内卷式竞争没有赢家 外卖补贴大战敲响ESG警钟

Core Viewpoint - The subsidy war among food delivery platforms is reaching its end due to regulatory intervention, highlighting the need for sustainable innovation and service capability competition rather than price wars [1][4][5] Group 1: Impact on Restaurants - Many restaurants are experiencing reduced dine-in orders as consumers prefer cheaper delivery options, leading to a decline in overall profitability [1][2] - The financial burden of subsidies has increased significantly for small and medium-sized restaurants, with some facing unsustainable costs due to high promotional expenses from platforms [2][3] - The negative impact of low-price competition is evident, as restaurants struggle to maintain profitability while still covering operational costs such as rent and labor [3][4] Group 2: Regulatory Changes - The newly revised Anti-Unfair Competition Law, effective from October 15, aims to regulate chaotic competition and prevent platforms from forcing merchants to sell below cost [6][7] - Regulatory bodies are actively engaging with major platforms to ensure compliance with laws and promote fair competition within the food service industry [4][5] Group 3: ESG Considerations - The subsidy war has exposed significant shortcomings in platforms' ESG responsibilities, affecting the entire industry ecosystem and raising concerns about food safety and quality [4][8] - Experts emphasize the need for platforms to balance the interests of merchants, consumers, and delivery personnel as part of their ESG commitments [8][9] - The environmental impact of increased packaging waste from delivery services is a growing concern, necessitating a shift towards sustainable practices [8][9] Group 4: Future Directions - Platforms are encouraged to move away from low-price competition and focus on innovative business models that support small merchants and community values [8][9] - There is a call for enhanced regulatory frameworks to ensure fair practices and transparency in platform operations, particularly regarding algorithmic decision-making [9]