Chinese Cities Research Articles

Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy

There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI.

Organizational resilience and primary care nurses' work conditions and wellbeing: a multilevel empirical study in China.

Resilience is crucial for the health system better preventing and responding to public health threats and providing high-quality services. Despite the growing interest in the concept of resilience in health care, however, there is little empirical evidence of the impact of organizational resilience, especially in primary care settings. As the largest professional group in primary care, primary care nurses are taking more and more responsibilities during their daily practice, which influences both their work conditions and wellbeing. This study aims to examine the association between organizational resilience and primary care nurses' working conditions and wellbeing. Using a convenience sampling approach, we recruited 175 primary care nurses from 38 community health centers (CHCs) in four cities in China. Organizational resilience was operationalized as comprising two domains: adaptive capacity and planning capacity, and measured using a 16-item scale. The primary care nurses' working condition indicators comprised variables of psychological safety, organizational commitment, professional commitment, and self-directed learning; wellbeing indicators included depression and burnout. Hierarchical linear regression models were built for analysis. We found that the sampled CHCs have a relatively high level of organizational resilience. The organizational resilience was positively associated with the four indicators of working conditions: psychological safety (β=0.04, p<0.01), organizational commitment (β=0.38,p<0.01), professional commitment (β=0.39, p<0.01), and self-directed learning (β=0.28, p<0.01). However, organizational resilience was not significantly associated with the two wellbeing indicators. Furthermore, we found the adaptive capacity has stronger association compared with planning capacity. Therefore, primary care manager should build resilient organizations, especially the adaptive capacity, in order to enhance primary care nurses' psychological safety, commitment and learning behaviors. Further studies should also be conducted to understand the link between organizational resilience and primary care nurses' wellbeing.

The Effects of Foreign Direct Investment on Green Technology Innovation: New Evidence from China’s 278 Cities

ABSTRACT Foreign direct investment (FDI) has played a pivotal role in fostering economic development, and the FDI technology spillover effect is one of the core forces driving an open economy toward new green growth milestones. Evaluating the impact of FDI inflows on green innovation is essential for sustainable and environment-friendly city design. Based on the panel data of 278 cities in China from 2010–2019, we conducted an empirical analysis to test the impact and mechanisms of FDI on green technology innovation (GTI). The results demonstrate that FDI has a positive and highly significant effect on GTI. The channel analysis indicates that the FDI significantly stimulates urban GTI through two mechanisms: alleviating financing restraints and mitigating the risk of new GTI in the early stages. In the heterogeneity analysis, we find that the FDI effect is more pronounced in China’s resource-based regions with high levels of institutional and human capital and in regions with low-carbon pilot policies and good protection of intellectual property rights. Furthermore, the study shows FDI can affect GTI in neighboring areas through spatial spillover effects. Our findings underscore the importance of encouraging and sustaining internationalization in environmentally friendly sectors to foster green transformation. In essence, developing foreign investment policies and enhancing the green technology spillover effect of FDI are vital for strengthening GTI capacity.

Spatiotemporal Evolution and Driving Forces of Tourism Economic Resilience in Chinese Provinces

This study focuses on the resilience of tourism economies in Chinese provinces, exploring their spatiotemporal evolution and driving forces as a crucial prerequisite for promoting the high-quality development of China’s tourism industry. We construct a resilience evaluation index system from four dimensions: resistance resilience, recovery resilience, reshaping resilience, and development resilience, using provincial tourism data from 2012 to 2022. The study employs Moran’s Index, kernel density estimation, and GIS technology to investigate the differentiation characteristics, spatial evolution processes, and spatial agglomeration characteristics of provincial tourism economic resilience in China. Finally, the GeoDetector model is used to analyze the driving factors. The findings are as follows: (1) Over time, most provinces and cities in China have shown varying degrees of improvement in tourism economic resilience, with different changes observed across the four dimensions. (2) Spatially, significant differences exist between provinces, with better resilience in the east than in the west and in the south than in the north. (3) Regionally, while no polarization is observed, there is a distinct differentiation between high and low-value areas. (4) Regional linkages indicate the presence of interregional associations in China’s tourism economic resilience, with non-uniform distribution of cold and hot spots. (5) Key driving factors include per capita railway mileage, domestic tourism revenue, the number of travel agencies, and the number of employees in accommodation and catering. Under the backdrop of rapid tourism economic development, improving infrastructure construction and enhancing the comprehensive strength of the tourism industry is vital for boosting tourism economic resilience.

The Influence of Morphological Elements of Urban Gated Communities on Road Network Connectivity: A Study of 120 Samples of the Central Districts of Jinan, China

Currently, the dominant gated communities (GCs) in Chinese cities have fragmented the urban road network, causing traffic congestion, energy consumption, carbon emissions, and environmental pollution. The morphological elements of GCs are key factors affecting road network connectivity. This paper aimed to explore the influence of the morphological elements of GCs on road network connectivity, to provide a quantitative basis for the evaluation and renovation of the connectivity of GCs, and to provide insights for urban planning and policy. This paper quantitatively analyzed the connectivity of GCs using 120 samples from the central districts of Jinan, China. Morphological elements were the independent variables, while route directness (RD) and the network distance (D) to the nearest entrance were the dependent variables. RD measured the internal connectivity, and D measured the connectivity between the internal and external road networks of GCs. GIS was used to measure RD and D, and SPSS was used to conduct a correlation analysis to identify significant variables. Multiple linear regression and LASSO regression were used to test the influence of these factors on RD and D. LASSO regression was employed to construct prediction models for RD and D. We found that intersection density had the greatest impact on RD, while the number of entrances and exits, and the scale of GCs, had the greatest impact on D. Using thresholds of D = 250 and RD = 1.3, the four types of GCs were classified and corresponding renovation measures were proposed.

Green innovation and natural resource efficiency: The role of environmental regulations and resource endowment in Chinese cities

Achieving natural resource utilization efficiency (NRUE) is a critical objective in addressing the pressing challenges of climate change and the risks associated with the finite availability of mineral resources. Enhancing NRUE can mitigate environmental impacts and support sustainable development for future generations. In this context, green innovation emerges as a pivotal driver of environmental sustainability and resource conservation. However, its potential role in advancing the NRUE has not been fully explored. To fill this gap, this study conducts an empirical analysis of 268 Chinese cities from 2010 to 2022, examining the impact of green innovation on NRUE. This study also incorporates the mediating role of environmental regulations in shaping this impact. The findings demonstrate that green innovation significantly enhances NRUE, primarily by facilitating industrial structural upgrading and reducing resource dependence. Additionally, environmental regulations strengthen the positive effects of green innovation, further amplifying its contribution to NRUE. Moreover, geographical location and differences in resource endowments among cities lead to variations in the effectiveness of green innovation on NRUE. These findings underscore the complex interplay between green technological progress, proactive government policies, and NRUE, offering valuable insights for policymakers and researchers alike.

Developing a suitability assessment model for Public Private Partnerships: a case in urban China

Spatial heterogeneity is a fundamental aspect of Public Private Partnerships (PPPs). The appropriateness of PPPs applications varies across regions. While the assessment of suitability for PPPs is crucial, it has received limited scholarly attention. This study, using China as s representative case, developed a suitability assessment model and evaluated it across 287 prefecture-level cities in China. The results reveal considerable spatial variation in the suitability of PPPs within the country. Utilizing the natural breakpoint method, this study categorized Chinese cities into four distinct levels of PPP suitability: high, medium, low, and unsuitable. 219 cities were identified as either unsuitable or having low suitability for PPPs, suggesting that PPPs may not be broadly applicable in the current Chinese context. This study not only lays the groundwork for sustainable PPP application in China but also significantly contributes to shaping strategies for successful PPP implementation globally.

Does Government Digital Transformation Drive High-Quality Urban Economic Development? Evidence from E-Government Platform Construction

Digitalization represents a pivotal global development trend and serves as a significant force propelling economic and social transformation. This manuscript uses the global Malmquist–Luenberger (GML) model to estimate green total factor productivity (GTFP) across 284 Chinese cities from 2003 to 2018, taking the pilot policy of “construction and application of e-government public platforms based on cloud computing” as an example to assess the impact of government digital transformation on the qualitative development of the economy by using a difference-in-differences model to explore the path of its role and driving mechanism. The results reveal that government digital transformation promotes the qualitative improvement of the city’s economic development, and its driving effect shows a marginal incremental law. Moreover, government digital transformation can contribute to the formation of a “latecomer advantage” in the lagging regions, which creates a “catch-up effect” on the regions with favorable development foundations, excellent geographical conditions, high urban ranking, and high education quality. Additionally, government digital transformation boosts economic and social development quality through both innovation spillover and structural optimization.

Carbon emission reduction model for sewage heat recovery and utilization in a northern Chinese city

The reduction of carbon emissions from buildings has become crucial in the context of global warming. Sewage heat is gradually becoming valued as a clean energy source; however, its efficient use remains to be studied. This study explored the potential of sewage heat recovery in reducing carbon emissions in an urban built environment and proposed a carbon emission reduction model for sewage heat recovery and utilization. The relationship between various sizes of sub-catchments and different types of heat use buildings was evaluated using detailed geographic and demographic data. The results of this study suggest that the strategic placement of recovery systems can minimize carbon emissions. For instance, when the proportion of residential buildings in an urban sewage pipe network sub-catchment area exceeds 60 %, retail and office buildings are the most ideal heat recovery subjects, achieving carbon reduction rates greater than 0.6. However, when the proportion of residential buildings in a sub-catchment was <30 %, retail buildings showed better results than those of office buildings. Through the proposed method and its application, retail buildings should be considered first in terms of urban sewage heat recovery and utilization, followed by office buildings. The combined simulations and scenario analyses in this study provide a reference for enhancing the sustainability of urban heating practices using underutilized sewage heat.

A victim risk identification model for nature-induced urban disaster emergency response.

In recent years, nature-induced urban disasters in high-density modern cities in China have raised great concerns. The delayed and imprecise understanding of the real-time post-disaster situation made it difficult for the decision-makers to find a suitable emergency rescue plan. To this end, this study aims to facilitate the real-time performance and accuracy of on-site victim risk identification. In this article, we propose a victim identification model based on the You Only Look Once v7-W6 (YOLOv7-W6) algorithm. This model defines the "fall-down" pose as a key feature in identifying urgent victims from the perspective of disaster medicine rescue. The results demonstrate that this model performs superior accuracy (mAP@0.5, 0.960) and inference speed (5.1ms) on the established disaster victim database compared to other state-of-the-art object detection algorithms. Finally, a case study is illustrated to show the practical utilization of this model in a real disaster rescue scenario. This study proposes an intelligent on-site victim risk identification approach, contributing significantly to government emergency decision-making and response.

Do Technology Transfer Networks Impact Urban Innovation Capacity? Evidence From Chinese Cities

Do Technology Transfer Networks Impact Urban Innovation Capacity? Evidence From Chinese Cities

Evaluation and Obstacle Factors of the Green Development Level of Mature Resource-Based Cities Based on Entropy Weight TOPSIS Model: A Case Study of Daqing City in China

Evaluation and Obstacle Factors of the Green Development Level of Mature Resource-Based Cities Based on Entropy Weight TOPSIS Model: A Case Study of Daqing City in China

The impact of digital economy on energy conservation and emission reduction: evidence from prefecture-level cities in China

In the face of global climate challenges and energy crises, China aimed to enhance energy conservation and emission reduction (ECER). This study explored the role of the digital economy in boosting energy efficiency and supporting sustainable development by constructing an index system, applying principal component analysis, and using spatial econometric models to assess the impact of digital economic development in China's prefecture-level cities on ECER. Findings revealed regional development disparities and complex spatial interactions that influence ECER. The digital economy showed potential to reduce emissions after reaching a particular scale, suggesting policy recommendations for balanced growth.

Ranking the attribution of high-risk genotypes among women with cervical precancers and cancers: a cross-sectional study in Ningbo, China

BackgroundThe region-specific importance of carcinogenic HPV genotypes is required for optimizing HPV-based screening and promoting appropriate multivalent HPV prophylactic vaccines. This information is lacking for Ningbo, one of the first cities of China’s Healthy City Innovation Pilot Program for Cervical Cancer Elimination. Here, we investigated high-risk HPV (HR-HPV) genotype-specific distribution and attribution to biopsy-confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+) before mass vaccination in Ningbo, China.MethodsA total of 1393 eligible CIN2+ archived blocks (including 161 CIN2, 1107 CIN3, and 125 invasive cervical cancers [ICC]) were collected from 2017 to 2020 in Ningbo. HR-HPV DNA was genotyped using the SPF10-DEIA-LiPA25 version 1 detection system and the SureX HPV 25X Genotyping Kit. Genotype-specific attribution to CIN2+ was estimated using a fractional contribution approach.ResultsRanking by the attributable proportions, HPV16 remained the most important genotype in both cervical precancers and cancers, accounting for 36.8% of CIN2, 53.2% of CIN3, and 73.3% of ICC cases. Among cervical precancers, HPV52 (17.3% in CIN2, 12.7% in CIN3) and HPV58 (13.9%, 14.9%) ranked second and third, while HPV33 (8.3%, 7.9%) and HPV31 (6.5%, 4.1%) ranked fourth and fifth, respectively. However, among ICCs, HPV18 (5.7%) accounted for the second highest proportion, followed by HPV33 (5.4%), HPV58 (4.0%), and HPV45 (3.2%). HPV18/45 together accounted for 46.8% of adenocarcinomas, which was slightly lower than that of HPV16 (47.7%). The remaining HR-HPV genotypes (HPV35/39/51/56/59/66/68) combined accounted for only 6.7% of CIN2, 2.9% of CIN3, and 4.2% of ICC.ConclusionsWith Ningbo’s strong medical resources, it will be important to continue HPV16/18 control efforts, and could broaden to HPV31/33/45/52/58 for maximum health benefits. However, different strategies should be proposed for other HR-HPV genotypes based on their lower carcinogenic risks.

How Does Digital Finance Stimulate Urban Innovation in China? The Perspective of Human Capital and Consumption Upgrading

ABSTRACT Existing literature has mainly focused on the impact of digital finance on urban innovation, while they rarely explore the channel from the composite perspective of supply and demand in innovation activities. This study aims to explore how digital finance affects urban innovation in China, based on the exploration of factor supply and market demand channels. Through using panel data from 286 cities in China over the period 2011–2020, this paper empirically checks the role of digital finance in urban innovation. The estimation indicates a significant beneficial influence of digital finance on urban innovation in China after a series of robustness tests. The mechanism analysis suggests that digital finance promotes urban innovation by relying not solely on the improvement of human capital, but also on the advancement of market consumption. These findings deepen our understanding about how digital finance stimulates urban innovation in developing countries.

Spatiotemporal evolution of land use efficiency in 357 cities across mainland China from 2000 to 2020 based on SDG 11.3.1

High-efficiency land use facilitates the maximization of land utilization, lowers urban construction costs, and optimizes urban functional patterns. The Sustainable Development Goal 11.3.1 (SDG 11.3.1) can be used to assess land use efficiency (LUE), understand the current state of land use, and identify the potential for optimization. This study combines SDG 11.3.1 with other supplementary indicators to establish a land use efficiency evaluation system. This system provides a more precise understanding of internal city changes and enables a scientific assessment of urban LUE in Mainland China. The results showed that: (1) A significant number of cities were growing cities, particularly in the eastern region, with the population of built-up areas increased by 2.92 times from 2000 to 2020; (2) From 2000 to 2020, cities in China underwent rapid urban expansion, with the most significant urban expansion index in 2015–2020; (3) The coordination between population growth rate (PGR) and land consumption rate (LCR) worsened in the western region, while the central and eastern regions showed better coordination. (4) As the urban expansion index increased, the compactness index of the cities in the above three regions decreased and were at lower levels. This study establishes an evaluation system to assess the LUE and reveals the spatial and temporal characteristics of urban and population change. It holds paramount significance in enhancing LUE and encouraging sustainable development in Mainland China and serves as a valuable reference for global urban management.

An intervention study of poly-victimization among rural left-behind children based on the theoretical framework of planned behavior

BackgroundPoly-victimization (PV) not only threatens physical and mental health but also causes a range of social problems. Left-behind children in rural areas are more likely to experience PV problems. However, there have been fewer studies on PV among rural children, and even fewer intervention studies.ObjectiveThe difference-in-differences method was employed to analyze the impact of intervention measures, based on the theory of planned behavior, on PV among left-behind children in rural areas.MethodsThe study subjects were left-behind children from six middle schools in two cities in southern China, who completed the baseline survey from 2020 to 2021. They were divided into a control group and an intervention group, each consisting of 228 cases, based on their schools. Before and after the intervention, the Self-made victimization-related knowledge, attitude, and practice questionnaire, Poly-victimization scale, and Middle school students’ coping style scale were used to evaluate the victimization-related KAP(knowledge, attitude, and practice), victimization occurrence, and coping styles of left-behind children, respectively. Stata 15.0 was used to establish a difference-in-differences regression model to analyze the impact of the intervention measures on poly-victimization and coping styles.ResultsMixed Anova revealed that after the intervention, the KAP scores of the intervention group were significantly higher than those of the control group (p < 0.05). After the intervention, the incidence of child victimization in the intervention group dropped to 9.60% (n = 22), lower than in the baseline survey, with a statistically significant difference (p < 0.01). The incidence of PV among children in the intervention group was lower than that in the control group, with the difference being statistically significant (p < 0.01). The net reduction in the incidence of PV among children was 21.20%. After the intervention, the protection rate for preventing PV among children was 73.33%, and the effect index was 3.75. The intervention improved children’s coping styles, problem-solving, and help-seeking, while reducing negative coping styles such as avoidance and venting, with the differences being statistically significant (p < 0.05).ConclusionIntervention measures based on the theory of planned behavior reduce the occurrence of PV among left-behind children, and the intervention effects on different types of victimization are also different.

What factors promote the development of technological innovation? An analysis of 286 Chinese cities

What factors promote the development of technological innovation? An analysis of 286 Chinese cities

Impact of awareness of sports policies, school, family, and community environmental on physical activity and fitness among children and adolescents: a structural equation modeling study

BackgroundUnderstanding the impact of environmental factors on physical activity (PA) and physical fitness (PF) is crucial for promoting a healthy lifestyle among children and adolescents. This study examines how awareness of sports policies, school, family, and community environments influence PA and PF in Chinese youth.MethodsA cross-sectional study was conducted with 2747 children and adolescents (mean age 12.90 ± 2.49; 48.2% male) from 17 schools across five Chinese cities. Environmental factors were assessed via questionnaires, and PA levels were measured using the International Physical Activity Questionnaire-Short Form (IPAQ-SF). PF metrics, including BMI, waist-to-height ratio, grip strength, vertical jump, and 20-m shuttle run test (20-mSRT), were measured onsite. Structural Equation Modeling (SEM) was used to explore relationships between environmental factors and PA/PF outcomes.ResultsThe school environment scored highest (78.0 ± 9.5), while the community environment scored lowest (38.7 ± 18.0). Family environment positively influenced low-intensity PA (LPA) (β = 0.102, P < 0.001) but negatively affected moderate-to-vigorous PA (MVPA) (β = -0.055, P = 0.035). Community environment and awareness of sports policies positively impacted MVPA (β = 0.216, P < 0.001; β = 0.072, P = 0.009, respectively). Family environment positively influenced BMI reduction (β = -0.103, P < 0.001) but negatively affected grip strength (β = -0.063, P = 0.018). Community environment improved grip strength and 20-mSRT performance (β = 0.088, P = 0.002; β = 0.065, P = 0.027).ConclusionsSchool environments, despite high scores, do not significantly impact PA and PF. Community environments, though scoring lower, positively affect MVPA, grip strength, and 20-mSRT. Awareness of sports policies boosts MVPA, while family environments support LPA and BMI but are inversely related to MVPA and grip strength. Integrated strategies involving community infrastructure, family support, and policy awareness are essential for promoting active lifestyles among children and adolescents.

Parental stress interaction with environmental factors in childhood allergic asthma: Early life significance

Although stress and indoor/outdoor environments have been independently linked with asthma, their interactions on childhood physician-diagnosed allergic asthma (PDAA) remain undisclosed. We aimed to research the effect of parental stress and their interactions with indoor/outdoor environmental factors on childhood PDAA. A retrospective cohort study was undertaken of preschoolers at Changsha, a city in central China. We collected information on parental stress levels and various indoor environmental exposures, through questionnaire surveys. Outdoor air pollution exposure was obtained from ten ambient pollution monitoring stations in Changsha, and individual pollution exposure were calculated. Using logistic regression models, we assessed the individual and synergistic impacts of parental stress and indoor/outdoor factors on childhood PDAA. We found that preschoolers' PDAA showed a significant association with high parental gross annual income and psychological stress indicators including fatigue, headache, and inattention, with ORs = 2.10 (1.31–3.35), 1.38 (1.04–1.82), 1.33 (1.07–1.65), and 1.42 (1.14–1.76), respectively. Furthermore, in preschoolers with high parental economic stress, the association between indoor mold/moisture spots and PDAA was notably more pronounced. Significantly stronger association between outdoor SO2 exposure in the past year in preschoolers with socially stressed parents. Parental headache and inattention exacerbated the effects of PM2.5-10 and PM10 exposure during the 7-9th months before pregnancy and throughout the pregnancy on PDAA. Preschoolers with high parental psychological stress and high exposure to outdoor atmospheric pollutants showed an increased likelihood of developing PDAA. Our research suggests that both parental stress and its interaction with hazardous environments from indoor sources and atmospheric pollutants contribute to PDAA.