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Traditional maritime search and rescue methods mainly rely on manual operation, which takes a long time to identify and results in poor search and rescue results. This paper applies computer vision technology to the field of maritime distress object detection and proposes an improved object detection algorithm YOLOv5s‐EFOE based on the YOLOv5s algorithm. Firstly, the authors change the detection head of the YOLOv5s algorithm to use a mixed channel strategy to build a more efficient decoupling head, reducing the number of 3 × 3 convolutional layers in the middle of the decoupling head to only one. The width of the head is scaled by the width multipliers of Backbone and Neck. Secondly, in conjunction with the SimOTA matching algorithm, the positive samples of the target to be tested are dynamically allocated to improve the recognition ability of different targets. Finally, considering the low pixel value of the maritime distress target from the perspective of unmanned aerial vehicles (UAV), the loss function CIoU in the original YOLOv5s is changed to EIoU. EIoU not only considers the distance and aspect ratio of the centre point, but also considers the true difference between the width and height of the prediction box and the real box, which improves the prediction accuracy of the anchor box. Experiments are conducted on a subset of the public dataset SeaDronesSee. The Recall$Recall$ of the YOLOv5s‐EFOE model proposed in this paper reached 75.9%, mAP0.5$mA{P_{0.5}}$ reached 79.9%, and mAP0.5−0.95$mA{P_{0.5 - 0.95}}$ reached 44.5%. These indicators are superior to the original YOLOv5s model, YOLOv7 series models, and YOLOv8 series models. Compared with the original model, the YOLOv5s‐EFOE model has increased the Recall$Recall$ by 5.4%, mAP0.5$mA{P_{0.5}}$ by 5.6%, and mAP0.5−0.95$mA{P_{0.5 - 0.95}}$ by 4.6%, improving the difficult to detect and missed detection situations. This model can be deployed on UAVs and can effectively identify maritime distress target, achieving the purpose of search and rescue. [ABSTRACT FROM AUTHOR]

Sea ice formation over open water exerts critical control on polar atmosphere‐ocean‐ice interactions, but is only crudely represented in sea ice models. In this study, a collection depth parameterization of new ice for flux polynya models is modified by including the sea ice concentration and ice growth rate as additional factors. We evaluated it in a climate model BCC‐CSM2‐MR and found that it improves simulation of Antarctic sea ice concentration and thickness in most of Indian and Atlantic sectors. Disagreement between the observed Antarctic sea ice expansion during 1981–2014 and the modeled decline still exists but is mitigated when the modified scheme is implemented. Further analysis indicates that these improvements are associated with the overcoming of premature closure of open water, which enhances the response of ocean to surface wind intensification during 1981–2014, and consequently slowdowns the sea surface temperature increase and the resulting Antarctic sea ice reduction. Plain Language Summary: Open water ice formation critically modulates sea ice variations and the associated polar atmosphere‐ocean interaction, but is not well represented in sea ice models. In this study, a modified collection depth parameterization of new ice based on an existing scheme is presented after including sea ice concentration and ice growth rate as additional factors. We evaluated this modified scheme in BCC‐CSM2‐MR and found that it can improve the simulation of mean Antarctic sea ice thickness and concentration in winter as well as Antarctic sea ice expansion from 1981 to 2014. Further analysis implies that these improvements can be attributed to the overcoming of the premature closure of open water areas in model simulations. Key Points: A modified collection thickness parameterization of new ice suitable for large‐scale climate simulations is presentedIt improves the simulation of Antarctic sea ice thickness and concentration, as well as Antarctic sea ice expansion during 1981–2014The improved simulations can be attributed to the overcoming of the premature closure of open water areas where new ice forms [ABSTRACT FROM AUTHOR]

In recent years, the task of maritime emergency rescue has increased, while the cost of time for traditional methods of search and rescue is pretty long with poor effect subject to the constraints of the complex circumstances around the sea, the effective conditions, and the support capability. This paper applies deep learning and proposes a YOLOv5s‐SwinDS algorithm for target detection in distress at sea. Firstly, the backbone network of the YOLOv5s algorithm is replaced by swin transformer, and a multi‐level feature fusion module is introduced to enhance the feature expression ability for maritime targets. Secondly, deformable convolutional networks v2 (DCNv2) is used instead of traditional convolution to improve the recognition capability for irregular targets when the neck network features are output. Finally, the CIoU loss function is replaced with SIoU to reduce the redundant box effectively while accelerating the convergence and regression of the predicted box. Experimenting on the publicly dataset SeaDronesSee, the Precision$Precision$, Recall$Recall$, mAP0.5$mAP_{0.5}$ and mAP0.5−0.95$mAP_{0.5-0.95}$ of YOLOv5s‐SwinDS model are 87.9%, 75.8%, 79.1% and 42.9%, respectively, which get higher results than the original YOLOv5s model, the YOLOv7 series of models, and the YOLOv8 series of models. The experiments verifies that the algorithm has good performance in detecting maritime distress targets. [ABSTRACT FROM AUTHOR]

Compared with the impact of listed corporates' environment, social responsibility, and corporate governance (ESG) on financing and market value, considerably less attention is paid to the role of ESG performance in improving environmental quality in China. However, exploring the environmental motivation of listed corporates' ESG engagement is vital for shareholder protection and corporate strategic development under state–business interactions. Thus, we study the relationship between state–business interactions and air pollution from the perspective of ESG using the dataset of Chinese listed corporates. The results demonstrate that the state–business collusion interactions increase air pollution by hindering the fulfillment of ESG. Meanwhile, this impact of heavy pollution corporates is higher than that of industrial corporates. However, the deleterious relationship might be largely improved when state–business interactions are "clean." Finally, we observe that ESG mediates the relationship between state–business interactions and pollutants. [ABSTRACT FROM AUTHOR]

The energy sector matters the sustainable development directly and is sensitive to climate change. A few empirical studies concentrate on how climate change influences firm performance. Climate change brings higher production costs and lower efficiency on the supply side and leads to beneficial sale performance and improves profit. However, it is ambiguous to the relationship of supply–demand and revenue‐cost levels, and their change is not consistent. Using panel data of 99 China‐listed firms active in the energy sector over the period 2000–2020, this study aims to identify the causal relation between climate change and the profitability of energy firms while controlling for firm‐specific and energy market‐related factors. The quantile regression results show that temperature deviation (TDEV) positively impacts the profitability of energy firms, and this impact varies at different quantiles of the profitability distribution. These findings remain unchanged by four robustness checks. Moreover, mechanism results indicate that revenue and cost have different interaction effects on the causal relation. The findings of this study can provide forward‐thinking expectations and assess climatic adaptation and risks, particularly inspired stakeholder engagements. [ABSTRACT FROM AUTHOR]

Abstract: Evidence of energy price elasticity would allow for a better understanding of economic, distributional and environmental consequences of varying energy prices. We document the previously unnoticed causal relationship between energy price elasticity and economic transition by modelling energy price elasticity as endogenous to the economic system. The central message is that economic transition promotes incentives and flexibility of micro‐units to sufficiently use price signals. Three potential mechanisms are proposed and tested for economic transition affecting the energy price elasticity. The findings provide implications for energy price policies because it reveals how energy consumers respond to energy price changes alongside economic transition. [ABSTRACT FROM AUTHOR]