Chinese Cities Research Articles

Artificial intelligence and green development: Evidence from China

ABSTRACT Artificial intelligence plays a crucial role in addressing environmental challenges. Grounded in the theory of innovation diffusion, this study employs panel data from 286 prefecture-level cities in China over the period from 2012 to 2022 to investigate the impact and underlying mechanisms of artificial intelligence on green development. The findings reveal that artificial intelligence and green development exhibit a statistically significant U-shaped relationship. Green technological innovation serves as the primary mechanism through which artificial intelligence influences green development. Furthermore, the positive effect of artificial intelligence on green development is more pronounced in the eastern, northeastern, and coastal regions of China. Additionally, artificial intelligence has significantly contributed to economic growth, demonstrating a notable ‘reduction in emissions and enhancement of efficiency’.

Bridging gaps in ecological security: spatial conservation prioritization through balancing ecological features in a great Chinese city

Summary Megacities around the world are increasingly confronted with conservation and restoration bottlenecks due to the competing demands of urban expansion and environmental conservation. This study investigates conservation prioritization strategies for balancing biodiversity protection, ecosystem service (ES) supply and landscape connectivity in rapidly urbanizing Beijing. By employing spatially explicit modelling and prioritization scenario techniques, we identify spatially heterogeneous priority zones. We demonstrate that high-value areas for ES supply, particularly carbon storage and water regulation, concentrate primarily in Beijing’s north-western mountainous regions, covering c. 62% of the city’s area. Conversely, critical habitats for threatened species and key connectivity corridors are dispersed, with 22.89% of critical habitats located within urban built-up areas. Gap analysis reveals limited alignment between Beijing’s current ecological security patterns, with only 9.6% coverage of the identified top 10% conservation priority zones, especially within the metropolitan core. The study underscores significant trade-offs among different ecological objectives and multi-criteria conservation strategies. We propose an optimized conservation framework based on zonation analysis to guide targeted landscape planning decisions. This approach provides actionable insights for urban policymakers to achieve comprehensive sustainability, emphasizing the importance of protecting critical ecological areas in both urban and rural landscapes amid ongoing urban expansion.

Significant influence of oxygenated volatile organic compounds on atmospheric chemistry: a case study in a typical industrial city in China

Abstract. Oxygenated volatile organic compounds (OVOCs), an important subgroup of volatile organic compounds (VOCs), are emitted directly or formed secondarily through photochemical processes. They play a crucial role in tropospheric chemistry as ozone (O3) precursors. However, due to measurement limitations, the influence of OVOCs on O3 formation has often been underestimated. In this study, 74 VOCs (including 18 OVOCs) were measured at five representative stations (urban, suburban, industrial, upwind, and downwind stations) in Zibo, an industrial city in the North China Plain. The VOCs level in Zibo (44.6 ± 20.9 × 10−9) is in the upper-middle range (> 32 × 10−9) compared to previous studies conducted in most Chinese cities, with OVOCs contributing for 30.0 %–37.8 %. The average O3 formation potential in Zibo is 410.4 ± 197.2 µg m−3, with OVOCs being the dominant contributor (31.5 %–55.9 %). An observation-based model (OBM) was used to access the contributions of chemical production (RNetProd) and emissions/transport (REmis&Trans) to individual OVOCs. Daytime (08:00–18:00 LT) RNetProd is highest at the urban site (5.9 × 10−9 h−1), while nighttime REmis&Trans is most negative at the industrial site (0.76 × 10−9 h−1). Simulations without OVOC constraint overestimate OVOCs (42.1 %–126.5 %) and key free radicals (e.g., hydroperoxy radicals (HO2, 5.3 %–20.4 %) and organic peroxy radicals (RO2, 6.6 %–35.1 %)), leading to a 1.8 %–11.9 % O3 overestimation. This overestimation causes an underestimation of hydroxyl radicals (OH) (1.8 %–20.9 %) and atmospheric oxidizing capacity (3.5 %–12.5 %). These findings emphasize the importance of comprehensive OVOC measurements to constrain numerical models, especially in regions with dense anthropogenic emissions, to better reproduce atmospheric photochemistry, and to formulate more effective air pollution control strategies.

Reviewing the Efficacy of Urban Landscape Design Strategies in China: A Systematic Review

This study evaluates the ecological effectiveness of urban landscape designs in Chinese cities through systematic PRISMA review methodology that integrates 70 peer-reviewed articles concerning design since 2018. Three core dimensions about cultural integration and ecological functionality and landscape integration receive explicit assessment for ecosystem services including carbon sequestration potential along with urban cooling intensity reductions and stormwater retention capacity as well as biodiversity outcomes including habitat connectivity indices and species richness gains and climate resilience. Research indicates that culturally sensitive design improves regional identity together with social cohesion yet maintains a superficial approach because it does not develop community‑driven stewardship programs. Plant cover increases between 15 to 25 percent and habitat connections increase 30 percent while temperature decreases between 1 to 2 degrees Celsius and pollinator populations grow through ecological network approaches to urban development despite planning gaps and funding problems and implementation challenges from governance restrictions. Public spaces gain improved multifunctional character and aesthetic appeal when stakeholders implement landscape integration yet regulatory inconsistencies with stakeholder disputes reduce its effective implementation. The paper ends with recommendations for adopting ecological standard indicators in evaluations together with urban masterplan inclusion of macro-scale ecological planning both with stronger participatory governance and improved green space management systems. These findings present practicable guidance for enhancing biodiversity preservation and ecological stability together with ecosystem service delivery within urbanizing areas.

Artificial Intelligence Technology Applications and Energy Utilization Efficiency: Empirical Evidence from China

Although artificial intelligence (AI) serves as a core driver of the new round of technological transformation, its crucial role in improving energy utilization efficiency has not yet received sufficient attention. This analysis empirically explores how the application of AI technology influences energy utilization efficiency using panel data from Chinese cities over the period from 2008 to 2021. The following are the primary conclusions: (1) AI technology applications are able to enhance energy utilization efficiency, and the outcomes remain valid after extensive reliability tests have been conducted; (2) the investigation of the mechanism demonstrates that AI technology applications can optimize energy utilization efficiency through technological and scale effects; (3) environmental regulation and digital infrastructure serve as positive moderators of the impact of AI technology applications on energy utilization efficiency; and (4) a heterogeneity analysis shows that the positive impact of AI technology applications on energy utilization efficiency is more significant within resource-dependent cities, cities with non-traditional industrial foundations, and those with a strong emphasis on environmental protection. The application of AI technology significantly enhances energy efficiency, which is a finding that remains robust across multiple reliability tests.

Technical note: Reconstructing missing surface aerosol elemental carbon data in long-term series with ensemble learning

Abstract. Ground-based measurements of elemental carbon (EC) – classified under thermal–optical methods and considered a surrogate for black carbon – are essential for assessing air quality and evaluating climate impacts. However, data gaps caused by technical challenges impede comprehensive analyses of long-term trends. This study proposed an ensemble learning modeling method to address these challenges. The model used readily accessible ground observation air pollutant data as proxies for EC-related tracers, along with meteorological parameters, to enhance prediction accuracy. It integrated outputs from Gradient Boosting Regression Trees, eXtreme Gradient Boosting, and random forest models, combining them through ridge regression to produce robust predictions. We applied this approach to reconstruct hourly EC concentrations from 2013–2023 for four cities in eastern China, filling 45 %–79 % of missing data and improving prediction performance by 8 %–17 % compared to individual models. Over the 11-year period, EC exhibited an overall decline (−0.20 to -0.14µgm-3a-1), with a more significant decrease from 2013–2020 (−0.24 to -0.15µgm-3a-1). During this time, the average EC concentration in the four cities dropped from 3.26 to 1.59 µg m−3, followed by a noticeable slowdown in the rate of decline from 2020–2023 (−0.12 to -0.04µgm-3a-1). Additionally, a fixed emission approximation method based on ensemble learning was proposed to quantitatively analyze the drivers of long-term EC trends. The analysis revealed that anthropogenic emission controls were the predominant contributors, accounting for approximately 92 % of the changes in EC trends from 2013–2020. However, their influence weakened post-2020, contributing approximately 80 %. These findings highlighted that while China's Clean Air Actions implemented since 2013 have substantially reduced black carbon concentrations, sustained and enhanced strategies are still necessary to further mitigate black carbon pollution.

Association between physical activity and executive function of Chinese adolescents aged 13–18

ObjectiveThis study aimed to investigate the relationship between physical activity (PA) and executive function (EF) among adolescents in China.MethodsUsing a stratified cluster random sampling, we recruited 4,991 adolescents from 11 Chinese cities, including Urumqi, Lhasa, and Naqu etc. Participants completed execution function test and PA questionnaire survey. Data were analyzed using one-way ANOVA, Pearson correlation analysis, and 30-minute isotemporal substitution models (ISM) to assess the association between PA and EF.ResultsDaily moderate-to-vigorous physical activity (MVPA) time in both boys and girls was negatively correlated with inhibitory control RTs (r = −0.279, −0.173, P < 0.01), 2back-RTs (r = −0.367, −0.268, P < 0.01), and cognitive flexibility RTs (r = −0.283, −0.305, P < 0.01). Replacing 30 min of sedentary behaviour (SB) with MVPA was significantly shorter RTs in inhibitory control (P < 0.05), 2back tasks (P < 0.01), and cognitive flexibility (P < 0.01). Similarly, substituting 30-min of light physical activity (LPA) with MVPA led to reduced RTs in all three EF domains (P < 0.05 for inhibitory control; P < 0.01 for others). After adjusting for covariates, a U-shaped dose-response relationship emerged between MVPA duration and EF performance. Adolescents with 59.02–60.88 min/day of MVPA (the relatively high-level group) demonstrated the lowest RTs in inhibitory control, working memory, and cognitive flexibility (P < 0.05).ConclusionsAfter controlling for confounders, MVPA exhibited an inverted U-shaped relationship with inhibitory control, working memory, and cognitive flexibility. Optimal EF improvement was observed at 59.02–60.88 min/day of MVPA, suggesting this range may be most beneficial for adolescents’ executive function.

WATER NEWS: A field approach for sustainable detection of pathogens in wastewater

Abstract Wastewater surveillance is a critical tool in responding to the COVID-19 pandemic, yet traditional centralized methods are unsustainable due to their high costs and complex implementation requirements. Here we introduce Wastewater Analysis and Tracking for Epidemiological Recognition: Necessary Early Warning System (WATER NEWS), a field CRISPR–diagnostic approach designed for robust and cost-effective pathogen detection in wastewater treatment plants (WWTPs). WATER NEWS combined a one-pot assay, optimized reporter probe, on-site nucleic acid extraction, an improved freeze-drying process, and a user-friendly, low-cost, battery-operated device, thereby overcoming the limitations of field-deployable wastewater surveillance. In evaluations across 25 WWTPs in 10 Chinese cities, WATER NEWS achieved sensitivities exceeding 90% and specificities of 100% in SARS-CoV-2 detection from the influent and effluent samples of WWTPs. Notably, the system consistently detected SARS-CoV-2 presence within 20 days at a local WWTP. Economic analysis reveals that our approach achieved a 6.5-fold cost reduction compared to clinical testing and costs nearly half that of traditional wastewater surveillance methods, making it a viable and sustainable option for wastewater surveillance.

The Impact of Integrated Straw Utilization on Low‐Carbon Agricultural Development: Evidence From China

ABSTRACTIn recent years, the real environmental and economic impacts of China's comprehensive straw burning ban and utilization policy, as well as its mitigating effect on climate change challenges, have often been questioned. For this reason, this paper investigates the impact of pilot straw comprehensive utilization (SCU) policy on carbon emission intensity using panel data of 285 Chinese cities from 2012 to 2020. The analysis adopts the difference‐in‐differences (DID) method. The results show that, guided by subsidies, the SCU pilot policy enhances total carbon intensity while decreasing the emission intensity of another greenhouse gas, CH4, through banning burning and promoting more efficient utilization. In particular, SCU mitigates carbon intensity partially by reducing mechanized inputs and effectively by expanding the scale of crop cultivation. In addition, the SCU pilot policy leads to higher carbon intensity in northern cities, cities with low total grain output, poor counties, and areas with low investment in agriculture, forestry, and water conservancy. Therefore, to prevent the policy from deviating from the goal of green and low‐carbon agricultural development and effectively respond to climate change challenges, the effectiveness of the policy should be reassessed, and more appropriate measures should be implemented in areas with weaker economic and social foundations.

Consumer preferences for evaluative front-of-package nutrition labels: evidence from a choice experiment in China

This study examines consumer preferences, behaviors, and willingness to pay (WTP) for evaluative front-of-package (FOP) nutrition labels in China, with a particular focus on the role of information provision in influencing consumer behavior. A choice experiment was conducted involving ham sausages with different evaluative nutrition labels, including the Health Star Rating and Nutrition Score System, among 998 participants from five representative cities in China. The choice experiment also incorporates an information intervention to assess its impact on consumers’ choice. A random parameter logit model was employed to estimate consumers preferences and WTP for the different evaluative FOP labels. The results show that consumers exhibit a higher preference for ham sausages with evaluative FOP nutrition labels compared to those without any labels. The Nutrition Score System emerges as the most preferred labeling format. Furthermore, under information intervention, consumers exhibit the highest WTP for evaluative FOP labels, with 2.47 yuan for the Health Star Rating (124% premium) and 2.77 yuan for the Nutrition Score System (138% premium). These findings suggest a substantial demand among Chinese consumers for evaluative FOP nutrition labels and underscore the importance of providing related information to enhance consumer acceptance and promote healthier food-choice behavior.

A pressure-state-response framework incorporating static and dynamic considerations for assessing urban flood resilience

ABSTRACT Urban flooding represents a major long-standing and historical challenge that has become more severe in recent years due to the intensification of climate change and rapid urbanization. This study establishes a new comprehensive framework for assessing urban flood resilience based on a novel combination of the ‘pressure-state-response’ theory and through consideration of natural, social, economic and infrastructure dimensions. Through an analysis of both the static and the dynamic considerations, a more comprehensive evaluation system is developed. This new framework is then used to evaluate the flood resilience levels of three flood prone cities in China. The results show that Wuhan has the highest level of flood resilience, Nanchang ranks second, while Changsha has the lowest level of flood resilience. This study provides new insights to support the development of understanding of urban flood resilience at the macro scale and will be useful to policy makers and in shaping new strategic approaches to flood risk management.

Rural Renewable Energy Resources Assessment and Electricity Development Scenario Simulation Based on the LEAP Model

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO2/NOx will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results.

Knowledge based convolutional transformer for joint estimation of PM2.5 and O3 concentrations.

Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM2.5 and O3 by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function designed with the correlation between PM2.5 and O3 concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM2.5:RMSE = 6.136µg/m³, O3:RMSE = 8.250µg/m³) and the highest prediction accuracy (PM2.5:R2 = 0.923, O3:R2 = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.

The relationship between FDI quality, new quality productivity forces, and economic resilience in China’s Yangtze River Delta region: insights from an empirical spatial Durbin model

In the context of increasing natural disasters, volatile geopolitical dynamics, and rising anti-globalization trends—particularly in the post-pandemic era—enhancing economic resilience has become crucial. This study investigates how FDI quality and new quality productivity forces influence economic resilience across 41 cities in China’s Yangtze River Delta (2009–2022). We establish measurement systems for FDI quality, new quality productivity forces, and economic resilience, conducting empirical analyses across temporal and spatial dimensions. Three main findings emerge: First, significant spatial disparities exist, with eastern/northern regions outperforming western/southern areas in all three dimensions. While new quality productivity forces and resilience show upward trends with fluctuations, FDI quality exhibits stagnation and regional imbalances. Second, temporal analysis reveals a U-shaped relationship where FDI quality affects both new quality productivity forces and resilience, with new quality productivity forces demonstrating significant mediation effects. Third, spatial econometric results show an inverted U-shaped spillover effect of FDI quality on neighboring regions’ resilience. These findings provide empirical evidence on the mechanisms through which external investments (FDI) and internal innovation capabilities (new quality productivity forces) jointly shape regional economic resilience, offering valuable insights for policymakers to address development disparities and enhance risk resistance capacities in the new era.

Dynamics of regional knowledge creation and inter-city R&D collaboration in China: evidence from an empirical agent-based simulation model

The objective of this study is to simulate the dynamic relationships between regional knowledge creation and inter-city R&D collaboration in China. We contribute to the scarce literature using empirically driven agent-based simulation for modelling regional knowledge creation. We provide original evidence for China creating a better understanding of the underlying micro-dynamics that shape regional knowledge creation rather than on aggregate levels (countries or larger regions). We demonstrate that collaboration produces higher knowledge gains at the system level, but benefits differ across Chinese cities. Moreover, technology-specific knowledge gains from collaborations depend on place-based factors locally moderating collaboration benefits in specific technologies.

Exploring health challenges and coping strategies in young patients undergoing maintenance hemodialysis: a qualitative study

BackgroundMaintenance hemodialysis (MHD) presents significant physical, psychological, and social challenges for young patients, affecting their overall well-being and quality of life. There is a lack of qualitative research on the actual experiences and coping mechanisms of young people aged 18 to 45 years undergoing MHD, and understanding how these patients cope with the multidimensional impact of MHD is crucial for developing effective support strategies. This study aimed to explore the physical, mental, and social health challenges faced by young MHD patients and examine the coping strategies they employ to manage these difficulties.MethodsA qualitative study was conducted through semi-structured interviews with nine MHD patients in a tertiary hospital located in a new first-tier city in China. The interview data were analyzed using Colaizzi’s method with the aid of NVivo 14 (QSR International) software, and all patients’ information was anonymized to protect their privacy. ResultsWe found that the patients faced physical health problems such as fatigue, dizziness, and dry mouth as their treatments continued. In terms of mental health, depression, irritability, and low self-esteem were reported, as well as adverse reactions that also affected the patients’ mental well-being. The patients also experienced social adjustment problems, such as difficulties with role identity, barriers to employment, and reduced social participation. They reported using many coping strategies, including self-regulation and seeking outside help. Family and partner support also played an important role in the management of the patients’ psychological states.ConclusionsThis study identified complex and interrelated physical, psychological, and social challenges faced by young MHD patients and explored the coping strategies employed by them to cope with these challenges. By revealing the dynamic interplay between these domains, the findings suggest the development of personalized, multidimensional interventions to improve patient quality of life. The study provides new insights into the real-life experiences of young MHD patients and provides practical recommendations for key stakeholders, including healthcare providers, family members, and policy makers. The authors used AI assistance to draft this abstract, followed by thorough review and revision.Clinical trial numberNot applicable.

Prevalence and factors with potentially inappropriate prescribing among older outpatients with depression: a multicentre study across China.

Older outpatients with depression are at high risk for potentially inappropriate prescription (PIP). This investigation sought to determine the frequency and associated factors of PIP within Chinese older adults. This cross-sectional study used prescription data from older outpatients with depression from 90 hospitals in seven cities in China from January-December 2021. Risk factor identification for PIP employed multivariate logistic regression analysis. Trend assessment was performed through joinpoint regression to calculate the average annual percent change. The prevalence of PIP reached 50.42%. The top five PIM were alprazolam, clonazepam, olanzapine, lorazepam, estazolam. The prevalence of PIP decreased from 51.56 to 50.99% (average annual percent change = -0.335%). Logistic regression demonstrated that tertiary-level hospital (odds ratio (OR) = 1.215; 95% confidence interval (CI) = 1.100, 1.342, P < 0.001), department of psychiatry (OR = 1.958; 95% CI = 1.855, 2.067, P < 0.001), age ≥80 (OR = 1.069; 95% CI = 1.016, 1.124, P = 0.01), more diseases (OR = 1.209; 95% CI = 1.092, 1.339, P < 0.001), polypharmacy (OR = 1.672; 95% CI = 1.541, 1.814, P < 0.001) exhibited positive links to PIP among older outpatients suffering from depression. This investigation revealed that the occurrence of PIP in older outpatients with depression is high in China.

Unveiling intra-urban complexity and identifying urban cores through the lens of living structure using point-of-interest data

ABSTRACT The intra-urban space is essentially an organized structure of complexity that consists of centers at different hierarchical levels or scales. This kind of complexity can be measured from the perspective of living structure inspired by Christopher Alexander’s organic view of space. Previous studies have revealed that the living structure can be used to characterize the structural complexity of photos, satellite images and urban systems. However, its potential to measure intra-urban complexity using massive point-based datasets remains underexplored. This study introduces a recursive method to analyze intra-urban complexity using massive point-of-interest (POI) data. By recursively decomposing urban substructures, we quantified structural complexity based on the livingness of substructures using a unified criterion. Our findings indicate that cities or intra-urban areas with higher livingness exhibit greater structural complexity. The resulting substructures exhibit power-law distributions and align closely with human activity patterns across multiple spatial scales in four large cities in China. Remarkably, intra-urban structures can be effectively understood with no more than four levels of recursive decomposition. Furthermore, we found that the urban centers or core areas can be effectively located using the proposed method. These insights underscore the potential of living structure as a framework for understanding and measuring the organized complexity of intra-urban spaces.

Impact of enhancing national autonomous innovation demonstration zones on cities’ efficiency in technological innovation

National independent innovation demonstration zones (NIIDZs) play a crucial role in scientific and technological innovation (STI). However, there have long been disputes regarding their ability to continuously increase urban STI efficiency, the paths through which they generate innovation-driven effects, and their spatial effects. This study uses the panel data of 274 cities in China from 2006 to 2020 and applies various methods including the multiperiod difference-in-differences model, quantile model, and spatial Durbin model to systematically explore the impact of NIIDZs on urban STI efficiency. The research findings are as follows: the positive impact of NIIDZs on urban STI efficiency begins to emerge in the fourth year after their establishment and persists. The spatial effect is distance dependent, suppressing the STI efficiency of surrounding cities within 110 kms while promoting that of areas at a distance between 110 and 410 kms, with the strongest effect occurring at 360 kms. The mechanism analysis shows that NIIDZs affect urban STI efficiency through factor agglomeration, innovation environment optimization, and industrial structure transformation and upgrading. This study provides important policy insights for fully harnessing the innovation-driven effect of NIIDZs to increase urban STI efficiency.

Pollution profiles and human health risk assessment of BPA analogues in seafood from a Chinese coastal city.

Pollution profiles and human health risk assessment of BPA analogues in seafood from a Chinese coastal city.