Total factor productivity of China's marine economy: A meta-analysis

With the rapid development of the global economy and the depletion of land resources, the ocean has gradually become a new area for human society to seek resources and space utilization. In China, the marine economy also has become an essential part of the national economy. Therefore, it is crucial to evaluate marine productivity to understand China's marine industry's input-output status and development level. We conducted a meta-analysis using 622 observations from 33 primary empirical studies to quantify the discrepancies in measurement findings and identify the influencing factors of total factor productivity (TFP) growth in the marine industry. The results indicate that: (1) In the existing literature, the mean TFP growth of China's marine economy at the provincial level is 1.002, which is lower than that at the national level (1.022); (2) The time span of the data, the estimation model, the evaluation indicator, and the journal's rank all have a significant effect on the estimation results of the marine economic TFP growth, whereas the year of publication does not affect the estimation results; (3) The TFP growth of China's marine economy peaked during the Ninth Five-Year Plan period, after which it began to decline year after year; (4) The TFP growth of the marine economy in the Yangtze River Delta region and Circum Bohai Sea region is significantly higher than that in the Pan-Pearl River Delta region; (5) Economic factors such as gross ocean product, level of opening-up, level of marine science and technology, and industrial structure all have an impact on the marine economic TFP growth. Accordingly, the following insights were obtained: In terms of marine economic development policies, we should continue to enhance the investment in marine environmental governance, strengthen the construction of marine ecological civilization, and pay attention to synergistic regional development, opening up to the outside world, scientific and technological innovation, and industrial structure optimization. In addition, the follow-up study should use long-period sample data as much as possible, pay attention to the parametric SFA model, and strip the negative environmental impact by constructing a green evaluation index system.