Objective/Meaning Taiwan Region of China has developed a compound fishery system featuring the coexistence of offshore, inshore and coastal fishing, as well as marine and inland aquaculture. Obvious disparities exist among counties and cities in terms of fishing fleet scale, aquaculture spatial layout, and the allocation of processing and cold-chain facilities. This study constructs a unified county-level accounting framework for fishery carbon emissions, clarifies the impacts of production scale, industrial structure and production efficiency on carbon emissions, further identifies the key links for emission reduction, and sorts out the low-carbon transformation path for the fishery industry in Taiwan Region of China.
Methods/Procedures Based on fishery statistics from counties and cities in Taiwan from 2013 to 2024, this paper uses a combination of activity data and emission factors to calculate carbon emissions from fishing and aquaculture production processes, incorporating processing and cold chain emissions into the expanded accounting scope. After characterizing the spatiotemporal features of total carbon emissions, emissions per unit output, and emissions per unit output value, a two-way fixed effects model, extreme random trees, and the SHAP method are used to analyze the relationship between fishery production structure, production efficiency, industrial chain conditions, and carbon emission intensity.
Results/Conclusions This study found that carbon emissions from Taiwan's fisheries generally show a fluctuating upward trend, with the fishing stage consistently dominating and emissions highly concentrated in a few major fishing counties. Total carbon emissions and emission intensity are not simply correlated; some small-scale fishing counties have higher emissions per unit of output. The proportion of catch yield shows a relatively stable positive correlation with carbon emissions per unit of output, while single-vessel fishing efficiency and aquaculture efficiency per unit area are generally negatively correlated with carbon emission intensity. The effects of processing and cold chain conditions are highly dependent on the accounting method. Machine learning results further indicate that the proportion of catch yield is the primary predictor of differences in carbon emission intensity among counties, and its related impact exhibits significant non-linear characteristics. This study suggests that the low-carbon transformation of fisheries in Taiwan should place greater emphasis on optimizing production structure and improving energy efficiency.
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