UNASSIGNED: Although the regular exercise population is a key metric for gaging the success of China\'s fitness-for-all activities, effective policy approaches to increase mass sports participation remain unclear. Previous research suggests that GDP, educational attainment, sports resources, and meteorological conditions could influence regular exercise participation. Therefore, this study first analyzed the macro-level correlates influencing China\'s regular exercise population.
UNASSIGNED: We utilize ordinary least squares (OLS) regression and geographical weighted regression (GWR) to theorize the relationship. The analysis encompasses data from the 31 administrative regions of Mainland China, as reported at the end of the 13th Five-Year Plan period. The log-log model enables us to quantify the marginal effect (elasticity) of the explanatory variables.
UNASSIGNED: The OLS regression showed that regional GDP and the proportion of the population with a university education were significant predictors. In the global model, the marginal effects of regional GDP and university education were 0.048 and 0.173, respectively. Furthermore, the GWR revealed a distinct geographic pattern that corresponds to the classic Hu Line.
UNASSIGNED: While regional GDP was also a significant correlate in our model, the elasticity demonstrates that university education had an asymmetric effect on China\'s regular exercise population. Therefore, this paper sheds light on a policy priority for the upcoming 15th Five-Year Plan, emphasizing the strategic importance of expanding university education to enhance mass sports participation. In turn, a better-educated populace may yield significant secondary effects on public health and contribute to the high-quality development of the Chinese path to modernization.

The translation elongation factor 1α (EF1α) protein is a highly conserved G protein that is crucial for protein translation in all eukaryotic organisms. EF1α quickly became insoluble at temperatures 42 °C treatment for 2h in vitro, but generally remained soluble in vivo even after being exposed to temperatures as high as 45 °C for an extended period, which suggests that protective mechanisms exist for keeping EF1α soluble in plant cells under heat stress. EF1α had fast in vivo insolubilization when exposed to 45 °C, resulting in about 40% of the protein aggregating after 9 h. Given its established role in protein translation, heat-induced aggregation is most likely to impact the function of the elongation factor. Overexpression of constitutive mutants in both GTP-bound and GDP-bound forms of EF1α resulted in significantly decreased heat tolerance. These findings provide evidence to support the critical role of EF1α, a thermosensitive protein, in the heat tolerance of plants.

It is unclear whether Gross Domestic Product (GDP) and greenness have additional modifying effects on the association between air pollution and respiratory system disease. Utilizing a time-stratified case-crossover design with a distributed lag linear model, we analyzed the association between six pollutants (PM2.5, PM10, NO2, SO2, O3, and CO) and 555,498 respiratory hospital admissions in Beijing from 1st January 2016 to 31st December 2019. We employed conditional logistic regression, adjusting for meteorological conditions, holidays and influenza, to calculate percent change of hospitalization risk. Subsequently, we performed subgroup analysis to investigate potential effect modifications using a two-sample z test. Every 10 μg/m3 increase in PM2.5, PM10, NO2, SO2, and O3 led to increases of 0.26% (95%CI: 0.17%, 0.35%), 0.15% (95%CI: 0.09%, 0.22%), 0.61% (95%CI: 0.44%, 0.77%), 1.72% (95%CI: 1.24%, 2.21%), and 0.32% (95%CI: 0.20%, 0.43%) in admissions, respectively. Also, a 1 mg/m3 increase in CO levels resulted in a 2.50% (95%CI: 1.96%, 3.04%) rise in admissions. The links with NO2 (p < 0.001), SO2 (p < 0.001), O3 (during the warm season, p < 0.001), and CO (p < 0.001) were significantly weaker among patients residing in areas with higher levels of greenness. No significant modifying role of GDP was observed. Greenness can help mitigate the effects of air pollutants, while the role of GDP needs further investigation.

Microplastic (MP) pollution is evolving into one of the most pressing environmental concerns worldwide. This study assessed the impact of economic activities on atmospheric MP pollution across 17 megacities in northern China, analyzing the correlation between the deposition flux of atmospheric MPs and variables such as city population, gross domestic product (GDP), and industrial structure. The results have shown that the MP pollution is obviously impacted by human activities related to increased GDP, population, as well as tertiary service sector, in which the MP pollution shows most close relationship with the GDP growth. Polypropylene, polyamide, polyurethane, and polyethylene were identified as the primary components of atmospheric MPs. The average particle size of MPs in atmospheric dustfall is 78.3 µm, and the frequency of MP particles increases as the particle size decreases. The findings highlight the complex relationship between socio-economic development and atmospheric MP accumulation, providing essential insights for the formulation of targeted emission reduction strategies.

Life expectancy can reflect both health benefit and implementation cost of climate policy. Nevertheless, little research has quantified the relation between life expectancy and climate policy in literature. In this paper, we attempt to narrow the research gap by studying how life expectancy is related to the Chinese nationwide emission trading scheme (CNETS). To achieve this research target, a Computable General Equilibrium (CGE) model is employed to simulate the operation of the economic system and the policy shock from emission abatement. The CGE model results show that life expectancy is prolonged by GDP but shortened by emissions, and the GDP impact on life expectancy is larger than the emission impact. Climate policy has dual effects on life expectancy because it relieves both negative emission impacts and positive GDP impacts on lifespan; its net effect on life expectancy is positive. Life expectancy positively impacts GDP, and this impact is moderated by climate policy; specifically, climate policy reinforces the positive impact of life expectancy on GDP. Life expectancy minimally affects carbon emissions during climate policy implementation; in other words, it has minimal impacts on emission abatement. These findings imply that climate policy and life expectancy complement each other; the government could implement climate policy to increase lifespan or prolong life expectancy to facilitate policy implementation.

In recent years, rising carbon dioxide (CO2) emissions and other negative environmental effects of human activity have raised concerns about the planet\'s future. Rapid industrialization, urbanization, and economic activity have shown a surge in CO2 emissions, contributing to global warming and climate change. The main purpose of this study is to examine the impact of such as gross domestic product, gross domestic product square, foreign direct investment, environmental diplomacy, environmental diplomacy security, and renewable energy consumption on environmental degradation. This study provides a new perspective on environmental diplomacy in OECD countries using panel data econometric methodologies from 1991 to 2020. It contributes to our understanding of the role of environmental and economic factors in reducing CO2 emissions. The panel data is also analyzed by CD, CIPS, FMOLS, DOLS, and PMG-ARDL tests. However, as per the findings of this research, all the factors significantly impact environmental degradation (Co2 emission). Finding data to either confirm or deny the efficacy of the Environmental Kuznets Curve theory within the framework of OECD countries is possible through this approach. This policy framework attempts to solve the issues at the connection of environmental diplomacy and economic concerns by emphasizing cooperation and sustainability and incorporating environmental considerations into economic decision-making processes in OECD countries.

Transportation energy demand has a significant impact on worldwide energy consumption and greenhouse gas emissions. Accurate transportation energy demand predictions can help policymakers develop and implement successful energy policies and strategies. In this study, a novel approach to predict transportation energy demand using the Artificial Neural Network (ANN) based on the Improved Red Fox Optimizer (IRFO) has been suggested. The proposed method utilizes the ANN model to solve the complex nonlinear relationships between transportation energy demand and its effective parameters including Gross Domestic Product (GDP), population, and vehicle numbers. Also, the IRFO algorithm was utilized to modify the ANN model\'s parameters to improve the prediction accuracy. The experimental findings demonstrate the ANN-IRFO model performs better than the other method in terms of accuracy and effectiveness. It predicts the growth of GDP, population, and vehicles number by 5.5 %, 4.8 %, and 4.2 %, respectively. The findings demonstrate that the suggested method can provide accurate forecasts for transportation energy demand, which can help decision-makers to make informed decisions and policies regarding energy management and sustainability.

To understand the epidemiological trend of gonorrhea in China from 2004 to 2021, predict the prevalence of the disease, and provide basic theory and data support for monitoring and managing gonorrhea. Gonorrhea incidence data in China from 2004 to 2021 were collected through the China Public Health Science Data Center and National Administration of Disease Prevention and Control, and the incidence and epidemiological characteristics were analyzed. Statistical analysis was performed using Joinpoint and autoregressive integrated moving average (ARIMA) models. A linear correlation model was used to analyze the correlation between gross domestic product (GDP) and the incidence rate. From 2004 to 2021, a total of 2,289,435 cases of gonorrhea were reported in China, with an average reported incidence rate of 9.46/100,000 people and a downward followed by an upward trend. Individuals with gonorrhea were primarily 20-30 y of age, with 1,034,847 cases (53.38%) from 2004 to 2018. The trend of increasing incidence was most obvious in the 10-20 age group (5,811 cases in 2004 to 12,752 cases in 2018, AAPC = 6.1, P < .001). The incidence of gonorrhea in China was negatively correlated with GDP from 2004 to 2021 (r = -0.547, P = .019). The correlation coefficient between the average incidence growth rate of each region from 2012 to 2018 and the average growth rate of regional GDP was 0.673 (P < .01). The root mean square error (RMSE) of the ARIMA model was 4.89%, showing powerful performance. There would be 97,910 gonorrhea cases in 2023 as predicted by the model.

The present study has investigated the impact of enterprises, collaborative innovation, industrial structure, inflation, and entrepreneurship in 27 provinces of China. The study used annual time series data from 2003 to 2019. This study adopts the CC-EMG and AMG tests to estimate the long-term association between the variables. The study applied one-step system GMM, two-step system GMM, Cup-FM, and Cup-BC regression estimations to calculate robust and reliable outcomes. The findings show that collaborative innovation, industrial structure, and entrepreneurship positively impact economic growth, whereas enterprises and inflation negatively impact economic growth. The estimated results also provide important policy implications for the selected and the other emerging economies in designing an appropriate way forward to economic development.

How do digitalizing businesses help them achieve sustainable growth? This research examines the mediating function of green technology innovation in answering this question by defining sustainable development performance in terms of corporations\' financial and environmental success. The educational system in China is examined to see how much of an impact it has on eco-innovation, as well as the relationship between green technology and innovation. The IFE test was utilized to determine whether or not the associations between variables such as GDP per capita, urbanization, green technology, higher education, and carbon dioxide emissions will continue to exist between 2004 and 2020 in China. The data for this analysis came from 30 of China\'s provinces. The findings of both the short-term and long-term CS-ARDL estimations demonstrated a positive link between eco-innovation and GDP per capita, green technology, higher education, and CO2 emissions. On the other hand, a negative correlation was found between urbanization and eco-innovation. The next topic covered in the research was how the effects of green technology might be seen in areas such as GDP per capita, higher education, and carbon dioxide emissions. The findings might provide valuable knowledge that developing economies can use to construct a feasible, sustainable path.