Fishing vessels are important contributors to global emissions in terms of greenhouse gases and air pollutants. However, few studies have addressed the emissions from fishing vessels on fishing grounds. In this study, a framework for estimating fishing vessel emissions, using a bottom-up dynamic method based on the big data from the Beidou VMS (vessel monitoring system) of fishing vessels, is proposed and applied to a survey of fishing vessel emissions in the East China Sea. The results of the study established a one-year emission inventory of fishing vessels in the East China Sea. This study was the first to use VMS data to estimate fishing vessel emissions in a fishing area, and the results will help to support the management of their carbon emissions.

Illegal, unreported, and unregulated (IUU) fishing significantly threatens marine ecosystems, disrupts the ecological balance of the oceans, and poses serious challenges to global fisheries management. This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. A preprocessing protocol addressing multidimensional noise in raw VMS datasets was developed, incorporating velocity normalization and gap filling to ensure data reliability. The CNN-BiLSTM hybrid model emerged as optimal for fishing behavior classification, achieving 89.98% accuracy and an 87.72% F1 score through synergistic spatiotemporal feature extraction. Spatial analysis revealed significant policy-driven reductions in fishing intensity during the moratorium (May–August), with hotspot areas suppressed to sporadic coastal distributions. However, concentrated vessel activity in Zhejiang’s nearshore waters suggested potential illegal fishing. Post-moratorium, fishing hotspots expanded explosively, peaking in October and clustering in Yushan, Zhoushan, and Yangtze River estuary fishing grounds. Quarterly patterns identified autumn–winter 2021 as peak fishing seasons, with hotspots covering >80% of East China Sea grounds. The framework enables real-time fishing state detection and adaptive spatial management via dynamic closure policies. The findings underscore the need for strengthened surveillance during moratoriums and post-ban catch regulation to mitigate overfishing risks.

State‐owned enterprises (SOEs) play an important role in China. During the transformation from a planned to a market economy, plenty of Chinese SOEs fell into trouble. Dalian machine tool group (DMTG) who was once a leading enterprise in the Chinese machine tool industry bankrupted in 2017. To explore the causes of its collapse, we employ the interpretative structural model (ISM) to investigate the reasons for its failures from multi‐aspect and at different levels. The results indicate that the root cause of this bankruptcy is the top manager’s mismanagement; the lack of a reasonable strategic positioning and long‐term product planning are also important factors of DMTG’s failure, and the problems of human resource management accelerated the bankruptcy. Findings provide lessons to be learned from the bankruptcy for SOEs and offer managerial insight into SOEs.