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《民法典》视域下延时性合规产品侵权之新解——以“市场份额责任”改造为核心

A New Interpretation of Delayed Compliant Product Tort from the Perspective of the Civil Code:Taking “Market Share Liability” as the Core
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摘要 我国药品、食品安全领域的延时性合规侵权案件频发,现行规范无法直接适用,《民法典》的侵权责任编却未作出任何回应。适当引进美国法上的“市场份额责任”并予以本土化改造迫在眉睫。药企的合规之抗辩不能被认可,因为其仍具有“不合理的危险”,且违反产品跟踪观察义务之行为亦应被视为缺陷,在利益衡量上应倾向于救济被侵权人。具体构建上,以《民法典》产品责任章第1202条为基础,对“产品”“缺陷”等要件进行合理解释,并对因果关系要件进行适当宽松改造,即可构建本土化体系。在损害赔偿的计算上,“本地市场份额”相较于“全国市场份额”标准具有法理和技术上的双重优势,更值得采纳。同时,大数据技术的飞速发展为我们提供了更多的可能性,针对线上企业和线下企业可采用不同的计算方式以加强合理性。 In the field of drug and food safety in China,there are frequent cases of Delayed Compliant Product Tort,and the current regulations cannot be directly applied for settlement.However,the product liability section in the Civil Code has not given any response.It is urgent to introduce “Market Share Liability” in American law and reform it for localization.The compliance defense of the pharmaceutical company cannot be accepted,because its violation of the product tracking and observation obligation should be considered as a defect,and the benefit measurement should be inclined to remedy the infringed.In terms of concrete construction,the localization system can be built by taking the existing product liability norms as the benchmark and reasonably relaxing the elements of the causal relationship.In terms of identification criteria,“local market share” standard has legal and technical advantages over the “ national market share” standard,which is more worthy of adoption.In terms of computing,the rapid development of big data technology provides us with more possibilities.Different computing methods can be adopted for online and offline enterprises to enhance rationality.
作者 季若望 JI Ruowang(School of Law,Zhejiang Gongshang University,Hangzhou 310018,China)
出处 《浙江工商大学学报》 CSSCI 北大核心 2022年第3期72-84,共13页 Journal of Zhejiang Gongshang University
基金 教育部人文社会科学研究青年基金项目“‘全有或全无’赔偿责任体系的挑战:比例侵权责任研究”(19YJC820017) 中国博士后科学基金面上资助项目(2021M703615)。
关键词 市场份额责任 延时性侵权 合规产品侵权 大数据 比例责任 market share liability delayed tort compliant product liability big data proportional liability
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