ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Marine Affairs and Policy
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1647837
This article is part of the Research TopicChallenges and Solutions in Forecasting and Decision-Making in Marine Economy and Management, Volume IIView all 13 articles
Spatiotemporal Differentiation and Prediction Analysis of China's Marine Fishery Scientific and Technological Innovation Level
Provisionally accepted
- Zhejiang Ocean University, Zhoushan, China
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Marine fisheries scientific and technological innovation level (MFSTIL) drives the modernization and sustainability of China's fisheries. Based on the panel data of 11 coastal provinces and cities in China from 2011 to 2022, the article used the entropy weight-TOPSIS method, spatial econometric model (Standard Deviation Ellipse), Dagum Gini coefficient, Markov modified grey prediction model and other methods to analyze the spatiotemporal differentiation of China's MFSTIL and its future development trend. The results show that: (1) China's MFSTIL was generally good and grew steadily year by year from 2011 to 2022, but regional development was uneven; (2) The temporal evolution of MFSTIL has a sequence of "slow rise-rapid rise-steady rise", and the differentials between regions also fluctuate upward; (3) The spatial pattern of MFSTIL is uneven in distribution, and there are "lagging areas" in the three major marine economic zones; in terms of evolution, it has the dynamic equilibrium characteristics of "northeast-southwest", and the center of gravity of the standard deviation ellipse moves first to the northeast and then to the southwest; (4) The overall spatial variation in MFSTIL has increased year by year in recent years, with hyper-variance density contributing most significantly to regional differences; (5) The MFSTIL will maintain the growth trend of the previous 12 years in 2023-2030, and the ranking of provinces will change slightly. The gap between the northern and eastern regions will narrow, while their disparity with the southern region will widen, the absolute gap between regions cannot be ignored. In this regard, the article proposed following suggestions: (1) Implement targeted support strategies through special funds and the construction of industry-academia-research integration platforms to identify and empower regions lagging behind in innovation, thereby stimulating local scientific research and innovation capabilities; (2) Optimize the spatial layout of the three major marine economic zones, build integrated industrial chains, and achieve complementary regional development; (3) Establish a dynamic monitoring and early warning system based on big data and the Internet of Things to achieve real-time monitoring of resources, the environment, markets, and industries, thereby promoting sustainable and balanced development.
Keywords: Marine fishery, scientific and technological innovation level, spatiotemporaldifferentiation, Standard deviation ellipse, Dagum Gini coefficient, Grey prediction model
Received: 16 Jun 2025; Accepted: 11 Sep 2025.
Copyright: © 2025 Li, Gao, Qi, Qi, He and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xianzhe Cai, Zhejiang Ocean University, Zhoushan, China
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