牡蛎养殖业绿色发展水平评价模型构建与实证分析OA
Construction and empirical analysis of an evaluation model for the green development level of the oyster aquaculture industry
随着生态文明建设和绿色发展战略的持续推进,传统牡蛎养殖业面临着日益严格的环保监管,为推进产业绿色转型,亟需系统且科学地评估其绿色发展水平.本研究通过R-聚类因子分析构建适用于牡蛎养殖业绿色发展水平评价的指标体系,采用随机森林算法和CRITIC法的算数平均数对指标赋权,运用功效系数法、TOPSIS法、秩和比法组成的模糊联合评价法对中国牡蛎养殖业的绿色发展水平进行综合测度.结果显示,六大牡蛎主产省的绿色发展水平从高到低依次为:福建、广西、山东、辽宁、广东、浙江.其中福建表现最优,广西与山东水平接近,辽宁、广东和浙江评分低于80,属中低发展水平.基于各省产业现状与存在的主要问题,进一步提出强化技术培训与推广、加强海域生态治理、推动产业结构优化与价值链提升、加大政策支持与资源引导力度等政策建议.
With the growing prevalence of green development concepts and the continuous advancement of green development strategies,the oyster aquaculture industry faces increasingly stringent environmental regulations.To promote the industry's green transformation,there is an urgent need for a systematic and scientific assessment of its green development level.This study employs R-clustering factor analysis to construct an indicator system for evaluating the green development level of the oyster aquaculture industry.Indicator weights are determined using the arithmetic mean derived from the Random Forest algorithm and the CRITIC method.A fuzzy joint evaluation method combining the effectiveness coefficient method,TOPSIS method,and rank-sum ratio method is then applied to comprehensively measure the green development level of China's oyster aquaculture industry.The results indicate that the green development levels of the six major oyster-producing provinces,ranked from highest to lowest,are as follows:Fujian,Guangxi,Shandong,Liaoning,Guangdong,and Zhejiang.Fujian demonstrated the strongest performance,while Guangxi and Shandong showed comparable levels.Liaoning,Guangdong,and Zhejiang scored below 80,indicating a medium-to-low development level.Based on each province's industrial status and primary challenges,this study further proposes targeted policy recommendations.
余姝;周昌仕
广东海洋大学 管理学院,广东 湛江 524088广东海洋大学 管理学院,广东 湛江 524088
管理科学
牡蛎养殖业评价指标体系绿色发展水平产业结构优化
oyster aquaculture industryevaluation index systemgreen development levelindustrial structure optimization
《中国渔业经济》 2026 (5)
62-73,12
本文受广东海洋大学科研启动基金项目(060302092407)、湛江市哲学社会科学规划项目(ZJ25YB03)资助.
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