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In logistics and supply chain management, where small and medium enterprises dominate, integrating ESG practices is challenging and often favors larger companies with established frameworks. This study introduces an ESG recommendation system based on generative artificial intelligence (GERS) to provide accessible, tailored ESG guidance. Leveraging large language models and an ESG knowledge base, GERS offers actionable recommendations, particularly benefiting small and medium enterprises. Evaluated through a case study with a Hong Kong Logistics Association ESG assessment programme, expert panels confirmed the quality of its recommendations. Results demonstrate the GERS's ability to generate ESG improvement plans, enhancing capabilities efficiently. 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