted. The results showed that extracting six common factors had the greatest effect, with a cumulative variance contribution rate of 93.83%. Chongzuo city scored higher in the field of healthcare. The healthy environment score of Nanning was relatively high, which may be related to continuous increases in green measures. In terms of the healthy economy dimension, Beijing was far ahead. However, in recent years, the healthy economy level in Chongzuo has increased, and the GDP growth rate has ranked among the highest in Guangxi. In addition, the growth rate of healthy facilities in Nanning was relatively fast and has been greater than that in Chongzuo in recent years, which indicates that the Nanning Municipal Government believes urban construction and municipal supporting facilities are highly important. In terms of healthy transportation, Chongzuo and Nanning scored higher than Beijing. This may be because the transportation in these two cities is convenient and the traffic density is more balanced than that in Beijing, thereby reducing traffic congestion. Chongzuo had the highest score for a healthy population, and a steadily growing population provides the city with stable human resources, which helps promote urban economic and social development. Finally, relevant policy recommendations were put forwards to enhance the health level of the cities. [ABSTRACT FROM AUTHOR]

In recent years, the development of China's megacities has entered the stage of stock renewal. Research and practice concerning old city renewal in cities with a long history, represented by Beijing, has also become a hot spot for researchers and designers in the fields of urban planning, architecture and landscape architecture. As one of the main spatial components of the old city, Beijing's traditional quadrangles are closely related to the spatial perception and emotional experience of citizens and tourists due to their near-human scale characteristics. However, current research focuses more on the evaluation of the historical value of the quadrangles in the early stage of renovation and the specific design and construction methods in the process of renovation, and few studies pay attention to the characteristics of the built environment that promote the emotional experience of users after renovation is completed. Under these circumstances, the study focuses on the emotional attachment between people and the spatial composition and built environment characteristics of the renovated traditional Beijing quadrangles; the avant garde small-scale quadrangle renovation type, which has a wider coverage, more types of user, and pays more attention to people's emotional experience in the renovated space, was selected as the research object. Four typical quadrangle cases were selected for in-depth discussion. Based on the theory of emotional attachment from a multidisciplinary perspective, this study constructed a series of scales that can measure the degree and dimension of emotional attachment between people and the built environment, coupled with local observation and interviews, to obtain multi-dimensional data reflecting attachment, and used SPSS to conduct correlation analysis and exploratory factor analysis to quantitatively explore the effects of different built environment characteristics on attachment. The results show that: (1) As for the material characteristics, the organic integration of traditional and modern materials, structures and colors can effectively enhance people's positive emotional experience and promote the establishment of emotional attachment. The combination of these characteristics and the process of people's three-dimensional spatial experience can further enhance the degree of attachment. In addition, the consistency of materials and technologies, the organic integration of the old and the new, and the carrying capacity of the renovation method for traditional history and culture are the basis for promoting this kind of emotional attachment, which needs to be further explored and considered. (2) As for the non-material characteristics, the diverse, variable, recognizable, unique, and digital spatial function settings that respond to changes in people's need and current developments can significantly promote the establishment of emotional attachment between people and the environment. This further emphasizes the importance of positioning the space in the early stage of the renovation and the operation of the space in the later stage. (3) The results further support the validity and rationality of the series of scales constructed in this study in quantitatively measuring the attachment characteristics between people and the built environment. As a result, the study provides a reference for emotion-oriented design means, research logic and quantitative evaluation methods in the practice and research of urban renovation and renewal in the future. [ABSTRACT FROM AUTHOR]

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems. [ABSTRACT FROM AUTHOR]

Exploring urban spatial structures through spatial coupling analysis methods is an important method to provide theoretical support for the construction of sustainable urban structures. In order to make up for the neglect of POI species differences in previous studies, information entropy was introduced to calculate POI confusion, and a comprehensive POI index was constructed by combining kernel density and the entropy weight method; impervious surface coverage was extracted based on land cover data. The spatial distribution of the Beijing–Tianjin–Hebei urban agglomeration and some typical cities was analyzed by coupling two types of data using the dual-factor mapping method. The research indicates the following: (1). The spatial distribution of the two sets of data in the Beijing–Tianjin–Hebei region is highly consistent, indicating a state of high spatial coupling; Beijing has the highest proportion of coupling in the same region at the city level (73.39%). (2). The areas with different coupling of the two types of data are mainly distributed in the urban fringe areas transitioning from the city center to the suburbs, as well as in large-scale areas with single functionality such as airports, scenic spots, and ports. This study shows that analysis combining the POI comprehensive index and impervious surface coverage can effectively characterize urban spatial structure characteristics, providing a new perspective for the study of the spatial structure of the Beijing–Tianjin–Hebei urban agglomeration. It is of great significance for a deeper understanding of the laws of urban agglomeration spatial structures and guiding the coordinated development of the Beijing–Tianjin–Hebei urban agglomeration. [ABSTRACT FROM AUTHOR]

The surface urban heat island (SUHI) phenomenon, predominantly influenced by factors associated with the built-up environment, is prominent in large metropolitan areas. To effectively mitigate the escalating thermal environment challenges arising during large cities' developmental planning, rigorously examining the spatial interdependencies between the built-up environment and the SUHI phenomenon is imperative. Employing Beijing's primary urban area as a case study, this research addresses the gap in the systematic analysis of spatial correlations between the built-up environment and SUHI within the urban heat island effect research domain. The study leverages Landsat-8 satellite data spanning 2016–2020, Sentinel-2 land-use classification data, and 2020 digital elevation model (DEM) data, integrating them with geospatial data processing techniques to probe the multifaceted associations between the built-up environment and SUHI in a 1 × 1-km grid-based local-scale model. This investigation is distinguished by developing a Comprehensive Built-up Environment System Index, synthesized through multisource data and multidimensional methodologies. The study culminates in the following key findings: (1) Between 2016 and 2020, Beijing's primary urban area manifested an ascending spiral trend in the SUHI effect, with the heat island morphology exhibiting nonuniformity across the four seasons. (2) In the historical summer heat island scenarios of Beijing's primary urban area, the architectural and roadway environments demonstrated a consistently positive correlation with the SUHI effect. (3) Excluding the average number of floors parameter, all remaining urban built-up environment parameters exhibited a significant association with SUHI fluctuations. This research provides valuable insights on the following aspects for urban planners, policymakers, and practitioners addressing the surface urban heat island effect in Beijing's main urban area. (1) Urban heat island mitigation: Influential urban parameters, like building density, road connectivity coefficient, and green space, significantly impact surface urban heat island (SUHI) intensity. Targeted strategies, such as increasing green spaces and implementing cool roofs, can mitigate the SUHI effect and improve urban thermal comfort. (2) Season-specific planning: Recognizing seasonal variations in SUHI intensity allows for season-specific urban planning. Implementing cooling strategies, like urban cooling parks and remarkable pavement technologies, during summer in heat-prone areas efficiently helps manage thermal environments. (3) Data-driven decision-making: Leveraging geospatial data and advanced analytics fosters data-driven urban planning. Policymakers can use the Comprehensive Built-up Environment System Index to assess the impact of urban parameters on SUHI intensity, facilitating evidence-based policy formulation and adaptive management. (4) Climate-resilient urban design: Integrating SUHI mitigation measures, such as green infrastructure, cool roofs, and reflective materials, helps enhance climate resilience. Urban designers can create sustainable and climate-resilient urban environments, promoting long-term urban sustainability and well-being. [ABSTRACT FROM AUTHOR]

Understanding the evolution of rural landscapes in metropolises during rapid urbanization is crucial for formulating policies to protect the rural ecological environment. In this study, remote sensing and geographical information system data, as well as applied landscape index analysis, are used to examine the spatiotemporal evolution of rural landscape patterns in the Beijing-Tianjin region of China, which has experienced rapid urbanization. The relationships between land use/land cover changes and changes in rural landscape patterns are explored. The results revealed significant spatial differences in the rural landscapes in the Beijing-Tianjin region; farmland and forestland were the main types of landscapes, creating a "mountain-field-sea" natural landscape pattern. The conversion of rural landscapes in the Beijing-Tianjin region involved mainly the conversion of farmland to urban areas, with few exchanges between other landscape types. The urban areas in the Beijing-Tianjin region increased by 3% per decade; farmland decreased at the same rate. Additionally, the rural landscape patterns in the Beijing-Tianjin region were dominated by fragmentation, dispersion, and heterogeneity and moved from complex to regular. Water bodies displayed the most fragmented natural landscape; their number of patches increased by 36%, though their network characteristics were maintained. Forestland was the most concentrated natural landscape. In this study, theoretical support and a scientific reference for the optimization of rural landscape patterns and the improvement in rural living environments in rapidly urbanizing areas are provided. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of the Odd-Even policy in Jakarta from 2017 to 2022, comparing it with similar measures in Beijing from 2013 to 2017. Both megacities face severe traffic congestion due to rapid urbanization, population growth, and increased vehicle ownership. Jakarta's congestion is exacerbated by population density, inadequate public transportation, and high private vehicle ownership, leading to prolonged travel times, air pollution, and economic losses. Similarly, Beijing struggles with traffic congestion due to its massive population and rapid economic development, resulting in gridlock, air pollution, and decreased quality of life. The research aims to understand the Odd-Even policy's impact on traffic flow, emissions, and public perception in both cities, using qualitative methods and diverse data sources. It also identifies obstacles to policy effectiveness, such as enforcement, infrastructure, and public compliance, offering recommendations for policymakers and urban planners to improve sustainable transportation and environmental management. Overall, the study contributes insights into congestion mitigation in megacities, aiding policymakers in enhancing urban life quality. [ABSTRACT FROM AUTHOR]

• Experiments contrasted ten machine learning models in susceptibility prediction. • Hybrid ensemble models significantly outperformed widely-used individual models. • Study profiled the first urban waterlogging susceptibility map in Beijing, China. • Urban road network and densely populated areas face severer waterlogging risk. Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]

Green space accessibility affects housing prices, which is an issue that has been underappreciated. This paper takes Beijing's central urban area as an example. First, the Gaussian-based two-step floating catchment area (G2SFCA) method is used to measure four levels (community green space, subdistrict green space, district green space, and municipal green space) of green space accessibility by multiple travel modes (walking, cycling and driving). Second, the spatial error model (SEM) is used to examine the factors influencing housing prices. Finally, the geographically weighted regression (GWR) model is introduced to explore the spatial relationship between green space accessibility by walking, cycling and driving and housing prices. The results show the following: (1) The SEM shows that only driving accessibility has a positive spatial spillover effect on housing prices, indicating that the spatial inequity of local community green spaces by driving will exacerbate surrounding spatial inequity. (2) The GWR model shows that from the perspective of walking and driving accessibility, most areas within the Second Ring Road show spatial equity in green spaces, while cycling accessibility shows that the spatial inequity of green space exists only outside the Second Ring Road. (3) We find that municipal green space and subdistrict green space most affect the spatial equity of green spaces. Community green space, which is not open to the public, plays a minimal role. Regarding walking and driving accessibility, municipal green space increases the spatial equity of green spaces. In contrast, this type of green space can promote the spatial equity of green spaces regarding cycling accessibility. This study can significantly improve the quality of urban settlements in the Global South and has important implications for policy-makers, planners, and stakeholders in other large cities worldwide to create green and liveable cities. • We investigated green space accessibility impact on housing prices • G2SFCA was used to measure green space accessibility for multiple travel modes • Driving accessibility had positive spatial spillover effect on housing prices • Driving and cycling green space accessibility had opposite effect on housing prices • Our results provided guidance and example for other developing countries megacities. [ABSTRACT FROM AUTHOR]

There is considerable evidence that word reading in Chinese is predicted by morphological awareness. However, does learning to read contribute conversely to the later development of morphological awareness in Chinese? This two-year longitudinal study of Chinese children from Beijing and Hong Kong demonstrated that word reading predicted morphological awareness in Chinese. The findings from a latent growth curve analysis suggested that both the initial level and the growth rate of word reading predicted morphological awareness, with phonological awareness statistically controlled across the two Chinese-speaking societies. However, the association patterns were slightly different in the two cities. In Beijing, phonological awareness was found to partially mediate the prediction of reading growth in morphological awareness, but this mediation effect was not found in Hong Kong. Overall, these findings highlight the importance of early reading experience in the development of morphological awareness in Chinese children across different Chinese-speaking societies. [ABSTRACT FROM AUTHOR]

This study explores 'inspiration' in the context of the Olympic Games, focusing on the Beijing 2008 and London 2012 Olympics years after the event hosting. Utilizing a retroductive strategy, the study identifies key sources of inspiration, constructs a causal model based on existing literature, and validates it through qualitative and quantitative data. Although contextual differences exist between the two cities, similar findings emerged: inspirational feelings have indeed persisted years after the hosting of the Games, largely attributed to the 'elite role model' effects and 'the overall experience of the Games'. While the effects of such feelings on sports participation vary, in Beijing at least, the hosting of the Games had a significant correlation with participation in sport/physical activity in the years that followed. While hosting such events might be valuable for promoting participation, its influence should not be overstated, and further research is needed to fully understand its long-term effects. [ABSTRACT FROM AUTHOR]

Purpose: Retailers have recently leveraged store-loyal customers' store attachment to maintain customer relationships and motivate patronage intentions. However, the COVID-19 outbreak has driven customer migration from offline to mobile channels. Mobile retail applications (APPs) have been used by numerous retailers to reach their customers. Nonetheless, it has yet to be determined (1) whether store attachment can facilitate (or impede) the adoption of mobile retail APPs and (2) whether store-loyal customers will continue visiting offline stores in the post-pandemic era. To address these questions, we propose a theoretical account using integrated theories on trust transfer and store attachment. Design/methodology/approach: We conducted multi-stage, longitudinal field surveys in two cities of mainland China: Beijing and Guangzhou. From two rounds of data collection, 237 and 103 responses were obtained in March 2022. Hypotheses were tested by partial least squares – structural equation modelling (PLS–SEM). Findings: Results showed that customer trust in an offline retailer can be transferred to the retailer's mobile APP at the pre-adoption stage, facilitating APP adoption. Notably, store-loyal customers who exhibited a strong attachment to the physical store of a retailer were more inclined to transfer their trust to the mobile APP of the retailer. This occurrence leads to an increased adoption rate, enhanced post-adoption satisfaction and increased inclination to continue (rather than discontinue) usage. Originality/value: This study is the first to investigate the changes in store-loyal customers' shopping behaviour in the mobile retail era and in the post-COVID-19 pandemic recovery. Our findings elucidate the role of physical store attachment in the trust-transfer mechanism. Furthermore, store attachment may not prevent customers' channel migration behaviour. Retailers may have to re-consider how to manage channel cannibalisation issues in the post-pandemic recovery. [ABSTRACT FROM AUTHOR]

The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. [ABSTRACT FROM AUTHOR]

Promoting sustainable development in manufacturing is a paramount goal, with a focus on advancing green innovation. This study constructs a system for evaluating green innovation efficiency and employs the Super-EBM model, incorporating unexpected output, to assess the efficiency of green innovation in 13 cities across the Beijing-Tianjin-Hebei region from 2011 to 2020. The study further conducts dynamic analysis using the Malmquist-Luenberger index. Results reveal that, statically, the overall green innovation efficiency in the manufacturing industry of the Beijing-Tianjin-Hebei region is inefficient. There exists a considerable gap in green innovation efficiency among Beijing, Tianjin, and Hebei, with Beijing and Tianjin demonstrating superior performance compared to Hebei. Substantial variations exist in the green innovation efficiency of manufacturing across different cities in the Beijing-Tianjin-Hebei region. Only Beijing, Qinhuangdao, and Baoding achieve DEA-effective green innovation efficiency in the manufacturing industry, while the other cities do not. Dynamically, the green innovation efficiency of the manufacturing industry in the Beijing-Tianjin-Hebei region is on the rise. There is a varying degree of improvement in green innovation efficiency in Beijing, Tianjin, and Hebei, with Hebei showing the highest improvement, Tianjin ranking second, and Beijing having the least improvement. With the exception of Langfang and Hengshui, the green innovation efficiency in the manufacturing industry is improving in most cities in the Beijing-Tianjin-Hebei region, with Hebei witnessing the most significant improvement. This study aims to integrate "environmental pollution" into the evaluation index system for green innovation efficiency. It assesses green innovation efficiency in the manufacturing industry of the Beijing- Tianjin-Hebei region, considering both static and dynamic perspectives. This clarification offers insights into the level of green innovation, contributing valuable information for the advancement of high-quality development in the regional manufacturing industry. [ABSTRACT FROM AUTHOR]

Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model's bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China. [ABSTRACT FROM AUTHOR]

Rail transit airport lines, as fast passages connecting urban areas and airports, play an important role in facilitating residents' travel and optimizing urban spatial layout. This study aims to explore the passenger flow characteristics of airport landside modes under three kinds of rail transit access form: dedicated, general, and mixed. On the basis of analyzing the access features of different airport lines, a three-level nested logit (TLNL) model is proposed to explore the decision-making mechanism of passengers' landside mode choice in two stages. Then, taking the rail transit of Beijing Daxing International Airport as an example, scenario application is carried out to discuss the change of the landside mode sharing rate after the airport line is extended. The results show that the access form of rail transit has no essential impact on the landside traffic structure. When the airport line extends to the urban area, its sharing rate increases significantly, the passenger attraction effect of the dedicated line being significantly better than the other two forms. Extending the airport line downtown and connecting with large transport hubs can reduce the number of transfers, effectively managing the contradiction between passengers' travel convenience and comfort. In general, the TLNL model has good applicability to the analysis of how rail transit affects the sharing rate of landside modes; the results can provide reference and ideas for the selection and planning of rail airport line access forms. [ABSTRACT FROM AUTHOR]

Simple Summary: This study addresses the concern of managing and coexisting with community cats in urban areas. The study was conducted in urban China through a survey involving 5382 participants. It explores the residents' perceptions of the general health of community cats and the relationships between humans and these feline inhabitants. The findings show that about 70% of participants considered all or most of the community cats to be in good health, and about 60% reported harmonious or non-conflict coexistence. It was found that residents' relationships with community cats were associated with socio-demographic factors such as education, marital status, and income level. The study highlights the importance of customized approaches to cat management in various cities and communities. This provides useful information for stakeholders to create efficient policies and interventions for the welfare of the community cats and the people who live alongside them. The management and coexistence of community cats in urban areas is a growing concern amid global urbanization. Through a survey-based investigation, we examine the residents' perceptions of the general health of community cats and human-cat relationships in urban China. The data from 5382 participants revealed that approximately 70% of participants perceived community cats as being in good health, and 60% reported harmonious or non-conflict coexistence between residents and these cats. Around 45% of the participants rescued or helped community cats, 38% expressed their intention to adopt, and 18% complained about the issues of community cats to management staff. Linear, logistic, and multilevel-logistic regressions were employed to examine the associations between the types of cities and communities or the participants' socio-demographics and the perceived well-being of community cats or human-cat relationships. The results show that the cats in fourth-tier cities (e.g., county-level cities) had poorer living conditions than in first-tier cities (e.g., Beijing), while the cats in urban village communities (e.g., villages in the city) were less likely to exhibit good health than in ordinary commercial housing communities. The results also show that socio-demographic variables, such as educational attainment, marital status, and income level, predicted participants' relationships with community cats. This study is the first of its kind. It provides valuable insights for stakeholders to develop effective policies and interventions on cat management, emphasizing the need for tailored strategies in diverse urban settings and populations. [ABSTRACT FROM AUTHOR]

Rapid urbanization exerts noteworthy impacts on the terrestrial ecosystem carbon budget, with pronounced effects in a metropolis such as Beijing, the capital city of China. These impacts include both Direct and Indirect Impacts. For instance, direct impacts influence regional Net Primary Productivity (NPP) by directly altering the vegetation coverage area. Concurrently, indirect impacts primarily affect regional NPP indirectly through climate change and urban vegetation management. How direct and indirect impacts contribute to the NPP is the core content of our research. Owing to that, we need to precisely assess the spatial and seasonal characteristics of the impact of urbanization in Beijing from 2000 to 2020. Firstly, a novel framework was proposed to analyze the impact of urbanization on NPP at the pixel level. Meanwhile, we employ the Proximity Expansion Index (PEI) to analyze urban expansion patterns. Results reveal that the direct impacts led to a cumulative NPP loss of 0.98 TgC, with the largest loss stemming from cropland conversion to construction land. During the last two decades, there has been a 56.87% increase in the area used for urban development in Beijing, a clear sign of swift urban expansion. Concurrently, this urban growth has had favorable indirect effects on NPP, with an average annual increase of 9.76 gC·m−2·year−1, mainly observed in urbanized regions. Moreover, the seasonal analysis underscored that indirect impacts were primarily temperature-related, exhibiting higher values during autumn and winter within urban areas, indicating enhanced vegetation growth suitability in urban areas during these seasons. Our findings quantitatively examine the numerical relationship between direct and indirect impacts at a magnitude level. The carbon gain brought about by indirect impacts surpassed the carbon loss induced by direct impacts, with indirect impacts offsetting 29.41% of the carbon loss due to direct impacts. Ultimately, we advocate for enhanced greening initiatives in areas of Beijing with higher indirect impacts to achieve optimal carbon gain. This strategy might effectively reduce the negative impact of rapid urbanization on the carbon budget of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

New particle formation contributes significantly to the number concentration of ultrafine particles (UFPs, d ≤ 100 nm) and has a great impact on human health and global climate. Iodine oxoacids (HIO x , including iodic acid, HIO 3 , and iodous acid, HIO 2) have been observed in pristine regions and proved to dominate new particle formation (NPF) at some sites. However, the knowledge of HIO x in polluted urban areas is rather limited. Here, we conducted a long-term measurements of gaseous iodine oxoacids and sulfuric acid in Beijing from January 2019 to October 2021 and also in Nanjing from March 2019 to February 2020 and investigated the contribution of HIO x to UFP number concentration in both urban environments. HIO 3 is highest in summer, up to 2.85 × 10 6 and 2.78 × 10 6 cm -3 in Beijing and Nanjing, respectively, and is lowest in winter by 96 % and 75 %, respectively. HIO 3 exhibits more prominent variation than H 2 SO 4 in both urban sites. HIO 3 concentration shows a clear diurnal pattern at both sites with a daily maximum at around noontime, similar to the atmospheric temperature, solar radiation, and ozone (O 3) levels. HIO 2 concentration has the same diurnal and seasonal trend as HIO 3 but is overall about an order of magnitude lower than HIO 3 concentration. Back trajectory analysis suggests that the sources for inland iodine species could be a mix of marine and terrestrial origins, with both having peak iodine emission in warm seasons. While the contribution of HIO 2 to particle growth is marginal in Beijing and Nanjing, our results demonstrate that HIO 3 enhances the particle survival probability of sub-3 nm particles by about 40 % (median) and occasionally by more than 100 % in NPF events, suggesting HIO x are significant contributor to UFPs in polluted urban areas. As the growth contribution from HIO 3 and H 2 SO 4 is similar on a per-molecule basis, we propose that the sum of HIO 3 and H 2 SO 4 could be used to estimate sub-3 nm particle growth of inorganic acid origin in polluted atmospheres with a significant amount of HIO x. [ABSTRACT FROM AUTHOR]

• Coordination among subsystems is key to improving urban resilience. • There is a large gap between the UR of Hebei Province and of Beijing-Tianjin. • The CCD of the urban resilience subsystem is trending upward but growing slowly. • The degree of spatial correlation of subsystems CCD in BTHUA is weak. Building resilient cities is a novel concept for dealing with risk crises and achieving sustainable urban development in modern times. This paper utilized data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) spanning from 2009 to 2019, along with the CRITIC-Entropy weight method, coupling coordination degree (CCD) model and spatial correlation analysis to measure urban resilience and investigate subsystem coupling coordination relationships while considering spatial clustering characteristics. The results indicate that: (1) Spatially, there is a significant gap between the UR index of cities in Hebei Province and that of Beijing-Tianjin, with a "Beijing-Tianjin dual-core driven" trend. Regarding temporal evolution, the growth trend of the UR index is consistent with that of the subsystem resilience index. (2) In general, the CCD of the urban resilience subsystem is slowly trending upwards, with the overall CCD level of BTHUA ranging from primary coordination to moderate coordination. (3) The degree of spatial correlation of subsystem CCD in BTHUA is weak, and more cities exhibit heterogeneous agglomeration. These findings provide valuable inspiration and reference for resilient city construction and coordinated regional development to a certain extent. [ABSTRACT FROM AUTHOR]

Public sub-health has emerged as a pressing concern in densely populated urban areas. The urban environment, with its innate ability to modulate public emotions, harbors a precious resource in the form of urban rivers, which provide a serene and verdant space. This study focuses on the Liangma River in Chaoyang District, Beijing, selecting two rivers with diverse landscape features as the subjects of research. By employing physiological feedback data in conjunction with a subjective questionnaire, the emotional impact of high-density urban riverside spaces on individuals is quantitatively analyzed. Electrocardiogram (ECG) data, eye movement data, and the positive–negative emotion scale (PANAS) are subjected to data analysis. The study reveals the following key findings: (1) The riverside landscape in high-density urban areas exerts a positive influence on emotional well-being. Individuals in more natural river settings experience greater levels of contentment and relaxation, while those in areas with a higher proportion of artificial elements exhibit increased excitement and happiness. Moreover, scenes characterized by a greater degree of greening have a more pronounced soothing effect on mood. (2) A specific correlation between visual characteristics and emotional fluctuations is observed. The waterfront side of the trail exerts a stronger spatial attraction, and a higher proportion of blue and green spaces significantly contributes to stress relief. (3) The utilization of human-induced engineering technology, which captures emotional changes through physiological feedback, demonstrates a higher level of accuracy and is well-suited for small-scale studies. These findings highlight the potential of arranging diverse types of waterfront footpath landscapes in high-density urban areas and approaching waterfront landscape design and transformation from a novel perspective centered on health intervention. Such efforts hold promise for alleviating the daily pressures faced by the general public and fostering the development of a "healthy city". [ABSTRACT FROM AUTHOR]