Influencing Factors In China Research Articles

Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

ObjectivesThe purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China, from January to October 2020. MethodsAbout 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases. ResultsThe distribution of COVID-19 in China tends to be stable over time, with spatial correlation and prominent clustering regions. Spatio-temporal scanning analysis showed that most COVID-19 high-incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR=491.57) which was 491.57 times the aggregation risk of other regions. Among the meteorological variables, the daily average temperature, wind speed, precipitation, and new COVID-19 cases were negatively correlated. The air pollution concentration and migration index were positively correlated with new confirmed cases, and the government response strict index was strongly negatively correlated with confirmed COVID-19 cases. ConclusionsEnvironmental temperature has a certain inhibitory effect on the transmission of COVID-19; the air pollution concentration and migration index have a certain promoting effect on the transmission of COVID-19. The strict government response index indicates that the greater the intensity of government intervention, the fewer COVID-19 cases will occur.

Economic growth of green agriculture and its influencing factors in china: Based on emergy theory and spatial econometric model

Based on emergy theory, this paper calculates the agricultural green GDP of China and its provincial units from 2003 to 2018. Following this, the spatial distribution characteristics and influencing factors of the economic growth of green agriculture in China are studied using the spatial analysis method. The results show that: (1) Compared with the traditional GDP derived from agriculture, the per capita green agriculture GDP growth in China is relatively slow, and the proportion of green agriculture GDP to the traditional GDP from agriculture is between 85 and 91%, which shows a downward trend. (2) The economic growth of China's green agriculture manifests significant spatial agglomeration, with gradually growing effects. (3) The per capita GDP of green agriculture has not broken the overall pattern of high economic growth in China’s eastern regions and low growth in the western. According to the results, the major cause of this spatial pattern lay in the higher efficiency of agricultural production in the eastern, economically more developed areas, and the relatively less efficient mode of production in the western areas. (4) A region’s level of economic development, technological innovation, infrastructure investment and openness were found to have a positive impact on the growth of green agriculture within it. In previous research, emergy methods have not been applied to study the economic sectors in China, the largest developing country in the world, especially in the agricultural sector. Only few studies report on the analysis of spatial characteristics and factors influencing the growth of green agriculture. The agricultural green GDP was calculated based on the emergy analysis method. The temporal and spatial characteristics, as well as the driving factors of green agriculture GDP, were analyzed under the full consideration of the spatial correlation, which contributes to the theoretical explanation of spatial agglomeration of economic factors in spatial economic theory. The conclusions provide a theoretical and practical basis for accurate evaluation of the green growth of China's agricultural economy, optimization of the spatial structure of the country’s green agricultural economy and coordination of the development of green agriculture in different regions.

Research progress on indoor formaldehyde pollution and its influencing factors in China, a Review

China’s economic development accelerates the process of urbanization, accompanied by formaldehyde pollution in indoor environment. we summarized the recent research on the pollution and impact factor of indoor formaldehyde in China, and elaborated the shortcomings of the existing research and the future research direction from the perspective of source emission, environmental fate and human health effects.

Assessing urbanization efficiency and its influencing factors in China based on Super-SBM and geographical detector models.

Improving urbanization efficiency (UE) has gradually become an important requirement for the development of new-type urbanization. In this paper, first, we applied the Super-SBM model with undesirable outputs and Malmquist index to evaluate the UE of 30 provinces in China from 2006 to 2016 based on static and dynamic perspectives. Second, influencing factors were explored using the novel geographical detector model. The results show that China's UE was not high overall and was increasing slowly. Regionally, the UE presented a high-to-low spatial evolution from east to west. Technical change was the main reason for the slow growth in efficiency, and undesirable outputs (pollutant emissions) were the key to efficiency loss. Over the study period, GDP per capita and urbanization rate had the greatest determining power on the UE, but their determination power was declining. Meanwhile, the impact of research and development (R&D) and foreign direct investment (FDI) on UE became increasingly important. Finally, some suggestions for improving the urban ecological environment and UE in China are discussed.

The characteristics of ICU physical restraint use and related influencing factors in China: a multi-center study.

The use of physical restraint has been a common phenomenon in clinical practice. However, the current status of physical restraints in intensive care unit (ICU) patients and related potential factors in China remains unclear. We aimed to evaluate the status of physical restraint use of patients in ICU, and to identify the potential factors affecting the physical restraint use. Patients in 16 ICUs of five hospitals from September 15 to October 15, 2019 were included. The ICU Patients' Physical Restraint Evaluation Scale was used for survey investigation. The basic information, the behavioral, treatment and muscle strength level with 35 detailed items were collected and evaluated. Multivariate logistic regression analyses were performed to identify the potential risks of physical restraint use. A total of 386 ICU patients were included, the incidence of physical restraint use was 59.07%. The incidence of physical restraint uses in patients with irritability, unconsciousness and disorientation, Richmond agitation-sedation scale (RASS) ≥2 or -3< RASS <2 were significantly higher that other conditions (all P<0.05); the incidence of physical restraint uses in patients with continuous renal replacement therapy (CRRT), temporary pacemaker, intracranial pressure (ICP) monitor, invasive blood pressure monitoring, tracheotomy, central venous catheter (CVC), extracorporeal membrane oxygenation (ECMO), peripheral intravenous infusion were significantly higher that other conditions (all P<0.05); the male gender, mechanical ventilation, retained catheters or tubes with level II or irritability were the independently influencing factors for the physical restraint use (all P<0.05). The incidence of physical restraint use is rather high in ICU, clinical attentions are needed especially for those patients with male gender, mechanical ventilation, retained catheters or tubes with level II or irritability.

Public Attitudes towards Intimate Partner Violence against Women and Influential Factors in China and Spain

Intimate partner violence against women is a social problem affecting the rights of women in different countries. The present study aimed to compare the public attitudes toward intimate partner violence against women and their influencing factors in China and Spain. A sample of 506 participants completed questionnaires related to attitudes toward intimate partner violence against women. Chinese participants demonstrated less awareness of the existence and seriousness of the issue, but more proactive attitudes than Spanish participants did. We also found that culture, gender, and age affected these attitudes directly and indirectly through gender equality attitudes. Our findings suggest that promotion of legal reforms can improve social awareness and gender equality attitudes, which in turn changes public attitudes toward intimate partner violence against women, while traditional gender roles and patriarchal society lead to cultural legitimization of the violence, resulting in remained conservative attitudes.

Visual outcome of cataract surgery and its influencing factors in China

Objective: To explore the visual outcome of cataract surgery and its influencing factors in China. Methods: A case-control study. The information of 2 078 479 patients who underwent cataract extraction of 31 provinces (municipalities and autonomous regions) recorded in the National Cataract Surgery Information Reporting System during January to December, 2017 were analyzed. The postoperative visual outcomes of 3 days after surgery and 3 months after surgery were analyzed. The effects of age, gender, preoperative corrected visual acuity, past medical history, and surgical methods on the results of cataract surgery were analyzed. Surgery effect was divided into ideal effect, moderate effect and poor effect according to the best corrected visual acuity after operation. Wilcoxon rank sum test and multinomial logistic regression model were used for statistical analysis. Results: There were 1 197 942 female patients (57.64%) and 880 537 male patients (42.36%) in all 2 078 479 cases. The average age was (69±11) years old. The main type of cataract was age-related (1 928 440 cases; 92.78%). There were 1 608 385 cases, 1 126 961 cases and 389 020 cases with complete corrected visual acuity before surgery, 3 days after surgery and 3 months after surgery, respectively. The median difference of corrected visual acuity of preoperative versus three days after surgery, and preoperative versus three months after surgery were -0.48, -0.52 respectively (after logarithm of the minimum angle of resolution conversion). The interquartile ranges were 0.60 and 0.73 respectively. The differences were statistically significant (Z=551 599.30, 206 815.35; both P<0.01). Multivariate analysis showed that postoperative complications (moderate effect: OR=0.50, 95%CI: 0.46 to 0.55; ideal effect: OR=0.31, 95%CI: 0.28 to 0.33), preoperative pupil abnormal (moderate effect: OR=0.52, 95%CI: 0.44 to 0.61; ideal effect: OR=0.55, 95%CI: 0.47 to 0.63), preoperative intraocular pressure abnormalities (moderate effect: OR=0.86, 95%CI: 0.79 to 0.93; ideal effect: OR=0.45, 95%CI: 0.42 to 0.49), history of hypertension (moderate effect: OR=0.90, 95%CI: 0.85 to 0.95; ideal effect: OR=0.88, 95%CI: 0.83 to 0.93) and history of diabetes (moderate effect: OR=0.86, 95%CI: 0.80 to 0.93; ideal effect: OR=0.92, 95%CI: 0.86 to 0.99) were predictors of worse outcome. Good preoperative corrected visual acuity (0.2 to 0.4 for moderate effect: OR=2.20, 95%CI: 2.06 to 2.34; for ideal effect: OR=5.25, 95%CI: 4.94 to 5.59; 0.5 to 0.7 for moderate effect: OR=1.08, 95%CI: 0.96 to 1.21; for ideal effect: OR=6.35, 95%CI: 5.69 to 7.08; above 0.8 for moderate effect: OR=0.73, 95%CI: 0.60 to 0.89; for ideal effect: OR=6.58, 95%CI: 5.51 to 7.86), small incision extracapsular extraction (moderate effect: OR=3.19, 95%CI: 2.35 to 4.33; ideal effect: OR=6.79, 95%CI: 5.13 to 8.97) and phacoemulsification (moderate effect: OR=2.12, 95%CI: 1.60 to 2.81; ideal effect: OR=3.34, 95%CI: 2.59 to 4.32) were predictors of better outcome. Conclusions: Visual acuity of patients in China improve significantly after cataract surgery. However, some patients still suffer moderate visual impairment. The effect of surgery can be affected by many factors, such as preoperative corrected visual acuity, preoperative intraocular pressure, preoperative pupil status, surgical approach and postoperative complications, etc. (Chin J Ophthalmol, 2021, 57: 63-70).

On the price volatility of steel futures and its influencing factors in China

Steel futures have the function of price discovery and hedging. Steel related enterprises can judge the hedging strategy through the direction of steel futures price volatility, and reasonably avoid the risk brought by price volatility. Therefore, it is particularly important to study steel futures price volatility and its influencing factors. Because steel futures in China have characteristics of peak and rear tail aggregation, the paper constructs Model of GARCH (1,1) to make positive analysis of futures price volatility and its influencing factors of deformed steel bars and hot rolled coils, and the following conclusions have been drawn: (1) The volume and open interest of deformed steel bars have very significant explanatory ability to futures price volatility of deformed steel bars; (2) The volume and open interest of hot rolled coils also have very significant explanatory ability to futures price volatility of hot rolled coils; (3) The sustainable capacity of the price volatility of deformed steel bars and hot rolled coils is relatively small; (4) Iron ore price have no obvious explanatory ability to futures price volatility. Finally, some managerial implications and suggestions are derived from the analysis of the proposed model.

Study on the spatial correlation between traditional villages and poverty-stricken villages and its influencing factors in China

Study on the spatial correlation between traditional villages and poverty-stricken villages and its influencing factors in China

The evolution of "Production-Living-Ecological" space, eco-environmental effects and its influencing factors in China

The evolution of "Production-Living-Ecological" space, eco-environmental effects and its influencing factors in China

Analysis on the influencing factors of China&amp;apos’s domestic tourism economic development

Tourism, as China&apos’s tertiary industry, plays an important role in China&apos’s economic development. This paper selects the relevant statistical data of China&apos’s tourism industry from 1994 to 2019, and establishes a multiple linear regression model to analyze the impact of seven factors, including the number of domestic tourists, per capita GNP, railway mileage, highway mileage, airline mileage, the number of travel agencies, and the number of star hotels, on China&apos’s tourism economic development. The results show that the number of railway miles, the number of domestic tourists and the number of star hotels have a significant impact on the development of China&apos’s tourism economy, among which the increase of the number of domestic tourists has the greatest impact on the development of China&apos’s tourism economy.On the basis of the above empirical evidence, this paper puts forward some suggestions to promote the development of China&apos’s tourism industry, such as solidly promoting the integration of domestic tourism resources, giving full play to spillover effect of resource integration, strengthening tourism infrastructure and improving residents&apos’income level.

Spatial distribution pattern of classic red tourism scenic spots and heterogeneity of its influencing factors in China

Spatial distribution pattern of classic red tourism scenic spots and heterogeneity of its influencing factors in China

Analysis of the International Competitiveness and Influencing Factors of China’s Service Trade

Analysis of the International Competitiveness and Influencing Factors of China’s Service Trade

Spatial effects of carbon emission intensity and regional development in China.

Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China's provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China's 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China's provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China's carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.

The influencing factors of China's green building development: An analysis using RBF-WINGS method

Energy efficiency and emission reduction in construction industry has been concerned in the world. Green building become the focus of academic research in recent years. To fundamentally realize the green development of construction industry, it is necessary to systematically analyze the influencing factors of green building development. On the basis of science and technology investment, industrial size, industry potentiality, and policy incentives, this paper introduced the green financial supportive factor and established a multi-layer green building influencing factors index system. Radial basis function neural network is adopted to improve the Weighted Influence Non-linear Gauge System (WINGS) model, namely RBF-WINGS model, and direct strength-influence matrix is determined. This method objectively analyzed the weights of the green building development influencing factors. The result indicates that science and technology input is the fundamental influencing factor, while industrial size and green financial support are the main influencing factors for the development of green building. This study provides theoretical evidence for the development of green buildings in China and offers decision-making reference for government departments.

Analysis of industrial eco-efficiency and its influencing factors in China

Industry is the largest sector for energy consumption and pollution emissions in China. Thus, improving industrial eco-efficiency is necessary for China to achieve sustainable development. Based on panel data from 31 industrial sectors from 2001 to 2015, a three-stage data envelopment analysis model was used to empirically explore industrial eco-efficiency and its influencing factors from the perspective of industrial heterogeneity. The results show that the overall level of industrial eco-efficiency in China is not high, first declining and then rising during the study period. Low eco-efficiency was mainly due to low scale efficiency. After removing the influences of external environmental factors and noise, industry profit rates, ownership structures, and foreign direct investments were all significantly and positively correlated with eco-efficiency. Environmental regulations were significantly and negatively correlated, while the intensity of research and development exhibited no linear relationship. Industrial heterogeneity significantly affects eco-efficiency. Capital-intensive industries had the highest eco-efficiencies, followed by resource-intensive industries and labor-intensive industries, respectively. Comparison of technical efficiency (TE) before and after adjustment on a panel of 31 industries in China from 2001 to 2015.

Quantifying the effects of contour tillage in controlling water erosion in China: A meta-analysis

Contour tillage is a widely practiced conservation tillage in China. The beneficial effects of contour tillage in controlling water erosion have been recognized, further researches are needed to quantify the effectiveness of contour tillage in reducing water erosion and identify the influencing factors in China. We conducted a nationwide meta-analysis based on 229 runoff and 290 sediment paired observations from 47 published papers from national and international literatures. The results showed that compared to traditional tillage, the benefits of contour tillage in China with respect to runoff and sediment reduction were 35.86% and 49.02%, respectively. Sediment yield reduction by contour tillage was greater under simulated rainfall than under natural rainfall. Runoff reduction by contour tillage decreased with the increasing mean annual precipitation and temperature, while sediment yield reduction was not affected by climate factors. Contour tillage in loamy soils and soils with organic carbon content > 1% showed the greatest benefits in reducing sediment yield (64.26% and 52.52%, respectively). The greatest runoff and sediment yield reduction (61.04% and 67.24%, respectively) occurred at slope length > 20 m. The greatest runoff reduction by contour tillage was found for tillage duration > 10 years (44.08%), while the greatest sediment yield reduction occurred at tillage duration ≤ 5 years (52.89%). The effectiveness of contour tillage under cereals was better for runoff and sediment yield reduction compared to legumes and rotation as consequence of the high biomass quantity and lower mineralization rate of cereal straw. Our results conclude that contour tillage is an effective measure for controlling water erosion, and its benefits in reducing runoff and sediment yield are different according to the climate, soil properties, topography and management practices.

Industrial environmental efficiency and its influencing factors in China: analysis based on the Super-SBM model and spatial panel data.

The industry sector is not only an important driving force for economic growth but also the largest sector of resource consumption and pollution emission. In this study, we first constructed a super-slack-based measure (Super-SBM) model including the resource consumption and undesirable outputs, and estimated the industrial environmental efficiency (IEE) in China from 2007 to 2016. Afterwards, based on the spatial autocorrelation test and the spatial Durbin model, the spatio-temporal evolution and the influencing factors of IEE were analyzed. The empirical results are obtained as follows: the average IEE from 2007 to 2016 was 0.5176. IEE in the east of China was the highest, whereas it was the lowest in the west. The spatial autocorrelation test showed that the regions with similar levels of IEE in China had significant spatial agglomeration, whereas the local spatial distribution of IEE was unbalanced. The high-high IEE agglomeration areas were located in Liaoning, Jilin, and Inner Mongolia. The low-low IEE agglomeration areas were concentrated in Gansu, Ningxia, and Sichuan. Finally, according to the spatial Durbin panel model and spillover effect decomposition, GDP, FDI, human capital, environmental governance investment, research and development investment, and urbanization have a positive impact on IEE. The industrial and energy consumption structures have a negative impact on IEE. Therefore, the central government should focus on balancing IEE of different provinces and regions, increasing investment in industrial pollution treatment, and encouraging FDI to improve IEE.

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China.

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

Analysis on the influencing factors of China’s tourism economy from the perspective of aging and tourism culture

The empirical analysis of this paper focuses on two issues, one is the impact of China’s aging process on China’s domestic tourism economy, and to what extent; the other is how to define and measure the impact of tourism culture on tourism economy. This research takes the domestic tourism revenue as the dependent variable and takes the elderly dependency ratio as the indepedent variable. In the modelling process, “external conditions” and “internal desires” are introduced as control variables and regarded as the software and hardware parts of tourism culture respectively. The time dummy variable is used to catch outliers and structural abrupt changes. Furthermore, quantile regression is carried out for the selected models. Based on ADL model and quantile regression, this paper analyzed the influence of aging and tourism culture on the development of China’s tourism economy and its changing trend.