Major Cities In China Research Articles

Analysis of the Impact of Hema Supermarket's Innovation Strategy on Marketing Effectiveness

Abstract: Hema Fresh is an innovative online and offline integrated new retail fresh supermarket. In recent years, Hema Xiansheng has been popular with consumers in major cities in China. Adhering to the concept of "freshness, convenience, quality and Internet", it is committed to providing consumers with fresh, high-quality food and convenient shopping experience. This paper aims to analyse the impact of Hema supermarket's innovative approach on its marketing effectiveness through theoretical analysis and comparative research. Through research and analysis, the innovation strategy of Hema Supermarket has a positive impact on its marketing effectiveness and is a key factor for its success. In addition, this article explores how Hema Supermarket can succeed and summarizes its marketing advantages, highlighting strengths and avoiding weaknesses, providing references for similar enterprises.

Matching analysis between the heating/cooling capacity of PVT heat pumps and the heating/cooling demand of residential buildings across various regions in China

PVT-HP (Photovoltaic-Thermal Heat Pump) is a novel type of energy saving technology. Existing researches on PVT-HP technology focuses only on its operating performance under some specific conditions, ignoring the matching of its energy supply capacity with the energy demand of buildings, which is very important for its engineering design and application. In this paper, the matching relationship between the heating/cooling capacity of the PVT-HP system and the heating/cooling demand of residential buildings in major cities of China has been analyzed. To achieve this goal, a novel prediction model used to calculate the heating/cooling performance of the PVT-HP system has been proposed. And the daily heating guarantee rate and daily cooling guarantee rate have been put forward to evaluate the matching relationship. Based on the evaluation indexes, the applicable matching relationship between the installed capacity of the PVT-HP system and the heating/cooling area of residential buildings has been analyzed. The result of case study shows that for the selected cities of Shenyang, Beijing and Wuhan, the heating/cooling area of residential building for PVT-HP system with installed capacity of 1hp should not exceed 17m2, 31m2 and 25m2, respectively. Further matching analysis of major cities in China shows that the PVT-HP system is more suitable for application in hot summer and cold winter zones of China, in where the suggested heating/cooling area for the PVT-HP system with installed capacity of one horse power is within the range of 30m2 to 37m2.

Does rising land price promote the circulation industry development? An analysis of internet-based moderating effects

With the rapid development of the Internet, digital technology is increasingly integrating into the modern circulation system. The influence mechanisms of land prices on the development of the circulation industry are gradually transforming. Using data from 104 major cities in China from 2003 to 2021, this study uses the panel two-way fixed effects model, moderating effects model and threshold regression method to examine the mechanisms by which land prices affect the circulation industry development and the moderating role of Internet development. The results show that elevated land prices significantly stimulate the growth of the circulation industry, leading to an expansion in the circulation industry scale, optimization of its structure, and enhancement of its facilities. The findings remain robust even after addressing endogeneity concerns and conducting various robustness tests. Moreover, the impact of high land prices on the circulation industry is more pronounced in small and medium-sized cities group as well as central region cities group. Real estate investment and industrial structure upgrading are the key channels through which high land prices promote the development of the circulation industry. Additionally, Internet development has a positive moderating effect on the promotion that high land prices stimulate the development of the circulation industry. Notably, only when Internet development surpasses a certain threshold that the promotional effect of land prices on the circulation industry development be fully realized. Based on the above conclusions, the government should continue to deepen the market-oriented reform of land elements, implement precise industrial policy support for the circulation industry and promote circulation digital transformation.

직원경험이 혁신 행동에 미치는 영향에 대한 연구 : 심리적 임파워먼트의 매개효과를 중심으로

This study investigates the impact of employee experience (EX) on innovative behavior, with a particular focus on the mediating role of psychological empowerment. Data was collected from 450 employees across various industries in eight major cities in China, including Beijing, Shanghai, and Shenzhen. The survey employed a five-point Likert scale to measure employee experience across three dimensions: physical environment, cultural environment, and technological environment. Structural equation modeling was used to analyze the data. The results indicate that the three dimensions of Employee Experience (physical environment, cultural environment, and technological environment) have both direct and indirect effects on innovative behavior through Psychological Empowerment. First, each dimension of Employee Experience was found to have a significant positive impact on innovative behavior. Specifically, improvements in physical, cultural, and technological environments lead to more innovative behavior among employees. Second, Psychological Empowerment positively influences innovative behavior and mediates the relationship between Employee Experience and innovative behavior. More specifically, Psychological Empowerment partially mediates the relationship between the physical and cultural environments of Employee Experience and innovative behavior. This suggests that when employees work in a pleasant environment and feel a sense of belonging to the organizational culture, their Psychological Empowerment increases, leading them to engage more actively in innovative behavior. Third, the technological environment does not have a significant impact on Psychological Empowerment. This result indicates that simply introducing the latest technology may not enhance employees' Psychological Empowerment, highlighting the need for training and support systems to effectively utilize technology. This study underscores the importance of Employee Experience in understanding employee behavior, particularly innovative behavior. By examining the sub-dimensions of Employee Experience, the study extends existing research on the relationship between Psychological Empowerment and innovative behavior, providing a deeper understanding of their interactions. It demonstrates that improving Employee Experience can promote innovative behavior. The study also discusses its limitations and suggests directions for future research.

Parental involvement and children’s L2 learning motivation and engagement: A person-centered approach

Research has consistently demonstrated that parental involvement plays a key role in children’s early second language (L2) development. However, little is known about how different aspects of parental involvement in children’s L2 learning are configured and how these differentiated combinations of parental involvement are associated with children’s L2 motivation and engagement. Moving beyond prior studies, which typically focused on the effects of single aspects of parental involvement, this study utilized a person-centered approach to examine the configuration patterns of various indicators of parental involvement in children’s L2 learning (i.e. parental autonomy support, psychological control, and behavioral control). Data were collected from a sample of 702 seventh graders ( Mage = 13.09, SD = 1.64; 50.14% girls) in two major cities in China. Four distinct profiles (i.e. Authoritative, Neglectful, Control-centered, and Intrusive) were identified. Children’s L2 learning motivation and engagement varied across these profiles. These more nuanced findings shed light upon the heterogeneous nature of various parental involvement in children’s L2 learning and carry significant practical implications for both parents and practitioners aiming to foster a more supportive environment for children’s L2 learning.

Motivation in learning mathematics among high school students in Ningbo, China

Motivation plays a crucial role in enhancing students' achievement in mathematics, as motivated students are more likely to engage actively with the subject. However, in China, there is a concerning trend of declining motivation among students toward mathematics, with many exhibiting avoidance behaviors. Despite Ningbo's significance as a major city in China, there is a lack of studies and surveys addressing students' motivation in mathematics learning. This study addresses this gap by employing a questionnaire based on Keller’s ARCS (Attention, Relevance, Confidence, Satisfaction) model, which has been proven effective in measuring students’ motivation. The survey, conducted among 384 high school students in Ningbo, China, aimed to assess their motivation levels in mathematics learning. Descriptive statistical analysis was performed using SPSS Statistics 29 to calculate the mean and percentage for each item. The results revealed that the motivation level among high school students in Ningbo is notably low. Two significant factors contributing to this low motivation were identified: students’ lack of confidence due to the perceived difficulty of mathematics and the belief that mathematics is not closely related to real life.

DCAI-CLUD: a data-centric framework for the construction of land-use datasets

A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The Gini index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K.

Parents’ and Teachers’ Perceptions of the Transportation Experiences of Youth With Intellectual and Developmental Disabilities

Enhancing transportation skills is crucial in promoting quality of life for youth with intellectual and developmental disabilities (IDD). To learn about transportation skill development and improve the transportation experiences of youth with IDD in China, we conducted in-depth interviews with special education teachers and parents of transition-age youth with IDD (i.e., 23 teachers and 19 parents) from five major cities in China. The purpose of this study was to explore the transportation experiences of youth with IDD through parents’ and teachers’ perspectives. Adopting the constructivist grounded theory method, three main themes were identified: (1) the need to develop transportation skills, (2) the practices for supporting transportation, and (3) the challenges of transportation for youth with IDD in China. The findings ascertain the importance of travel training to promote community inclusion of transition-age youth with IDD and disclose the helpful practices and existing barriers for improving youth’s transportation skills. Limitations and implications for research, policy, and practice are also described.

Climate Classification for Major Cities in China Using Cluster Analysis

Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China’s traditional geographical division along the Qinling–Huaihe River line.

Carbon emissions forecasting based on temporal graph transformer-based attentional neural network

In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency “energy-electricity-carbon” integrated dynamic emission factor can be achieved. Through the spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making.

Older Adults' Exposure to Food Media Induced Unhealthy Eating during the COVID-19 Omicron Lockdown? Exploring Negative Emotions and Associated Literacy and Efficacy on Shanghainese.

The COVID-19 pandemic, propelled by the highly transmissible Omicron variant, had a global impact and significantly affected Shanghai, a major city in China. This study investigates how food media exposure influenced unhealthy eating habits among older adults during the COVID-19 lockdown in Shanghai, focusing on the roles of negative emotions, food literacy, health consciousness, and eating self-efficacy. The random sample comprised 400 individuals aged ≥50 years who lived in Shanghai from March to June 2022. A path and correlation analysis was performed. The exposure of older adults to food media resulted in the acceleration of unhealthy eating. The relationship was significantly exacerbated by food literacy and negative emotions. In contrast, eating self-efficacy and health consciousness effectively countered the media. The enhanced pathway from food-related media influence to eating habits through negative emotions or self-efficacy towards health awareness and food literacy showed significant effects. The findings provide insights for future research and public health strategies. Importantly, this study has practical significance for media professionals, public health decision-makers, and healthy food businesses regarding how to enhance older adults' cognition to respond to unhealthy eating crises.

Perceptible landscape patterns reveal invisible socioeconomic profiles of cities

Urban landscape is directly perceived by residents and is a significant symbol of urbanization development. A comprehensive assessment of urban landscapes is crucial for guiding the development of inclusive, resilient, and sustainable cities and human settlements. Previous studies have primarily analyzed two-dimensional landscape indicators derived from satellite remote sensing, potentially overlooking the valuable insights provided by the three-dimensional configuration of landscapes. This limitation arises from the high cost of acquiring large-area three-dimensional data and the lack of effective assessment indicators. Here, we propose four urban landscapes indicators in three dimensions (UL3D): greenness, grayness, openness, and crowding. We construct the UL3D using 4.03 million street view images from 303 major cities in China, employing a deep learning approach. We combine urban background and two-dimensional urban landscape indicators with UL3D to predict the socioeconomic profiles of cities. The results show that UL3D indicators differs from two-dimensional landscape indicators, with a low average correlation coefficient of 0.31 between them. Urban landscapes had a changing point in 2018–2019 due to new urbanization initiatives, with grayness and crowding rates slowing, while openness increased. The incorporation of UL3D indicators significantly enhances the explanatory power of the regression model for predicting socioeconomic profiles. Specifically, GDP per capita, urban population rate, built-up area per capita, and hospital count correspond to improvements of 25.0%, 19.8%, 35.5%, and 19.2%, respectively. These findings indicate that UL3D indicators have the potential to reflect the socioeconomic profiles of cities.

Temporal Evolution, Oscillation and Coherence Characteristics Analysis of Global Solar Radiation Distribution in Major Cities in China’s Solar-Energy-Available Region Based on Continuous Wavelet Transform

The majority of the energy required for human survival is derived either directly or indirectly from solar radiation, thus it is important to investigate the periodic fluctuations in global solar radiation over time. In this study, six cities—Harbin, Shenyang, Beijing, Shanghai, Wuhan, and Guangzhou—located in the utilizable areas of solar energy in China, were selected, and the periodicity of the daily global solar radiation of these six cities with time were investigated by means of wavelet power spectrum analysis. Furthermore, Harbin, which has the lowest monthly average of global solar radiation in the cold of winter, was selected to explore the temporal evolution relationship between global solar radiation and other meteorological factors, and air quality factors by wavelet coherence analysis. Among the meteorological factors, the correlation between global solar radiation and sunshine duration exhibits the highest level of consistency. Global solar radiation demonstrates a positive association with atmospheric temperature/wind speed/precipitation over the annual cycle. Conversely, it displays a negative correlation with atmospheric pressure during this time frame. Additionally, on shorter timescales, global solar radiation is negatively correlated with air humidity and precipitation. Among air quality factors, the relationship between global solar radiation and CO/NO2/O3/PM2.5/PM10/SO2 primarily manifests in the broader annual cycle time scale. Within this context, global solar radiation shows a positive correlation with O3, while displaying negative associations with the other five air quality indicators. These findings contribute to urban environmental planning and the effective management and utilization of solar radiation, thereby providing valuable insights to guide the future development of cross-regional comprehensive energy utilization projects under diverse climatic and geographical conditions. Additionally, they serve as a reference for subsequent research aimed at enhancing the accuracy of global solar radiation prediction models.

Annual and Seasonal Dynamics of CO2 Emissions in Major Cities of China (2019–2022)

To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies.

Mapping urban green space of the 36 major cities in China using ZY-3 satellite images based on a feature robust to low-brightness pixels

Abstract. Urban green spaces (UGS), integral to modern urban life, play a pivotal role in shaping ecological, low-carbon, resilient, and livable cities. Understanding the microstructure and macro patterns of UGS is necessary but limited. In this context, we investigated the spatial patterns in the 36 major cities in China using Ziyuan-3 remote sensing images. We used a method for extracting UGS suitable for complex urban environments, and then selected 5 landscape pattern indices such as percent of landscape and patch density to analyse the spatial pattern of UGS in the 36 cities. The results show that: (1) The overall accuracy, recall, F1 value, and intersection over union of the extraction results of UGS were 94.62%, 94.11%, 94.36%, and 89.33% respectively; (2) Overall, the green space rate of the 36 cities exhibited a spatial distribution pattern of “high in the east and low in the west, low in the north and high in the south”; (3) Variations among cities were weak in terms of patch shape, but significant in patch density. The findings of this study cater to the escalating demands of urban planning and management.

Research of multimodal transport potential of major cities in China based on the satellite remote sensing

Abstract. By two or more modes of transportation to connect with each other and complete the transport process, multimodal transport could significantly improve transport efficiency, reduce costs, while promote energy conservation and emission reduction, compared with the traditional single mode of transport. Therefore, the multimodal transport development reflects the level of national or regional economic development to some extent. In this study, multimodal transport potential index was built to evaluate the possibility of multimodal transport. Remote sensing images from satellites were used to extract roads, railway stations and airports. A method was proposed to derive the road-rail transport potential index and road-air transport potential index, and then for the analysis of multimodal transport potential in 36 cities. Results show that there is a big gap among 36 cities on the development potential of road-rail and road-air transport. Compared with the road-rail transport potential index, the distribution of road-air transport potential index is more consistent among and within the seven regions. Index values of megacity and super cities are much higher than type I and II big cities. Moreover, in the 36 cities, there is a positive correlation between the multimodal transport potential index and GDP. The research results could be used to quantitatively evaluate the development of transport infrastructure, consequently provide more solid and reliable basic information for the planning, design and scheme adjustment of multimodal transport, thus comprehensively improve the efficiency and quality of multimodal transport, serve the territorial spatial planning.

Trends and prescribing patterns of antimigraine medicines in nine major cities in China from 2018 to 2022: a retrospective prescription analysis

BackgroundThe objective of this study was to investigate the trends and prescribing patterns of antimigraine medicines in China.MethodsThe prescription data of outpatients diagnosed with migraine between 2018 and 2022 were extracted from the Hospital Prescription Analysis Cooperative Project of China. The demographic characteristics of migraine patients, prescription trends, and corresponding expenditures on antimigraine medicines were analyzed. We also investigated prescribing patterns of combination therapy and medicine overuse.ResultsA total of 32,246 outpatients who were diagnosed with migraine at 103 hospitals were included in this study. There were no significant trend changes in total outpatient visits, migraine prescriptions, or corresponding expenditures during the study period. Of the patients who were prescribed therapeutic medicines, 70.23% received analgesics, and 26.41% received migraine-specific agents. Nonsteroidal anti-inflammatory drugs (NSAIDs; 28.03%), caffeine-containing agents (22.15%), and opioids (16.00%) were the most commonly prescribed analgesics, with corresponding cost proportions of 11.35%, 4.08%, and 19.61%, respectively. Oral triptans (26.12%) were the most commonly prescribed migraine-specific agents and accounted for 62.21% of the total therapeutic expenditures. The proportion of patients receiving analgesic prescriptions increased from 65.25% in 2018 to 75.68% in 2022, and the proportion of patients receiving concomitant triptans decreased from 29.54% in 2018 to 21.55% in 2022 (both P < 0.001). The most frequently prescribed preventive medication classes were calcium channel blockers (CCBs; 51.59%), followed by antidepressants (20.59%) and anticonvulsants (15.82%), which accounted for 21.90%, 34.18%, and 24.15%, respectively, of the total preventive expenditures. Flunarizine (51.41%) was the most commonly prescribed preventive drug. Flupentixol/melitracen (7.53%) was the most commonly prescribed antidepressant. The most commonly prescribed anticonvulsant was topiramate (9.33%), which increased from 6.26% to 12.75% (both P < 0.001). A total of 3.88% of the patients received combined therapy for acute migraine treatment, and 18.63% received combined therapy for prevention. The prescriptions for 69.21% of opioids, 38.53% of caffeine-containing agents, 26.61% of NSAIDs, 13.97% of acetaminophen, and 6.03% of triptans were considered written medicine overuse.ConclusionsMigraine treatment gradually converges toward evidence-based and guideline-recommended treatment. Attention should be given to opioid prescribing, weak evidence-based antidepressant use, and medication overuse in migraine treatment.

Balancing Wellbeing and Responsibility: CSR’s Role in Mitigating Burnout in Hospitality under UN-SDGs

This study investigates the effect of Corporate Social Responsibility (CSR) on burnout among employees in China’s hospitality industry, highlighting wellness as an essential driver of success and its correlation to the United Nations Sustainable Development Goals. It looks at CSR’s impact on stress, with employee happiness and resilience acting as mediators, whereas altruism is supposed to act as a moderator. This study collected data from various hospitality organizations across major cities in China over three phases through well-known scales. The findings of structural equation modeling show a negative relationship between CSR and burnout with the mediating effects of happiness and resilience moderated by altruism. The above findings highlight how CSR can reduce worker fatigue while creating a positive work environment within the hospitality sector. This study enriches theoretical knowledge and practical strategies for enhancing employee wellbeing, emphasizing the role of strategic CSR in creating a more sustainable and productive hospitality sector.

Consumer preference for sustainability labels in the context of China

Sustainability Label (SL) have emerged as an important product attribute in recent decades, and have evolved into various types as products with social, environmental, and economic benefits have become more prevalent in the marketplace. With the information of SL in products, consumers are encouraged to embrace environmental sustainability principles and to make environmentally sustainable choices and actions. SL helps alignment between consumers and the industry by enhancing consumers’ nderstanding of company’s act, and serve as an effective marketing message. However, there is limited research on consumer preferences for different types of sustainable labels or benefits. This study addresses this knowledge gap by applying choice experiment method to assess consumers’ choice behavior for products associated with various SL. Choice experiment designs are separately developed for two most relevant sustainable products (i.e. soymilk and EV) in China. A stated preference method (SPM) consumer survey was conducted in 2022 across six major cities in China, namely Beijing, Shanghai, Guangzhou, Shenzhen, Chengdu, and Xi’an, and a total of 840 valid responses were collected for assessing multinomial logit (MNL) model. Findings show that Chinese consumers prefer SL with environmental benefits, foreign COO/Brand in consuming soymilk, and prefer SL with employee friendliness, domestic COO/Brand for Electric Vehicle (EV). These findings provide insights for marketers and researchers Chinese consumers’ preference for specific SL and brand for two selected product categories.

Spatial patterns and driving forces of urban vegetation greenness in China: A case study comprising 289 cities

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.