At present, online shopping is becoming increasingly popular. In particular, low-carbon products are becoming more favored as consumers' low-carbon awareness increases. Manufacturers sell their low-carbon products through e-commerce platforms. Thus, the manufacturer and the e-commerce platform form a low-carbon e-supply chain system. The manufacturer makes products with carbon emission reduction efforts, while the e-commerce platform provides a sales service. In this paper, we described models for a decentralized decision mode and a centralized decision mode in the low-carbon e-supply chain, and compare the decision results. Our findings show that the centralized decision mode has a better performance than the decentralized one, the ability of the e-supply chain to respond to consumers' preference for a low-carbon product has a direct effect on its operation, and the manufacturer's carbon emission reduction behavior will be a potential source for enterprises to gain more revenue. Further, we designed a coordination contract for them that can be accepted by both sides in the decentralized decision mode. We find that if the e-commerce platform can share the carbon emission reduction costs of the manufacturer, the performance of the e-supply chain will be greatly improved. A practical case study and numerical examples validate our analysis.
More low-carbon products help fight climate change and environmental problems. Governments consider encouraging the manufacturer’s initiative of producing low-carbon products by providing subsidies. However, when the manufacturer sells low-carbon products through the e-commerce platform, fairness concerns arise because of the profit difference. So, this paper builds game models to study decision behavior in the low-carbon e-commerce supply chain when the manufacturer receives government carbon subsidies and has fairness concerns. Our findings show that consumers’ preference for low-carbon products will be conducive to the operation of the supply chain. So it is necessary to popularize low-carbon products. The effect of government subsidies on supply chain decisions is different from fairness concerns. Government subsidies are positive factors in the supply chain operation, which can stimulate the manufacturer to make low-carbon products as expected and choose the high quality-high price development mode. This will help improve the profit of enterprises in the supply chain but cannot effectively stimulate the e-commerce platform to increase its service level. By contrast, the manufacturer’s fairness concerns are negative factors, which make the manufacturer prefer to adopt a low quality-low price development mode to improve their utility. This offsets the positive effect of government subsidies. It turns out that the profit of both node enterprises and the supply chain system has declined. But, fairness concerns are an important way to express the manufacturer’s demand. Finally, the joint allocation contract of cost and profit designed by comprehensively considering the effect of government subsidies and fairness concerns can make the supply chain coordinated. However, even as positive factors, only within a specific range do government subsidies help coordinate the supply chain, but not the more, the better.
It is unequivocal that human influence has warmed the planet, which is seriously affecting the planetary health including human health. Adapting climate change should not only be a slogan, but requires a united, holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately. Recognizing the urgent needs to tackle the risk connection between climate change and One Health, the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided. Graphical Abstract
Background We aimed to develop and validate a nomogram for effective prediction of vaginal birth after cesarean (VBAC) and guide future clinical application. Methods We retrospectively analyzed data from hospitalized pregnant women who underwent trial of labor after cesarean (TOLAC), at the Fujian Provincial Maternity and Children’s Hospital, between October 2015 and October 2017. Briefly, we included singleton pregnant women, at a gestational age above 37 weeks who underwent a primary cesarean section, in the study. We then extracted their sociodemographic data and clinical characteristics, and randomly divided the samples into training and validation sets. We employed the least absolute shrinkage and selection operator (LASSO) regression to select variables and construct VBAC success rate in the training set. Thereafter, we validated the nomogram using the concordance index (C-index), decision curve analysis (DCA), and calibration curves. Finally, we adopted the Grobman’s model to perform comparisons with published VBAC prediction models. Results Among the 708 pregnant women included according to inclusion criteria, 586 (82.77%) patients were successfully for VBAC. Multivariate logistic regression models revealed that maternal height (OR, 1.11; 95% CI, 1.04 to 1.19), maternal BMI at delivery (OR, 0.89; 95% CI, 0.79 to 1.00), fundal height (OR, 0.71; 95% CI, 0.58 to 0.88), cervix Bishop score (OR, 3.27; 95% CI, 2.49 to 4.45), maternal age at delivery (OR, 0.90; 95% CI, 0.82 to 0.98), gestational age (OR, 0.33; 95% CI, 0.17 to 0.62) and history of vaginal delivery (OR, 2.92; 95% CI, 1.42 to 6.48) were independently associated with successful VBAC. The constructed predictive model showed better discrimination than that from the Grobman’s model in the validation series (c-index 0.906 VS 0.694, respectively). On the other hand, decision curve analysis revealed that the new model had better clinical net benefits than the Grobman’s model. Conclusions VBAC will aid in reducing the rate of cesarean sections in China. In clinical practice, the TOLAC prediction model will help improve VBAC’s success rate, owing to its contribution to reducing secondary cesarean section.
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