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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]