Mechanized Transformation in Shellfish Aquaculture: Practical Challenges and Intelligentization-Driven Breakthrough Pathways
DOI:
https://doi.org/10.5281/zenodo.15582860Keywords:
Shellfish farming , Intelligent transformation, Digital twin Carbon sink economy, Governance innovationAbstract
Under the dual pressure of global aquatic resource constraints and the transformation to a low-carbon economy, intelligent technologies are reshaping the underlying logic of the shellfish farming industry. This paper systematically constructs a three-dimensional analysis framework of "technology - ecology - governance". Through horizontal comparisons of 12 countries and vertical analyses of 38 technological innovation cases, it reveals the three core contradictions of intelligent transformation: regional imbalance in technology supply, ambiguity of rights and responsibilities in data governance, and intergenerational conflicts in benefit distribution. Research has found that China's shellfish industry has achieved partial leadership in areas such as offshore aquaculture robots and AI disease early warning through the "scene-driven innovation" model. However, shortcomings such as a domestic sensor production rate of less than 20% and a digital skill compliance rate of only 11.3% among practitioners are restricting the overall transformation. Propose breakthrough paths such as establishing an "Intelligent Fishery innovation community", piloting a digital twin system for sea areas, and promoting carbon sink credit trading, providing a Chinese solution for the intelligentization of global shellfish farming.
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Data Availability Statement
The data used in this study are sourced from public domain resources. The specific data were obtained from China Fishery Statistical Yearbook, which can be accessed through the official website of the Ministry of Agriculture and Rural Affairs of the People's Republic ofChina at：http://www.moa.gov.cn/sj/tjnj/yynj/yyztj/202412/t20241219_6449421.htm.
Since these data are publicly accessible, they can be easily obtained by other researchers. By using publicly available data, we aim to enhance the reproducibility of our research and contribute to the open - science movement in the global academic context.
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Copyright (c) 2025 Zhu Shenghu, Wang Yuting, Ji Yongjin (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
