The 14th five-year plan emphasizes the importance of marine ecology and environmental protection, and the green concept is incorporated into the high-quality development system of the marine economy. This research used the data of 11 coastal provinces and cities in China from 2006 to 2016, based on the super-efficiency slack-based measure model and global Malmquist index model. The objective was to calculate the green total factor productivity (GTFP) of the marine economy, to study the impact of the evolution of the marine industrial structure on marine economic GTFP. The study found the following: (1) in general, the upgrade of marine industrial structure promoted the growth of marine economic GTFP and presented an inverted “U” trend of initially promoting and then suppressing. Spatially, only the advancement and rationalization of industrial structure in the Yellow and Bohai Sea regions inhibited the growth of marine economic GTFP. In terms of time, the advanced marine industrial structure promoted the growth of GTFP from 2006 to 2010, whereas that of industrial structure inhibited the growth of GTFP from 2011 to 2016. (2) The GTFP of the marine economy showed an increasing trend, but the conversion rate of production technology is low. Falling into the “efficiency trap” of highly advanced technology input and low-efficiency technology output should be avoided. (3) Affected by the mismatch of regional resources or industrial structure, government intervention showed an “opposite” mechanism in areas with different marine economic strengths. Government intervention in areas with higher marine economic strength was conducive to GTFP growth, whereas government intervention in areas with weaker marine economic strength would hinder GTFP growth.

The rapid development of China’s fisheries sector has brought about significant environmental problems, which are detrimental to the sustainable development of the sector. Balancing environmental protection while promoting fisheries development has become an urgent issue in China. Based on data from 30 provincial-level administrative regions in China from 2004 to 2019, this study utilizes the Epsilon-based Measure (EBM) model considering undesirable outputs and the global Malmquist-Luenberger (GML) index to measure the green total factor productivity (GTFP) growth in China’s fisheries sector. Furthermore, it explores the spatiotemporal evolution and driving forces of fisheries GTFP growth using spatial Durbin model (SDM). The results indicate that ignoring the resource and environmental costs in fisheries production would overestimate the growth of total factor productivity (TFP) by 1.3%. The growth of fishery output primarily comes from the increase in input factors, exhibiting extensive characteristics that have been gradually diminishing over time. During the sample period, the provinces with the fastest growth in GTFP shifted from being mainly concentrated in the central and western regions to the eastern region. The local driving forces behind the improvement of fisheries GTFP include internet penetration rate, transportation convenience, education level of rural residents. The driving forces from economically similar provinces include the positive spatial interplay between provinces, fishery disaster rate, fisherman training, fishery trade openness, and urbanization rate. Overall, these finds offer a novel approach to reexamine the growth of China’s fisheries and provide valuable insights for the future fisheries development.

Manufacturing and digital technologies are increasingly integrated to enhance productivity. While prior studies have investigated the effect of digitalization on productivity, limited attention has been paid to the moderating role of dynamic capabilities in this effect. This study bridges this gap by employing a panel fixed-effects model to test how dynamic capabilities moderate the effect of digitalization on total factor productivity (TFP) in Chinese listed manufacturing firms from 2007 to 2020. The results show that digitalization significantly improves firms’ TFP. Moreover, digitalization’s effects on TFP are moderated positively by absorptive, innovative, and adaptive capabilities. Among these, adaptive capability has the strongest moderating effect, followed by innovative capability, while absorptive capability has the weakest. The strength of these moderating effects is closely related to the industry competition and the degree of government intervention in the market where the companies operate. This study introduces a dynamic capabilities perspective to offer a novel interpretation for uncovering the pathways through which digitalization enhances TFP. It also provides insights for managers on how to better leverage digital technologies and for policymakers on how to design supportive policies.

Digitalization is transforming the marine economy at an accelerating pace, yet its effects on the Quality of Marine Economic Development (QMED) and the pathways driving these changes are underexplored. This study investigates these dynamics using an unbalanced panel of 168 A-share listed marine firms in China over the period 2003–2023. We apply a two-way fixed effects model to estimate the effect of digitalization on QMED and explore its mechanisms, complemented by heterogeneity analyses across firm sizes, industry types, government attention, and human capital levels. The results show that digitalization improves QMED, with a 0.01 rise in the digitalization index—about one-fifth of its mean—lifting Total Factor Productivity (TFP) of marine firms by roughly 0.599, or 6.85% of the average TFP. Digitalization boosts QMED by enhancing firms’ resource allocation efficiency and spurring technological innovation. Larger firms benefit more than smaller ones, while labor-intensive industries outpace capital-intensive ones in QMED gains. Higher human capital levels weaken digitalization’s positive effect on QMED. These findings suggest practical strategies for practitioners, such as adopting cost-effective digital tools like automation and big data analytics in labor-intensive sectors and providing subsidies or financing to support smaller firms’ digitalization. These insights highlight digitalization’s uneven effects and provide a foundation for targeted policy design to enhance marine economic development.

The deep integration of manufacturing and digital technology has become an important means to promote the high-quality development of manufacturing. Based on the data of China's listed manufacturing companies from 2007 to 2020, this study uses a panel fixed-effect model to empirically test the effect of digital transformation on the total factor productivity of enterprises. It found that digital transformation has significantly improved the total factor productivity of China's listed manufacturing companies. This conclusion still holds true when considering the endogeneity problem and carrying out a series of robustness tests. The results of the heterogeneity analysis showed that the promotion effect of digital transformation on total factor productivity exists only in samples receiving low government subsidies, samples with low industry concentration and samples of large-scale enterprises. In addition, this study also found that technological innovation, human capital and inventory management efficiency are important mechanisms for digital transformation to improve the total factor productivity of enterprises.