图 别策略。人民网记者 张若涵摄 浙江丽水 市航拍。资料图片 本期主讲人：国家数据发展研究院院长 胡坚波 数据作为数字经济时代的关键生产要素，其价值释放具有非消耗性、可叠加性、倍增效应的鲜明特征， 但也面临权属界定模糊、流通规则缺失等关键障碍。数据基础制度通过对数据要素生产关系的系统性调 整，核心作用在于破解"数据不愿供、流不动、用不好"等制度障碍，为数据要素从"资源"向"资产""资 本"转化提供明确规则指引和稳定市场预期。制度建设不仅能降低数据交易成本、提升要素配置效率， 推动数据与劳动、资本、技术等传统要素深度融合，更能激发各类主体参与数据生产、加工、应用的积 极性，实现数据价值的多维度、全链条释放，为数字经济高质量发展注入不竭动力，是构筑国家数字竞 争新优势的基础性保障。 通过数据产权制度"定分止争"，为价值释放筑牢基石。数据要素的权属及其确立规则的不清晰，一直以 来是影响数据要素流通交易的制约因素。数据承载了个人、企业、社会、国家等多元主体的不同利益诉 求，具有多方共生、非消耗性、非竞争性、报酬递增等特点，难以利用已有权利体系进行数据产权界 定。"数据二十条"以满足数据要素流通使用需求为出发点，以保护 ...

Group 1 - The core argument of the article emphasizes the importance of data as a new production factor in the digital economy, highlighting its explosive growth and the need for a structured market for data elements [1][2][3] - The article outlines the gradual policy developments in China regarding data as a production factor, including the establishment of a data property rights system and the promotion of data marketization [1][2][3] - The concept of "structural separation of data property rights" is introduced, which involves the division of data rights into holding rights, usage rights, and operational rights, aiming to enhance data governance and market efficiency [3][4][5] Group 2 - The article discusses the multi-layered process of transforming data from a resource to a productive factor, emphasizing the need for clear property rights to facilitate efficient economic activities [2][3][4] - It highlights the unique characteristics of data, such as non-competitiveness and low replication costs, which complicate traditional property rights frameworks [2][3][4] - The structural separation of data property rights is proposed as a solution to address issues like data monopolization and privacy protection, while also promoting cross-industry collaboration [3][4][5] Group 3 - The article identifies the challenges in the current data market, including unclear rights definitions and inefficient circulation mechanisms, which hinder the realization of data value [11][12][13] - It presents various models and frameworks for understanding the data value chain, emphasizing the importance of market transactions in transforming data into capital [11][12][13] - The research aims to explore the impact of structural separation of data property rights on the value realization of data elements, filling gaps in existing literature [13][14][15] Group 4 - The article elaborates on the influence of structural separation on different stages of data value formation, particularly focusing on the resource and asset stages [14][15][16] - It discusses how structural separation can break down data silos, improve data quality, and enhance data security, thereby facilitating better data utilization [15][16][17] - The article also highlights the role of structural separation in promoting data asset valuation and market recognition, which is crucial for the development of a data-driven economy [17][18][19] Group 5 - The article emphasizes the significance of data products and their marketization, noting that clear pricing mechanisms and property rights are essential for effective data transactions [20][21][22] - It discusses how structural separation can reduce transaction friction and improve supply-demand matching efficiency in the data market [21][22][23] - The article advocates for innovative application scenarios for data, suggesting that structural separation can facilitate cross-industry collaboration and enhance the overall value of data [22][23][24] Group 6 - The article concludes that exploring the structural separation of data property rights can maximize the value of data elements and support the high-quality development of the digital economy [26][27][28] - It provides policy recommendations for implementing this structural separation, including promoting data sharing, establishing clear asset valuation standards, and encouraging innovative data trading models [28][29]

Core Viewpoint - The initial rights definition and minimization of transaction costs are prerequisites for establishing a unified national data factor market, which will drive the industrialization and fair pricing of data factors, ultimately benefiting the real economy [3][4]. Group 1: Basic Elements for Building a Unified Data Factor Market - The four basic elements for constructing a unified national data factor market are initial rights definition, transaction cost minimization, infrastructure development, and industrialization [4]. - Current obstacles to data factor market construction include unclear data property rights and high transaction costs, primarily due to the lagging property rights system behind technological advancements [4][5]. Group 2: Key Scientific Issues in Data Factor Market Construction - The initial rights definition of data resource development and utilization plays a foundational role in the construction of a unified data factor market, while transaction cost issues need urgent solutions [5]. - The construction of data circulation infrastructure introduces a technology trust mechanism, supporting data resource development, transaction pricing, and regulatory safety [5]. - The industrialization of data factors will act as an accelerator in promoting the construction of a unified national market [5]. Group 3: Data Property Rights Governance - The "Data Twenty Articles" propose a structural division of data property rights, which, while aligning with social equity from a legal perspective, presents significant logical conflicts from an economic standpoint [7]. - The classification and grading of data rights can increase transaction costs and complicate the sharing and trading of data resources, leading to data protectionism and monopolization [7][8]. - The initial rights to data resource development should ideally be assigned to the government, which can represent public interests and facilitate the efficient use of data resources [9]. Group 4: Transaction Cost Issues in Data Factor Market - Five key transaction costs affecting data factor flow include externality transaction costs, communication transaction costs, institutional transaction costs, intermediary service costs, and application delivery costs [11]. - High transaction costs hinder the incentive mechanisms for data resource circulation and utilization, necessitating government intervention to reduce these costs [12].