Low blood glucose and fatigue accumulation at peak hours of occupational trauma in secondary industry workers.

Objectives. The incidence of occupational traumatic injuries caused by human error has been reported to occur at 11:00 and 8-9 h after commencing work. Impaired attention is closely related to the incidence of these accidents. Therefore, this study aimed to clarify the changes in blood glucose, fatigue and stress response hormone levels over time among workers in a secondary industry. Methods. The blood glucose and subjective fatigue levels of 26 male secondary-industry workers were measured on workdays. In addition, the cortisol and dehydroepiandrosterone levels in saliva were measured on one workday and one holiday. Results. Blood glucose levels at 11:00 and 17:30 on the workday were significantly lower than those at 09:30. Moreover, hypoglycemia was observed in some participants. A significant increase in subjective fatigue levels was observed during the workday. However, no significant differences in salivary cortisol levels were observed between the workday and the holiday at any time point. Conclusions. Blood glucose levels decreased and subjective fatigue levels increased at the time points that occupational accidents were reported to occur most frequently during work. These factors may contribute to human errors due to impaired attentional function.

Occupational health literacy level and its influencing factors among key populations in China in 2022 / 中国职业医学

Objective To analyze the level of occupational health literacy (OHL) and its influencing factors among key populations in China. Methods The front-line workers from 31 provinces, autonomous regions, municipalities, and Xinjiang Production and Construction Corps in China were selected as the research subjects using a combination of stratified cluster random sampling and probability proportional sampling. The Occupational Health Literacy Questionnaire of National Key Populations was used to investigate the OHL level. Results In 2022, a total of 340 506 people from 23 industries were surveyed. Among them, 168 455 and 172 051 people were surveyed in the secondary and tertiary industries, respectively. The OHL level of the research subjects was 52.6%. The OHL levels of workers in the secondary and tertiary industries were 56.5% and 48.9%, respectively. The results of multivariate logistic regression analysis showed that gender, age, marital status, educational level, household registration, monthly income, employment nature, years of service and industry category were independent influencing factors for OHL level of the research subjects (all P<0.01). Specifically, females had a higher OHL level than males (P<0.01); the older the age, the higher the education level, the higher the monthly income level, the higher the OHL level (all P<0.01); the level of OHL in unmarried people was higher than that in married people (P<0.01); the OHL level of workers with non-agricultural household registration was higher than that of workers with agricultural household registration (P<0.01); the OHL levels of workers in state-owned enterprises, foreign-funded enterprises and public institutions were higher than those in private enterprises (all P<0.01); the level of OHL in the group with 21.0-43.0 years of service was lower than that in the other years of service groups (all P<0.01); the OHL level of workers in the secondary industry was higher than that in the tertiary industry (P<0.01). Conclusion The workers in the key industries selected by the tertiary industry, the private enterprises in the secondary industry, those with more than 21.0 years of service, and the disadvantaged groups with younger age low income, low education level, and the agricultural household registration are the key groups for the improvement of OHL level in the future. Appropriate intervention methods and strategies should be actively explored to improve the OHL of these key populations.

Comparisons of CO2 emission performance between secondary and service industries in Yangtze River Delta cities.

To put the brakes on global climate change, China, the world's top emitter, has established ambitious CO2 emissions reduction targets. Industry-level emissions analysis can help policymakers determine better ways to achieve mitigation targets. This study is the first to target the total-factor carbon emission performance (TCPI) of secondary and service industries. We first compile industry-level CO2 emission inventories of 25 Yangtze River Delta cities during 2007-2016. The TCPI of secondary and service industries is then estimated by the non-radial directional distance function. We then compare the TCPI of the two industries across levels, dynamics, and inequalities using a global metafrontier approach. The results show the TCPI of the service industry (0.563 in 2016) was significantly higher than that of secondary industry (0.256 in 2016), suggesting that the service industry was more carbon-friendly. The TCPI gap between the secondary and service industries narrowed over the study period. The TCPI of secondary industry showed a promising increase during 2007-2016 with an annual growth rate of 2.30%, reflecting the positive effects of the government's reforms and environmental regulations. By contrast, the service industry saw a downward trend in TCPI, decreasing by 1.68% annually, primarily because it is a newcomer to low-carbon development. TCPI inequality in secondary industry was much larger than in the service industry, suggesting that significant heterogeneity exists in secondary industry. Therefore, policymakers should implement targeted mitigation policies for secondary industry, and place decarbonising the service industry on the agenda to reverse its decreasing TCPI.

Investigation of carbon dioxide emission in China by primary component analysis.

Principal component analysis (PCA) is employed to investigate the relationship between CO2 emissions (COEs) stemming from fossil fuel burning and cement manufacturing and their affecting factors. Eight affecting factors, namely, Population (P), Urban Population (UP); the Output Values of Primary Industry (PIOV), Secondary Industry (SIOV), and Tertiary Industry (TIOV); and the Proportions of Primary Industry's Output Value (PPIOV), Secondary Industry's Output Value (PSIOV), and Tertiary Industry's Output Value (PTIOV), are chosen. PCA is employed to eliminate the multicollinearity of the affecting factors. Two principal components, which can explain 92.86% of the variance of the eight affecting factors, are chosen as variables in the regression analysis. Ordinary least square regression is used to estimate multiple linear regression models, in which COEs and the principal components serve as dependent and independent variables, respectively. The results are given in the following. (1) Theoretically, the carbon intensities of PIOV, SIOV, and TIOV are 2573.4693, 552.7036, and 606.0791 kt per one billion $, respectively. The incomplete statistical data, the different statistical standards, and the ideology of self sufficiency and peasantry appear to show that the carbon intensity of PIOV is higher than those of SIOV and TIOV in China. (2) PPIOV, PSIOV, and PTIOV influence the fluctuations of COE. The parameters of PPIOV, PSIOV, and PTIOV are -2706946.7564, 2557300.5450, and 3924767.9807 kt, respectively. As the economic structure of China is strongly tied to technology level, the period when PIOV plays the leading position is characterized by lagging technology and economic developing. Thus, the influence of PPIOV has a negative value. As the increase of PSIOV and PTIOV is always followed by technological innovation and economic development, PSIOV and PTIOV have the opposite influence. (3) The carbon intensities of P and UP are 1.1029 and 1.7862 kt per thousand people, respectively. The carbon intensity of the rural population can be inferred to be lower than 1.1029 kt per thousand people. The characteristics of poverty and the use of bio-energy in rural areas result in a carbon intensity of the rural population that is lower than that of P.