Data as a Factor of Production and New Quality Productive Forces: Theoretical Logic and Practical Pathways

At present, a new round of technological revolution and industrial transformation is accelerating, and global technological innovation has entered a period of high density and intense activity, triggering profound changes in the economy and society. Many new forms of business are constantly emerging, future industries such as artificial intelligence and gene technology are making continuous breakthroughs, and the world economic landscape is being continuously reshaped. The world is facing changes unseen in a century. The United States and other Western countries are strengthening their technological hegemony and blockade measures, and the Chinese economy faces enormous challenges and development opportunities. Against this domestic and international backdrop, General Secretary Xi Jinping, based on the realities of China’s economic development

当前，新一轮科技革命和产业变革正加速演进，全球科技创新进入了高度密集活跃期，正在引发经济社会的深刻变革，许多新兴业态不断涌现，人工智能、基因技术等未来产业不断突破，世界经济格局正在不断重塑。世界正面临百年未有之大变局，美国等西方国家强化技术霸权和封锁举措，中国经济面对巨大挑战和发展机遇。在这样的国内外背景下，习近平总书记立足我国经济发展实际，提出发展“新质生产力”的重大论断。

With the vigorous development of the digital economy and the accelerating pace of digital industrialization and industrial digitalization, data has become a foundational resource and core factor of production for enterprises and even for national competition. General Secretary Xi Jinping has pointed out, “Build a digital economy with data as the key factor of production.” In 2019, the Fourth Plenary Session of the 19th Central Committee of the CPC [n1] listed data as a factor of production for the first time, and since then the state has increasingly emphasized the important role of data in the strategic layout of the economy. In March 2020, the “Opinions of the Central Committee of the CPC and the State Council on Building a More Complete Institutional Mechanism for Market-Based Allocation of Factors of Production” called for accelerating the cultivation of a data factor market. In December 2021, the “14th Five-Year Plan for Digital Economy Development” issued by the State Council proposed fully releasing the value of data factors and activating their potential. In December 2022, the “Opinions of the Central Committee of the CPC and the State Council on Establishing a Foundational Data System to Better Leverage the Role of Data Factors” provided a solid policy foundation at the institutional level for realizing the value of data factors. In December 2023, the “Data Factor × Three-Year Action Plan (2024–2026)” jointly issued by 17 departments including the National Data Administration made detailed work arrangements for the multiplier effect of data factors. This series of national-level policies fully demonstrates the important role of data factors in the current economic development; it concerns national competitiveness and is of great significance for seizing the commanding heights of the new round of technological revolution and industrial transformation, accelerating the development of new quality productive forces, and comprehensively advancing the building of a strong country and the great rejuvenation of the Chinese nation through Chinese-path modernization.

随着数字经济蓬勃发展、数字产业化和产业数字化进程不断加快，数据已成为企业甚至国家竞争的基础性资源和核心生产要素。习近平总书记指出，“构建以数据为关键要素的数字经济”。2019年，党的十九届四中全会首次将数据列为生产要素，自此开始，国家越发重视数据在经济战略布局中的重要作用。2020年3月，《中共中央国务院关于构建更加完善的要素市场化配置体制机制的意见》指出要加快培育数据要素市场，2021年12月，国务院印发的《“十四五”数字经济发展规划》提出要充分释放数据要素价值、激活数据要素潜能，2022年12月，《中共中央国务院关于构建数据基础制度更好发挥数据要素作用的意见》从制度层面为发挥数据要素价值提供了坚实的政策基础，2023年12月由国家数据局等17部门联合印发的《“数据要素×”三年行动计划(2024—2026年)》对数据要素乘数效应作出详细的工作部署。这一系列国家层面的政策充分说明数据要素在当前经济发展中的重要角色，其关乎国家竞争力，对于抢占新一轮科技革命与产业变革的制高点，加快发展新质生产力，以中国式现代化全面推进强国建设、民族复兴，具有重大意义。

Data factors endow new materials, new energy sources, new equipment, and the new industries and new drivers of growth they give rise to with powerful energy, prompting a qualitative leap in productive forces; they are the common factor of production behind these specific forms of new quality productive forces. From a global perspective, data is gradually becoming a key factor in reorganizing global factor resources, reshaping the global economic structure, and transforming the global competitive landscape. In view of this, data, as the core factor of the digital economy, naturally becomes the core factor of new quality productive forces. Currently, academic analysis of the functional relationship between data factors and new quality productive forces mainly adopts the following perspectives: first, discussing the mechanism by which data factors empower new quality productive forces from the perspective of the multiplier effect of data factors; second, studying the relationship between data factors and new quality productive forces, for example, exploring how data factors promote the development of new quality productive forces through the social production process of production, circulation, distribution, and consumption; third, taking total factor productivity, the core indicator of new quality productive forces, as a perspective to explore the relationship between data factors and new quality productive forces; fourth, from the perspective of data application scenarios, exploring how to promote the market-based allocation of data factors through scenario-driven approaches, thereby fostering the development of new quality productive forces. Overall, current academic research on new quality productive forces is in a phase of comprehensive expansion, and the discussion of the relationship between the two still needs to be deepened. This article is grounded in Marxist political economy. Starting from the technical-economic characteristics of data and data factors themselves, it explores how the integrated development of data factors and traditional factors of production in each link of the overall process of social production forms new quality productive forces. It discusses at the micro level how data factors promote the generation of new quality productive forces industries, and finally proposes practical pathways for accelerating the development of new quality productive forces through data factors.

数据要素为新材料、新能源、新设备及其催生的新产业、新动能赋予强大能量，促使生产力实现质的飞跃，是这些新质生产力具体形态背后共同的生产要素。从全球视角出发，数据正逐渐成为重组全球要素资源、重塑全球经济结构、转变全球竞争格局的关键要素。鉴于此，数据作为数字经济的核心要素，顺理成章地成为新质生产力的核心要素。目前学界分析数据要素和新质生产力之间的作用关系主要有以下几个角度：一是从数据要素的乘数效应讨论数据要素赋能新质生产力的机制;二是从数据要素与新质生产力的关系进行研究，如从生产、流通、分配、消费等社会生产过程探讨数据要素如何促进新质生产力发展;三是以新质生产力的核心标志全要素生产率为视角，探讨数据要素与新质生产力之间的关系;四是以数据应用场景视角探讨如何通过场景驱动数据要素市场化配置，进而促进新质生产力发展。总的来说，当前学界对新质生产力的研究处在全面铺开的研究阶段，对两者之间关系的讨论还有待深化。本文立足于马克思主义政治经济学，从数据和数据要素自身的技术—经济特性出发，探讨数据要素与传统生产要素在社会生产总过程各环节中的融合发展如何形成新质生产力，从微观层面讨论数据要素如何促进新质生产力产业的生成，最后提出数据要素加快新质生产力发展的实践路径建议。

I. Technical-Economic Characteristics of Data Factors

一、数据要素的技术—经济特性

“Data” has become a “high-frequency word” in today’s human society. Whether in scientific research literature, government work reports, or everyday discourse, “data” is everywhere, all the time. The concept of data emerged only in the era of information civilization; it developed from numbers, numerals, and digital computation. The development of numbers and computation is the foundation of data. The advancement of the Industrial Revolution also made data increasingly important in human society. Before the popularization of the internet, data referred to numerical values used for various statistics, calculations, scientific research, or technical design. From the birth of the first computer in 1946 to the present, the connotation of the concept “data” has expanded; it no longer refers only to numerical values, and its scope is much larger than that of numbers. This article draws on the following definition of data: “taking numbers as evidence, including quantities, numerical values, and numerals, and serving as the basis that can be transformed into information.” [n2] Data becomes increasingly important precisely because it contains information and knowledge and can be processed through computer technology.

“数据”已经成为当今人类社会的“高频词”，无论是在科研文献、政府工作报告中，还是在日常生活话语中，“数据”无处不在、无时不有。数据概念是在信息文明时代才产生的，是在数、数字和数字计算中发展而来的，数字和计算的发展是数据的基础。工业革命的推进也使得数据在人类社会中越发重要。在互联网普及之前，数据是指进行各种统计、计算、科学研究或技术设计等所依据的数值。从1946年第一台计算机诞生至今，“数据”这个概念的内涵就扩大了，不仅指数值，其范畴比数字要大得多。本文借鉴这一种数据定义：“以数为据包含数量、数值和数字等在内的并可转化为信息的基”。数据正是因其包含信息、知识并且可以通过计算机技术进行处理而变得越发重要。

Data itself possesses the following characteristics. First, virtuality and limitlessness. The virtuality of data is manifested in its non-material nature; data must rely on material entities to exist. The non-material nature of data allows it to be infinitely copied, disseminated, and shared by people. Although the costs of data collection, cleaning, storage, analysis, and application are relatively high, the marginal cost of its repeated use approaches zero. Second, reproducibility and shareability. As a carrier of information, once generated, data can be copied, transmitted, and shared by people at different times and places, on different material carriers. With the rapid development of cloud computing today, this reproducibility and shareability have been greatly expanded; people can store or use data in the cloud at any time. Third, non-rivalry and non-depletability. The objective form of existence of data and its natural attribute of being infinitely replicable at low cost determine that the same data can be used simultaneously by multiple parties without interfering with each other; it is non-rivalrous and non-depletable. Fourth, timeliness. People produce data every day. The progress and development of digital technology and changes in the social environment will inevitably cause the value of the information carried by data to gradually diminish over time.

数据自身具有以下特性。一是虚拟性和无限性。数据的虚拟性表现为数据的非实体性，数据必须依托物质实体而存在。数据的非实体性使得数据可以供人们无限复制、传播和分享。虽然数据在收集、清洗、存储和分析应用等环节耗费成本较高，但其在重复使用上的边际成本趋近于零。二是可复制性和分享性。数据作为信息的承载，在产生后可以供人们于不同的时间、空间，于不同的物质载体上复制、传递和分享。在云计算快速发展的今天，这种可复制和分享性得到了极大的拓展，人们可以随时用云端存储或使用数据。三是非竞争性和无耗损性。数据的客观存在形式以及可低成本无限复制的自然属性，都决定了相同的数据可同时被多方主体互不干涉地利用，具有非竞争性和无损耗性。四是时效性。人们每天都在生产着数据，数字技术的进步和发展以及社会环境的变化必然会导致数据所承载信息的价值随着时间的推移而逐渐衰减。

It is precisely because of the above characteristics of data itself that data factors possess the following technical-economic characteristics.

正是由于数据自身的上述特性，数据要素具有以下的技术—经济特性。

First, the interpenetration and permeability of data factors. Factors of production form specific structures through combination and coordination. As the content of production becomes richer and the economic structure more complex, the types of these factors have expanded from land and labor and continue to evolve into multiple modes of combination. The interpenetration and permeability of data are mainly manifested in two aspects: first, data can influence other factors of production in the process of circulation across various links, continuously improving the quality of traditional factors of production and enhancing their output efficiency; the integration of data factors with other factors of production can achieve complementary advantages in factor resources; second, each link also generates new data to promote the improvement of the data factors themselves.

一是数据要素的交融渗透性。生产要素通过组合和协调形成特定结构，随着生产内容的丰富和经济结构的复杂化，这些要素种类已从土地和劳动扩展，并持续演变出多种组合方式。数据的交融渗透性主要体现在两个方面：一是数据可以在各环节的流动过程中对其他生产要素产生影响，不断改善传统生产要素质量，提升传统生产要素的产出效率，数据要素与其他生产要素的融合可以实现要素间资源优势互补;二是各个环节又会产生新的数据来促进数据要素自身的提高。

Second, exclusivity. Theoretically, due to the virtual limitlessness, reproducibility, and shareability of data, data should not be exclusive. However, differences in the ownership of data directly create this exclusivity, such as with enterprise data and personal data. Moreover, even if most of this data is publicly available or its ownership is unclear, exclusivity can still arise from differences in the data collection and storage capabilities of different entities. “When the scale of data is sufficiently large and its content sufficiently complex and extensive, the data factor of production exhibits a high degree of exclusivity; enterprises and institutions that own the data will choose to ‘hoard’ rather than share data.” [n3] It should be noted that collected data generally needs to be cleaned and analyzed before it can be put to use, which also creates exclusivity.

二是排他性。理论上，由于数据具有虚拟无限性、可复制性和可分享性等特点，数据本不应该存在排他性。但数据的归属权不同会直接造成这种排他性，如企业数据和个人数据。而且，这些数据即使大多是公开或归属不明确的，也会因不同主体的数据收集和存储能力不同而造成排他性，“当数据的规模足够庞大、内容足够复杂和广泛时，数据生产要素就表现出高度的排他性(exclusive)，拥有数据的企业和机构会选择‘窖藏’而非分享数据”。需要注意的是，收集而来的数据基本上都需要经过清洗、分析后才能投入使用，这也会造成排他性。

Third, positive and negative externalities. “Externality” refers to the phenomenon whereby the economic behavior of one enterprise causes losses or benefits to other sectors outside the enterprise. Externalities can produce either positive or negative effects. A positive effect occurs when the benefits of an enterprise’s economic behavior spill over to benefit other enterprises while reducing its own gains; a negative effect occurs when the costs of an enterprise’s economic behavior escape to harm other enterprises while reducing its own losses. The externality of data has both positive and negative aspects. For enterprises, the positive externality of data is first manifested in the improvement of production efficiency for data-collecting enterprises. Digital platforms and some traditional industries—such as finance, media, and manufacturing enterprises, especially large internet companies—hold vast amounts of user data. These data include records of user browsing, clicks, searches, and consumption, and are used to promote innovation, improve operations and service quality, and thereby expand the user base. The negative externality of data is manifested in data fraud and data abuse. For example, enterprises use consumer data improperly, infringing on consumers’ privacy rights. The non-rivalrous nature of data as a factor of production means that the personal information of the data subject can be collected and used improperly by multiple actual data holders in different real-world scenarios, thereby directly infringing on the data subject’s privacy rights and increasing the probability of privacy leakage, harming consumers’ personal welfare. In addition, data fraud in the digital age can greatly harm society. Big-data-driven price discrimination against regular customers [4]4 Big-data-driven price discrimination against regular customers (大数据“杀熟”): A practice in which online platforms use collected user data to charge higher prices to existing or loyal customers than to new customers for the same product or service. is a typical act of personal data abuse and a manifestation of the negative externality of data as a factor of production. For individuals, the negative externality of data is mainly manifested as enterprises’ infringement of personal privacy and harm to welfare; for the state, the negative externality of data may affect national security.

三是正负外部性。“外部性”指的是一个企业的经济行为会使企业以外的其他部门受损或受益的现象。“外部性”会产生正效应，也会产生负效应。正效应是指企业的经济行为所产生的收益外溢使其他企业受益而其本身收益减少;负效应是指企业的经济行为所产生的成本外逃，使其他企业受损而其本身损失减少。数据的外部性兼具正负外部性。对于企业来说，数据的正外部性首先体现在数据收集型企业生产效率的提升上。数字平台及一些传统行业，如金融、媒体、制造业企业，特别是大型互联网企业，掌握着庞大的用户数据。这些数据包括用户浏览、点击、搜索和消费的记录，被用于促进创新、改进运营和服务质量上，进而扩大用户规模。数据的负外部性表现在数据造假、数据滥用上。比如，企业不规范使用消费者数据，侵犯消费者隐私权。数据要素的非竞争性导致数据当事人的个人信息可以在不同现实场景下被多个实际数据持有者收集并违规使用，从而直接侵害数据当事人的隐私权并增大隐私泄露的概率，损害消费者的个人福利。此外，数字时代的数据造假现象会极大地危害社会。大数据“杀熟”就是一种典型的个人数据滥用行为，是数据要素负外部性的一种体现。对于个人来说，数据的负外部性主要表现为企业等对个人隐私的侵犯和福利的损害;而对于国家来说，数据的负外部性则可能会影响国家安全。

Fourth, increasing returns to scale. Data is the carrier of information. The more comprehensive the data, the more valuable content people can extract from it. The more data there is, the better we can uncover the knowledge hidden behind the data, discover connections between things, more fully reflect a given thing, and make more accurate judgments. Therefore, data as a factor of production exhibits increasing returns to scale.

四是规模报酬递增性。数据是信息的载体，数据越多越全面，人们就越能从中挖掘出有价值的内容。数据越多，更有利于我们找出数据背后潜藏的知识，发现事物之间的联系，更能全面体现一个事物，更有利于我们做出准确的判断，因此，数据要素具有规模报酬递增性。

Fifth, ambiguity of property rights. Data as a factor of production has a certain ambiguity in terms of property rights attribution; the allocation of its ownership and various outputs between enterprises and consumers remains unclear. When consumers use information and communication technology (ICT) products and services provided by enterprises such as internet companies, they generate large amounts of data. These data are often directly collected and organized by the enterprises, and consumers objectively have no opportunity to dispose of or use these data.

五是产权模糊性。数据生产要素在产权归属上存在一定的模糊性，其所有权和各项产出在企业和消费者之间的分配尚不清晰。消费者在使用互联网公司等企业提供的各项信息与通信技术(ICT)产品和服务的过程中会产生大量数据，这些数据往往由企业直接收集和整理，消费者在客观上没有处置和使用这些数据的机会。

It is precisely because of the technical-economic characteristics of data and data as a factor of production that, when combined with other factors of production and applied to all aspects of social production and life, it produces multiplier effects, innovation effects, spillover effects, and so on. Thus it becomes increasingly important in economic life and plays an irreplaceable role in the generation process of new quality productive forces.

正是因为数据和数据要素自身的技术—经济特性，它在与其他生产要素相结合、应用于社会生产生活各方面时，会产生乘数效应、创新效应、溢出效应等，因而在经济生活中越发重要，在新质生产力的生成过程中起到不可替代的作用。

II. The Micro-Level Generation of Data as a Factor of Production and New Quality Productive Forces

二、数据要素与新质生产力的微观生成

General Secretary Xi Jinping has pointed out that “the vast ocean of data is like the petroleum resources of industrial society, containing immense productive forces and business opportunities; whoever masters big data technology holds the resources and the initiative for development.” [5]5 Xi Jinping, speech at the 36th collective study session of the Political Bureau of the 18th CPC Central Committee, October 9, 2016. Data as a factor of production plays a key role in the generation process of new quality productive forces. Exploring the internal mechanism by which data as a factor of production drives the generation of new quality productive forces is a prerequisite for gaining insight into the developmental logic of new quality productive forces and grasping the key drivers of the transformation of productive forces.

习近平总书记指出，“浩瀚的数据海洋就如同工业社会的石油资源，蕴含着巨大生产力和商机，谁掌握了大数据技术，谁就掌握了发展的资源和主动权。”数据要素在新质生产力的生成过程中发挥了关键作用，探索数据要素推动新质生产力生成的内在机理，是洞悉新质生产力的发展逻辑、把握生产力变革关键动因的前提。

Because of its own technical-economic characteristics and multiple effects, data as a factor of production develops into a new type of factor of production, digitally transforming and upgrading the combination of traditional factors of production, and permeating all links of social reproduction, thereby powerfully promoting the formation of new quality productive forces. The internal logic by which data as a factor of production drives the realization of new quality productive forces is shown in Figure 1.

数据要素因其自身的技术—经济特征和多重效应而发展成为新型生产要素，对传统生产要素进行数字化改造和组合提升，并渗透于社会再生产的各个环节，有力地推动了新质生产力的形成。数据要素推动新质生产力实现的内在逻辑如图1所示。

(A) The Integrated Development of Data as a Factor of Production with Other Factors of Production Gives Rise to New Quality Productive Forces

(一)数据要素与其他生产要素的融合发展催生新质生产力

After data becomes an independent factor of production, it has a close and complex relationship with other factors of production. In the specific production process, the types, proportional relationships, and modes of combination of factors of production all affect production efficiency. Data as a factor of production brings profound impacts and extensive shocks to different factors of production. By integrating with other factors of production, optimizing the combination of factors of production, and improving the quality of factors of production, it gradually manifests its own value.

数据成为独立的生产要素后，与其他生产要素存在着紧密而复杂的联系。在具体生产过程中，生产要素的种类、比例关系、结合方式等都会影响生产效率。数据要素给不同生产要素带来了深刻的影响和广泛的冲击，它通过与其他生产要素融合，优化生产要素组合和提升生产要素质量，逐渐呈现自身的价值。

First is the integration of data as a factor of production with the labor factor. First, data as a factor of production enhances the quality of laborers. On the one hand, using data as a factor of production itself requires laborers to master more knowledge and skills; on the other hand, in this interactive process, the innovative quality of laborers is also improved. Data as a factor of production can enable laborers to cultivate data-driven innovative thinking. By analyzing large amounts of data, laborers can discover potential problems and opportunities and propose innovative solutions, thereby increasing labor productivity. Second, more precise control of the labor process emerges. In current economic life, sensors are ubiquitous in production and daily life, which means that enterprises’ monitoring and control of the labor process becomes more comprehensive, accurate, and real-time, thereby increasing labor productivity.

首先是数据要素与劳动力要素的融合。一是数据要素提升了劳动者的素质。一方面，使用数据要素本身就要求劳动者掌握更多的知识和技能，另一方面，在这种互动过程中，劳动者的创新素质也得到提高。数据要素可以使劳动者培养数据驱动创新思维，劳动者通过分析大量数据，发现潜在的问题和机会，提出创新解决方案，从而提高劳动生产率。二是出现了更精准的劳动过程控制。在当前的经济生活中，生产生活中的传感器无处不在，这也就意味着企业对劳动过程的监测和控制更加全面、准确、实时化，从而提升劳动生产率。

Second is the integration of data as a factor of production with the technology factor. Data as a factor of production and the technology factor are in a symbiotic relationship of mutual promotion. Data as a factor of production is an important foundation for technological innovation. Large amounts of data provide training samples and validation data for the development of technologies such as artificial intelligence and machine learning, driving continuous technological progress. For example, deep learning algorithms require large amounts of data to train models, in order to improve the accuracy and generalization ability of the algorithms. The technology factor provides means and tools for the development and application of data as a factor of production. Advanced information technologies, such as big data processing technology, cloud computing technology, and blockchain technology, provide technical support for the storage, processing, analysis, and sharing of data, increasing the value and utilization efficiency of data.

其次是数据要素与技术要素的融合。数据要素与技术要素是互相推动的共生关系。数据要素是技术创新的重要基础。大量的数据为人工智能、机器学习等技术的发展提供了训练样本和验证数据，推动了技术的不断进步。例如，深度学习算法需要大量的数据来训练模型，以提高算法的准确性和泛化能力。技术要素为数据要素的开发和应用提供了手段和工具。先进的信息技术，如大数据处理技术、云计算技术、区块链技术等，为数据的存储、处理、分析和共享提供了技术支持，提高了数据的价值和利用效率。

Finally, the integration of data as a factor of production with the management factor. Data as a factor of production contains a large amount of information and knowledge. The integration of rich data resources and advanced data analysis capabilities with the management factor can enhance the scientific nature of management decision-making, facilitate efficient communication and smooth operation of organizational structures, enable enterprises to better meet customer needs, improve product and service quality, innovate business models, and thus stand out in market competition.

最后，数据要素与管理要素的融合。数据要素蕴藏着大量的信息和知识，丰富的数据资源和先进的数据分析能力与管理要素融合可以提升管理决策的科学性，有利于组织机构的高效沟通和顺畅运行，让企业可以更好地满足客户需求，提高产品和服务质量，创新业务模式，从而在市场竞争中脱颖而出。

In addition, data as a factor of production itself, as an independent factor of production, also directly participates in all links of the economic cycle as a new type of object of labor.

此外，数据要素自身作为独立生产要素，也作为新质劳动对象直接参与经济循环各环节。

(B) The Permeation of Data as a Factor of Production into All Links of the Economic Cycle Promotes the Formation of New Quality Productive Forces

(二)数据要素渗透于经济循环各环节促进新质生产力的形成

The application of data as a factor of production acts on all aspects of social production, triggering comprehensive changes in the economic field, and thereby promoting the realization of new quality productive forces. Data as a factor of production plays a role in the production, circulation, distribution, and consumption links of the economic cycle, smoothing connections, promoting circulation, and driving the formation of new quality productive forces.

数据要素的应用作用于社会生产的各个方面，引发经济领域的全面变革，进而促进新质生产力的实现。数据要素在经济循环的生产、流通、分配、消费环节发挥作用，畅通联系，促进循环，推动新质生产力的形成。

First is the permeation effect of data as a factor of production in the production link. In the production link, the permeation effect exerted by data as a factor of production is mainly manifested in three aspects. First, data as a factor of production integrates with traditional factors of production, promoting the intelligentization and greening of traditional factors of production, giving rise to new factors of production, driving the entire production system toward digitalization and networking, and pushing traditional production models toward high quality, high efficiency, and high technology. While improving traditional factors of production, it acts as a lubricant to coordinate the various joints of production, smooth upstream and downstream relationships, and realize real-time intelligent management of the supply chain. Second, data as a factor of production participates in the production process as an independent factor of production, giving rise to new services and new products. Currently, efficient centralized management and intelligent use of data are the core of the transformation of productive forces. Third, data as a factor of production, as the core link between the digital economy and the real economy, effectively promotes the integration of the digital economy and the real economy, fostering healthy economic development.

首先是数据要素在生产环节的渗透作用。在生产环节中，数据要素所发挥的渗透作用主要表现为三个方面。一是数据要素与传统生产要素融合，促进传统生产要素智能化、绿色化，催生新生产要素，促使整个生产系统数字化、网络化，促使传统的生产模式向高质量、高效能、高科技转型。在改善传统生产要素的同时，是作为润滑剂协调生产的各个关节，畅通上下游关系，实现供给链实时智能化管理。二是数据要素作为独立生产要素参与到生产过程中，催生新服务、新产品。当前，高效集中管理与智能利用数据是生产力变革的核心。三是数据要素作为数字经济和实体经济的核心纽带，有效推进了数字经济与实体经济相融合，促进经济健康发展。

Second is the penetration effect of data elements in the circulation link. In the circulation link, the use of data elements can effectively reduce logistics costs and optimize the supply chain. Data elements circulate through every link of the industrial chain, effectively breaking the data silos under traditional circulation models, efficiently connecting upstream and downstream people, goods, and affairs, and achieving timely and effective communication. This can well solve past problems such as information blockage and information asymmetry between upstream and downstream, improving circulation efficiency and reducing circulation costs. The use of data elements drives the supply chain system toward digital development, effectively coordinating the various subjects of the supply chain and optimizing the supply chain and its combinations. Taking the logistics field as an example, the use of data elements enables enterprises to finely adjust logistics routes and select the most appropriate logistics service providers, thereby improving logistics efficiency and effectively reducing logistics costs. Data elements have driven the emergence of new business forms and new models in the logistics field, such as cross-border e-commerce and smart logistics, injecting new impetus into the exertion of new quality productive forces.

其次是数据要素在流通环节的渗透作用。在流通环节，数据要素的使用能够有效降低物流成本、优化供应链。数据要素流通于产业链的各个环节，能够有效打破传统流通模式下的数据孤岛，将上下游的人、物、事高效地联系在一起，实现及时有效的沟通，能够很好地解决过去上下游信息堵塞、信息不对称等问题，提高流通效率，降低流通成本。数据要素的使用使得供应链体系朝向数字化发展，有效协同供应链各主体，优化供应链以及供应链组合。以物流领域为例，数据要素的利用使企业能够精细化调整物流路线，并选择最恰当的物流服务提供者，以此提高物流效率，有效降低物流成本。数据要素驱动催生了物流领域的新业态和新模式，如跨境电商、智慧物流等，为新质生产力的发挥注入了新动力。

Third is the penetration effect of data in the distribution link. The application of data elements helps optimize resource distribution. On the one hand, data elements optimize the distribution of means of production, rationally mobilizing various elements to maximize effects; on the other hand, the use of data elements can promote the fruits of social development to benefit all the people more broadly, realizing value sharing. This is because fully utilizing data elements can effectively resolve the digital divide problem existing in the current era, bridge the digital dividend gap between different regions and different groups, and promote value sharing. The reusability of data elements, while improving efficiency for distribution activities, can also take fairness into account. The flow and empowerment of data in every link of economic activity can drive participating subjects to form a community of interests, drive the multi-chain integration, cross-chain integration, and intra-chain integration of the industrial chain, value chain, and innovation chain, and foster a value community with a broader scope and deeper dimensions.

再次是数据在分配环节的渗透作用。数据要素的应用有助于优化资源分配。一方面，数据要素优化生产资料的分配，合理调动各要素，实现效应最大化;另一方面，数据要素的使用可以推动社会发展成果更多惠及全体人民，实现价值共享。这是因为充分利用数据要素可以有效化解时代存在的数字鸿沟问题，弥合不同地区、不同群体间的数字红利差距，促进价值共享。数据要素的复用性在为分配活动提高效率的同时还能兼顾公平。数据在经济活动各环节的流动与赋能，可以驱动参与主体形成利益共同体，驱动产业链价值链创新链的多链融合、跨链融合与链内融合，促成了范围更广、维度更深的价值共同体。

Finally is the penetration effect of data in the consumption link. Driven by the development of productive forces, people's needs are increasingly developing toward diversification, differentiation, and individualization. The application of digital technology can better meet people's diverse needs. The collection and analysis of data on people's consumption behavior, consumption preferences, etc., can help enterprises provide better services, including precision marketing and personalized recommendations, optimizing the consumption experience, promoting consumption decisions and trust, expanding consumption boundaries and potential, and improving market efficiency and fairness. Against the backdrop of the rapid development of the internet platform economy, data elements are increasingly becoming a key element linking industry, products, and the consumption end, promoting precise matching of supply and demand. With the support of relevant big data technologies, enterprises can better predict and guide demand, promoting a higher level of supply-demand balance.

最后是数据在消费环节的渗透作用。在生产力发展的驱动下，人们的需求日益向多样化、差异化、个性化发展，而数字技术的应用能够更好地满足人们的多样化需求，对人们消费行为、消费偏好等数据的收集和分析能帮助企业提供更好的服务，包括精准营销与个性化推荐、优化消费体验、促进消费决策与信任、拓展消费边界与潜力以及提高市场效率与公平性等。在互联网平台经济快速发展的背景下，数据要素日益成为衔接产业、产品与消费端的关键要素，推动供需的精准匹配，企业在相关大数据技术支持下可以更好地预测和引导需求，推动更高水平的供需平衡。

(3) Data Elements Act on the Three Elements of Productive Forces to Drive Their Qualitative Transformation

(三)数据要素作用于生产力三要素推动其发生质变

General Secretary Xi Jinping has pointed out that "data, as a new type of production factor, has a major impact on the transformation of traditional production methods." In Marx's view, "the transformation of the mode of production begins with the labor force in manufacture, and with the means of labor in large-scale industry." [6]6 Karl Marx, *Capital*, Vol. 1, Chapter 13, Section 1. The original Chinese quotation in the text is from the standard Chinese edition of Marx and Engels. The means of production are the technical means and technical conditions that cause changes in the mode of production. With the development of science and technology, data elements have become new means of production and new means of labor, driving a qualitative change in productive forces and causing a transformation in the mode of production. New quality productive forces are a modern new type of productive force led by scientific and technological innovation, centered on data elements, and characterized by the application of high and new technologies. Driven by data elements, all three elements of productive forces have been affected and have undergone qualitative change.

习近平总书记指出，“数据作为新型生产要素，对传统生产方式变革具有重大影响”。在马克思看来，“生产方式的变革，在工场手工业中以劳动力为起点，在大工业中以劳动资料为起点”。生产资料是引起生产方式变革的技术手段和技术条件。在科学技术的发展下，数据要素成为新的生产资料，成为新的劳动资料，催生生产力发生质变，引起生产方式的变革。新质生产力是以科技创新为引领，以数据要素为核心，以高新技术应用为主要特征的现代新型生产力。在数据要素的驱动下，生产力三要素都受到了影响，并且发生了质变。

First, data elements promote the formation of a new type of laborer. Laborers are the most active factor in productive forces; only when production factors are combined with laborers can they be transformed into actual productive forces. With the development of the new round of scientific and technological revolution, intelligent manufacturing and artificial intelligence will replace more physical labor and simple labor, giving rise to new human-machine collaboration models. New production conditions require laborers to master more skills and knowledge. Data elements penetrate every industry and field of life, with data scales being huge and structures complex. This requires laborers to cultivate data thinking, enhance digital literacy, and become a new type of laborer. The "14th Five-Year Plan for Digital Economy Development" [7]7 The "14th Five-Year Plan for Digital Economy Development" (《“十四五”数字经济发展规划》) is a policy document issued by the State Council of the People's Republic of China in December 2021, outlining the development goals and tasks for China's digital economy from 2021 to 2025. points out the need to "enhance the digital literacy and skills of the entire population," and laborers need to improve their labor capabilities to match the new mode of production.

一是数据要素促进形成新型劳动者。劳动者是生产力中最活跃的因素，生产要素只有和劳动者相结合才能转化为实际的生产力。随着新一轮科技革命的发展，智能制造和人工智能将会替代更多的体力劳动和简单劳动，催生新的人机协同模式，新的生产条件要求劳动者掌握更多的技能、知识。数据要素渗透到生活的各行各业中，数据规模庞大且结构复杂，这就要求劳动者培养数据思维、提升数字素养，成为新型劳动者。《“十四五”数字经济发展规划》指出，要“提升全民数字素养和技能”，劳动者需提升自己的劳动能力，与新的生产方式相匹配。

Second, data elements give rise to new means of labor. The emergence of massive amounts of data makes data analysis tools a crucial means of labor in production and life. Data analysis tools are not only one of the means to realize the value release of data elements but also indispensable labor tools for digital production in the era of big data. Big data technologies such as artificial intelligence algorithms, cloud computing, and blockchain can effectively collect, store, process, and analyze massive amounts of data. The enormous value hidden behind them has also driven the continuous iteration of digital technology. Therefore, technologies related to data collection and processing are new types of labor tools in the era of the digital economy.

二是数据要素催生新的劳动资料。海量数据的涌现使得数据分析工具在生产和生活中成为至关重要的劳动资料。数据分析工具不仅是实现数据要素价值释放的手段之一，也是大数据时代数字化生产不可或缺的劳动工具。人工智能算法、云计算、区块链等大数据技术可以对海量数据进行有效的采集、存储以及处理分析，其背后蕴藏着巨大价值，也催生了数字技术不断更迭。因此，数据收集、处理等相关技术是数字经济时代的新型劳动工具。

Data elements promote the digital-intelligent transformation of traditional means of labor, release the huge value of traditional means of labor, transform the form of means of production, update labor tools and their modes of action, and give rise to new quality means of labor. On the one hand, the combination of data elements with means of production such as factories and equipment promotes the construction of new infrastructure represented by digital infrastructure, innovating work scenarios into full-scenario applications, realizing the diversification of transaction models, helping enterprises understand market demand, optimize production plans, improve production efficiency, promote industrial chain coordination, and provide a convenient, safe, and efficient foundation of means of production for industrial chain cooperation. On the other hand, data elements, with the help of digital technology, artificial intelligence technology, and integration technology, create network platforms and derive new types of labor tools such as intelligent automated assembly lines and intelligent manufacturing equipment. This reduces manual operations, improves production safety levels, enhances the digitalization and intelligence of production, reduces resource consumption, accelerates the green transformation of production, updates labor tools and their modes of action, and gives rise to new quality means of labor.

数据要素推动传统劳动资料的数智化转型，释放传统劳动资料的巨大价值，变革生产资料形式，更新劳动工具及其作用方式，催生新质劳动资料。一方面，数据要素与厂房、设备等生产资料的结合，推动了以数字基础设施为代表的新型基础设施建设，将工作场景革新为全场景应用，实现交易模式的多样化，有利于企业了解市场需求，优化生产计划，提高生产效率，促进产业链协同，为产业链合作提供便捷、安全、高效的生产资料基础。另一方面，数据要素借助数字技术、人工智能技术、融合技术等，创造网络平台，衍生智能化自动化流水线、智能制造装备等新型劳动工具，这减少了人工操作，提升生产安全水平，增强了生产的数字化、智能化，减少资源消耗，加快生产的绿色化变革，更新劳动工具及其作用方式，催生新质劳动资料。

Third, data elements give rise to new objects of labor. Objects of labor refer to the objects upon which laborers act, either by themselves or by using production tools, including natural objects of labor and man-made objects of labor. Different levels of productive forces have correspondingly adapted objects of labor. On the one hand, data elements can participate in material production as objects of labor, such as data resources, data services, and data products; on the other hand, data elements combine with traditional production factors, changing the condition of objects of labor and expanding the scope of objects of labor. The widespread use of data can expand the types and quantities of traditional objects of labor, generate more intelligent objects of labor, increase the added value of original objects of labor, thereby expanding the field of production and promoting the progress of productive forces.

三是数据要素孕育新的劳动对象。劳动对象是指劳动者通过自身或使用生产工具作用于其上的对象，包括自然劳动对象和人为劳动对象，不同的生产力水平有与之相适配的劳动对象。一方面，数据要素可以作为劳动对象参与到物质生产中，如数据资源、数据服务、数据产品等;另一方面，数据要素与传统生产要素相结合，改变了劳动对象的状况和扩大劳动对象范围。数据的广泛运用能够扩充传统劳动对象的种类和数量，会产生更智能化的劳动对象，提高原有劳动对象的附加值，从而拓展生产领域并推动生产力的进步。

III. The Industrial Generation of Data Elements and New Quality Productive Forces

三、数据要素与新质生产力的产业生成

At the Central Economic Work Conference held in December 2023, General Secretary Xi Jinping proposed that "we must promote industrial innovation through scientific and technological innovation, especially by using disruptive technologies and cutting-edge technologies to give rise to new industries, new models, and new drivers, and develop new quality productive forces." [8]8 The Central Economic Work Conference (中央经济工作会议) is an annual high-level meeting convened by the CPC Central Committee to set economic policy priorities for the following year. The December 2023 meeting was held in Beijing. The development and penetration of data elements provide the foundation and carrier for the formation of new quality productive forces. In the interactive process between data elements and new quality productive forces, new industries are generated, new models are constructed, traditional industries are upgraded, and production models are optimized.

习近平总书记在2023年12月召开的中央经济工作会议上提出，“要以科技创新推动产业创新，特别是以颠覆性技术和前沿技术催生新产业、新模式、新动能，发展新质生产力”。数据要素的发展渗透为新质生产力的形成提供了基础和载体，数据要素在与新质生产力的互动过程中催生新产业、构建新模式，升级传统产业、优化生产模式。

(1) Generating New Industries and Constructing New Models

(一)催生新产业、构建新模式

The widespread application of data as a factor of production has given rise to new industries. Typical examples of new industries are the big data industry, the artificial intelligence industry, and the blockchain industry. The big data industry mainly involves data collection and storage, analysis and mining, and so on. Big data analysis companies use various data analysis tools and algorithms to conduct in-depth analysis and mining of massive data, in order to discover potential patterns, trends, and correlations within it. For example, through the analysis of consumer behavior data, enterprises can better understand customer needs and optimize products and services; financial institutions can conduct risk assessment and investment decisions through the analysis of market data. Data mining technology can also be applied in fields such as healthcare, transportation, and energy, providing strong support for the development of these fields. The artificial intelligence industry is highly dependent on the supply of high-quality data; data as a factor of production provides training materials for machine learning and deep learning. Enterprises and research institutions use large amounts of data to train machine learning models, enabling them to automatically learn and improve. For example, technological progress in fields such as image recognition, speech recognition, and natural language processing all rely on substantial data support. Blockchain technology can provide guarantees for the secure storage and transmission of data, ensuring the authenticity, integrity, and immutability of data through means such as encryption and distributed storage. For example, in the financial field, blockchain can be used to protect customers' transaction data and personal information; in the healthcare field, blockchain can ensure the security of patients' medical record data. Of course, the combination of data as a factor of production and new digital technologies with different industries has also given rise to many industries, such as smart agriculture and smart healthcare, demonstrating the morphological evolution of "original industry + new technology = new industry."

数据要素的广泛应用催生了新产业。新产业的典型代表是大数据产业、人工智能产业、区块链产业等。大数据产业主要涉及数据的采集与存储、分析与挖掘等。大数据分析公司利用各种数据分析工具和算法，对海量数据进行深入分析和挖掘，以发现其中的潜在模式、趋势和关联。例如，通过对消费者行为数据的分析，企业可以更好地了解客户需求，优化产品和服务;金融机构可以通过对市场数据的分析，进行风险评估和投资决策。数据挖掘技术还可以应用于医疗、交通、能源等领域，为这些领域的发展提供有力支持。人工智能产业十分依赖高质量的数据供给，数据要素为机器学习和深度学习提供了训练材料。企业和研究机构利用大量的数据训练机器学习模型，使其能够自动学习和改进。例如，图像识别、语音识别、自然语言处理等领域的技术进步，都离不开大量的数据支持。区块链技术可以为数据的安全存储和传输提供保障，通过加密和分布式存储等手段，确保数据的真实性、完整性和不可篡改性。例如，在金融领域，区块链可以用于保护客户的交易数据和个人信息;在医疗领域，区块链可以确保患者的病历数据安全。当然，数据要素和新的数字技术与不同产业的结合还催生出许多产业，如智慧农业、智慧医疗等，展现了“原有产业+新技术=新产业”的形态演变。

Data as a factor of production has given rise to new models. The first is a new production model. First, production has transformed from a traditional production model to an intelligent production model. Driven by data as a factor of production, the positions of consumers and producers have changed, shifting from producer dominance in the past to consumer dominance; consumers can influence the product production process to a greater extent, achieving personalized customization. Second, the widespread use of sensor equipment in the production process can comprehensively collect data during the production process, enabling precise control and grasp of every link in production, reducing errors, and improving the utilization efficiency of the means of production.

数据要素催生了新模式。首先是新的生产模式。一是生产从传统生产模式转型为智能生产模式。在数据要素的驱动下，消费者和生产者的地位发生变化，由过去的生产者主导转变为消费者主导，消费者可以更大程度地影响产品生产过程，实现个性化定制。二是传感器设备在生产过程的广泛使用，可以全方位收集生产过程中的数据，对生产的每一环节实现精准控制和把握，减少失误，提高了生产资料的利用效率。

The second is a new business model. The development of the digital economy has promoted online-offline integration, enabling enterprises to quickly respond to market demands and technological changes and launch new services and functions. This is typified by the platform model. Typical digital business platforms include Taobao, Pinduoduo, Douyin, Xiaohongshu, and so on. [9]9 Taobao is an e-commerce platform; Pinduoduo is a social e-commerce platform; Douyin is the Chinese version of TikTok; Xiaohongshu (Little Red Book) is a social commerce and lifestyle platform. This type of business model integrates various offline resources online, gathering a large number of suppliers and demanders, who can quickly and accurately connect with each other to meet short-cycle, high-frequency, fast-paced demands. In this model, data as a factor of production makes resource sharing more efficient and convenient. The shared mobility model, represented by shared bicycles and shared cars, achieves real-time vehicle positioning, booking, use, and management through IoT technology and big data analysis. Platforms can conduct intelligent dispatch based on user demand and vehicle distribution, improving vehicle utilization rates. In the shared accommodation field, platforms provide users with personalized accommodation recommendations by collecting and analyzing data such as property information and user reviews. At the same time, hosts and guests can conduct safe and convenient transactions through the platform. The sharing economy model makes full use of idle resources, reduces resource waste, and also provides users with more flexible and convenient service options.

其次是新的商业模式。数字经济的发展促进了线上线下融合，企业能够快速响应市场需求和技术变革，推出新的服务和功能，这以平台模式为典型。典型的数字商业平台如淘宝、拼多、抖音、小红书等。这类商业模式把线下各类资源整合在线上，聚集大量的供应方和需求方，双方能够快速准确地进行对接，满足短、频、快的需求。在这种模式里，数据要素使得资源共享变得更加高效和便捷。以共享单车、共享汽车为代表的共享出行模式，通过物联网技术和大数据分析，实现了车辆的实时定位、预约、使用和管理。平台可以根据用户的需求和车辆的分布情况，进行智能调度，提高车辆的利用率。在共享住宿领域，平台通过收集和分析房源信息、用户评价等数据，为用户提供个性化的住宿推荐。同时，房东和租客也可以通过平台进行安全、便捷的交易。共享经济模式充分利用了闲置资源，减少了资源浪费，同时也为用户提供更加灵活、便捷的服务选择。

The third is a new organizational model. Data as a factor of production has driven changes in enterprise organizational structure, shifting it towards platformization and networking. Data as a factor of production promotes the sharing and transparency of information within organizations, greatly reducing the cost of collaboration among enterprise employees, breaking down information silos between departments, and enabling good resource sharing and efficient decision-making. The development of digital technology has made remote collaboration and off-site work a reality, making enterprise organizations more flexible and efficient. In addition, with the widespread use of digital devices, enterprises can comprehensively monitor workers.

最后是新的组织模式。数据要素催生企业组织结构变革，使其转向平台化、网络化。数据要素促进了组织内部信息的共享与透明化，大大降低了企业员工的协作成本，打破了部门之间的信息壁垒，能够很好地实现资源共享和高效决策。数字技术的发展让远程协作和异地办公成为现实，这使得企业组织更加灵活和高效。另外，在数字设备的广泛使用下，企业能够全面监督劳动者。

(II) Upgrading Traditional Industries and Optimizing Production Models

(二)升级传统产业、优化生产模式

The integrated development of data as a factor of production with other factors of production has effectively promoted the optimization and upgrading of traditional industries. On the one hand, it promotes deep connections between various industries, realizing the digital and intelligent transformation of traditional industries, promoting the optimization and reorganization of industrial chains, and driving the transformation of the economic structure; on the other hand, it promotes internal change and innovation within industries, promoting the construction of a more efficient and flexible production system, thereby driving the development of new quality productive forces. The following provides specific explanations using manufacturing, agriculture, and commerce as examples.

数据要素与其他生产要素的融合发展，有效地推动了传统产业优化升级，一方面，促进各个产业之间的深度连接，实现传统产业的数字化和智能化转型，促进产业链的优化重组，推动经济结构转型;另一方面，促进产业内部变革创新，推动构建更加高效灵活的生产体系，从而推动新质生产力的发展。下文以制造业、农业和商业为例进行具体说明。

The use of data as a factor of production promotes the adjustment of traditional manufacturing from mechanization in the past to automation and intelligence, which also implies the digital transformation of enterprises, giving rise to many new products or new services. The upgrading of manufacturing to automation and intelligence means an increasing degree of data participation. By installing sensors and other devices on production equipment, manufacturing can easily achieve data acquisition across the entire production process, the entire chain, and the entire lifecycle. Through the mining, analysis, and real-time mastery of data, enterprises can, first, monitor the production process in real time, predict equipment failures for advance maintenance, reduce the risk of production interruptions, and ensure production continuity; second, they can continuously optimize production parameters based on data analysis, improving product quality and production parameters. For example, automobile manufacturing enterprises, through the analysis of production line data, precisely adjust production parameters to ensure stable automobile quality. In addition, enterprises can also connect production-side data with consumption-side data, enabling traditional enterprises to better meet consumers' personalized needs, thereby reducing inventory pressure and lowering costs.

数据要素的使用推动传统制造业从过去的机器化向自动化、智能化调整，这也意味着企业的数字化转型，催生出许多新产品或新服务。制造业向自动化、智能化升级，意味着数据参与程度越来越高。在生产设备上安装传感器等装置，制造业很容易实现生产全流程、全链条和全周期的数据获取。企业通过对数据的挖掘分析和实时掌握，首先能够实时监控生产过程，预测设备故障以提前进行维护，降低生产中断风险，保障生产连续性;其次可以根据数据分析不断优化生产参数，提升产品质量和生产参数。例如，汽车制造企业通过对生产线数据的分析，精准调整生产参数，确保汽车质量稳定。另外，企业还可以将生产端数据和消费端数据连通起来，传统企业能更好地满足消费者的个性化需求，进而减少库存压力、降低成本。

The application of data as a factor of production promotes the development of agriculture towards smart agriculture and precision agriculture. Farmers and agricultural technicians can use sensors, satellite remote sensing, and other technologies to collect data on soil, climate, crop growth, and so on. By analyzing this data, farmers can accurately understand field conditions, achieving precision fertilization, precision irrigation, precision pest and disease control, and so on. The consumer market can use data to establish agricultural product traceability systems, recording data on the entire process of agricultural products from planting, processing, and transportation to sales. By analyzing historical data on agricultural product markets, consumption trends, and other information, farmers and agricultural enterprises can better predict market demand, rationally arrange planting plans, and avoid blind production.

数据要素的应用推动农业向智能农业和精细农业发展。农民和农业技术人员可以运用传感器、卫星遥感等技术收集土壤、气候、作物生长等数据。通过分析这些数据，农民可以精确了解农田状况，实现精准施肥、精准灌溉、精准防治病虫害等。消费市场可以利用数据建立农产品溯源系统，记录农产品从种植、加工、运输到销售的全过程数据。通过分析农产品市场历史数据、消费趋势等信息，农民和农业企业可以更好地预测市场需求，合理安排种植计划，避免盲目生产。

The application of data as a factor of production promotes changes in business models, transforming past offline business models into current platform models. Platforms can collect data such as consumers' shopping history and browsing records to create precise user profiles. Based on user profiles, retailers can push personalized product recommendations and promotional information to consumers, improving marketing effectiveness.

数据要素的应用推动商业模式发生变革，使得过去线下的商业模式转变成现在的平台模式。平台可以收集消费者购物历史、浏览记录等数据，进行精准用户画像。基于用户画像，零售商可以向消费者推送个性化商品推荐和促销信息，提高营销效果。

IV. Practical Pathways for Data as a Factor of Production to Accelerate the Development of New Quality Productive Forces

四、数据要素加快新质生产力发展的实践路径

The "Decision of the Central Committee of the Communist Party of China on Further Comprehensively Deepening Reform to Advance Chinese-Path Modernization" (hereinafter referred to as the "Decision") [10]10 This refers to the resolution adopted at the Third Plenary Session of the 20th Central Committee of the CPC in July 2024. points out that education, science and technology, and human resources are the foundational and strategic pillars of Chinese-path modernization. Only by统筹推进 the coordinated development of education, science and technology, and human resources as a trinity can productive forces be better developed. [11]11 The Chinese term 统筹推进 (tongchou tuijin) means to coordinate and advance in a unified and holistic manner. Furthermore, it is necessary to improve the mechanisms and systems of the talent chain, technology chain, and industrial chain, using systematic mechanisms and systems to leverage data as a factor of production to support the development of new quality productive forces.

《中共中央关于进一步全面深化改革、推进中国式现代化的决定》(下称《决定》)指出，教育、科技、人才是中国式现代化的基础性、战略性支撑。只有统筹推进教育、科技、人才三位一体协调发展，才能更好地发展生产力。此外，要完善人才链—技术链—产业链的机制体制，以系统性机制体制运用数据要素助力新质生产力的发展。

(I) Actively Cultivating Digital and Intelligent Technology Talent and Optimizing the Talent Cultivation Mechanism

(一)积极培育数智技术人才，优化人才培育机制

The driving force of economic development comes from innovation, and innovation requires talent. Currently, the problem of insufficient supply of high-quality digital and intelligent talent in our country is very prominent; the scale of talent cannot meet the demand for high-quality digital and intelligent talent brought about by the rapid iteration and updating of the digital industry and the digital transformation of traditional industries. In view of this, our country must strengthen the cultivation of digital and intelligent technology talent, optimize the talent cultivation mechanism, and guide workers in traditional industries with surplus productive forces to transition into digital and intelligent talent. This can not only alleviate employment problems but also provide society with talent for new quality productive forces.

经济发展的动力来自创新，创新需要人才。当前，我国高质量数智人才供给不足的问题十分显著，人才规模无法满足数字产业迅速迭代更新及传统产业数字化转型带来的对高素质数智人才的需求。鉴于此，我国必须加强数智技术人才的培育，优化人才培育机制，引导生产力过剩的传统产业工人向数智人才转型，这不仅能够缓解就业问题，还能为社会提供新质生产力人才。

First, it is necessary to establish and improve a talent cultivation model. First, institutions of higher education should optimize their disciplinary structures in accordance with the latest trends in scientific development, and set up different cultivation models according to the requirements for developing new quality productive forces. On the one hand, universities should cultivate high-level, cutting-edge scientific and technological innovation talent, strengthening school-enterprise cooperation through an industry-academia-research collaborative model, jointly building artificial intelligence and big data industry colleges and various digital technology experimental platforms with high-tech enterprises, and enhancing the output of high-level talent and innovative achievements. On the other hand, universities should cultivate applied talent proficient in new digital and intelligent technologies, strengthen the cultivation of students’ practical abilities, and enhance the role of vocational colleges in cultivating talent related to data elements.

首先，要建立健全人才培养模式。一是高等院校根据科学发展最新趋势，优化学科设置，根据新质生产力发展要求设置不同的培养模式。一方面，高校要培养高精尖的科技创新人才，通过产学研协同模式，加强校企合作，与高新企业共建人工智能和大数据产业学院、各类数字技术实验平台，加强高水平人才培养和创新成果产出。另一方面，高校要培养熟练掌握新的数智科技的应用人才，加强学生实践能力的培养，加强职业院校在数据要素相关人才培养方面的作用。

Second, it is necessary to strengthen vocational training and education, enhance talent quality, and provide workers with timely training content related to data, such as data analysis and mining and artificial intelligence-related knowledge, to improve workers’ data literacy. The new models and new business forms brought by emerging technologies are constantly emerging, which also requires workers to possess lifelong learning capabilities. Relevant institutions should promptly strengthen vocational training in accordance with the development of digital and intelligent technologies.

二是要加强职业培训教育，提升人才素养，为劳动者及时提供数据相关的培训内容，如数据分析与挖掘、人工智能相关知识，提升劳动者数据素养。当前新兴技术带来的新模式新业态层出不穷，也要求劳动者具备终身学习能力，相关机构应该及时根据数智技术的发展加强职业培训。

Third, it is necessary to strengthen inclusive education in digital and intelligent technologies to alleviate the digital divide. The rapid development of science and technology is increasingly changing people’s modes of production and life, and is also quietly causing a huge divide between people of different nationalities and regions. All competition between nations ultimately boils down to competition in the quality of their citizens. It is currently very necessary for China to enhance the digital and intelligent literacy of the entire population. Therefore, enterprises, governments, and society should actively promote inclusive education in digital and intelligent technologies, so that the fruits of development benefit a broader range of people.

三是加强数智技术的普惠性教育，缓解数字鸿沟问题。科学技术的飞速发展日益改变着人们的生产生活方式，也悄然引发不同国籍、不同区域人群之间的巨大鸿沟。国家之间的一切竞争，归根到底是各国国民素质的竞争，我国当前十分有必要提升全民的数智素质。因此，企业、政府和社会应该积极推广数智技术的普惠性教育，让发展成果惠及更广泛的人群。

(II) Build and improve a data element market system

(二)构建完善的数据要素市场体系

The “Decision”[12]12 This refers to the “Decision of the Central Committee of the Communist Party of China on Further Comprehensively Deepening Reform and Promoting Chinese-Path Modernization,” adopted at the Third Plenary Session of the 20th Central Committee of the CPC in July 2024. proposes building a unified national market, cultivating a nationally integrated technology and data market, and improving the mechanism whereby factors of production such as labor, capital, land, knowledge, technology, management, and data have their contributions evaluated by the market and their remuneration determined according to their contributions. The market system is an objective organic system composed of market elements, an organic whole consisting of commodity markets such as consumer goods markets; factor markets such as capital, labor, technology, information, and real estate markets; and special trading markets such as futures, auctions, and property rights markets. At present, China’s data element market has not yet been fully opened, which may lead to insufficient supply of data elements and is not conducive to data playing a positive role as a new type of factor of production. Data is the core of the emergence of new quality productive forces. Therefore, China should strengthen the construction of data infrastructure, promote the opening of public data, cultivate data element entities, and improve the supply mechanism for data resources.

《决定》提出要构建全国统一大市场，培育全国一体化技术和数据市场，健全劳动、资本、土地、知识、技术、管理、数据等生产要素由市场评价贡献、按贡献决定报酬的机制。市场体系是由市场要素构成的客观有机系统，是由消费品等商品市场，资本、劳动力、技术、信息、房地产市场等要素市场，以及期货、拍卖、产权等特种交易市场组成的相互联系、互为条件的有机整体。目前，我国数据要素市场尚未充分开放，这可能会导致数据要素供应不足，不利于发挥数据作为新型生产要素发挥积极作用。数据是新质生产力涌现的核心，因此，我国要加强数据基础设施建设、推动公共数据开放和培育数据要素主体，完善数据资源的供应机制。

First, improve data infrastructure. The supply and use of data elements require the deployment of corresponding digital infrastructure equipment. Therefore, China needs to proceed from the perspective of a unified national market, promote the construction and improvement of digital infrastructure such as information and communication technology, big data, and the Internet of Things, and provide sufficient technical support and hardware guarantees for the production and supply of data elements. In addition, relevant departments should optimize the layout of computing resources such as cloud platforms and supercomputing centers. These facilities not only support various digital technologies but also promote the generation of data elements. All localities should especially avoid blindly establishing cloud computing data centers, and avoid turning the construction of cloud computing and related infrastructure into “land GDP.”[13]13 “Land GDP” is a Chinese term criticizing local governments for relying on land sales and real estate development to drive short-term economic growth, rather than pursuing sustainable development. Instead, they should proceed from the perspective of a unified national market, coordinate the arrangement of data infrastructure, and build a facility system with regional coordination and complete functions.

首先，完善数据基础设施。数据要素的供给和使用需要配备相应的数字基础设施设备。因此，我国需要从全国统一大市场出发，推进信息通信技术、大数据和物联网等数字基础设施的建设和完善，为数据要素的生产与供应提供充分的技术支持和硬件保障。此外，相关部门还应优化云平台、超算中心等计算资源的布局，这些设施不仅支持各种数字技术，还能促进生成数据要素。各地尤其要避免盲目建立云计算数据中心，避免把云计算等相关基础设施建设变成“土地GDP”，应从全国统一大市场出发，协调安排数据基础设施，构建区域协同、功能完备的设施体系。

Second, establish basic market-oriented standards for data elements. Currently, data elements have not yet possessed mature basic market-oriented standards like traditional factors. This requires us to accelerate the establishment and improvement of basic standards for data elements, such as rights confirmation, pricing, and asset evaluation, to lay a foundation for the smooth circulation of data elements. First, accelerate the establishment of unified data standards, strengthen cross-domain and cross-departmental data standard formulation and coordination work, and promote the standardization and normalization of data formats. Second, conduct in-depth research and determine the ownership allocation and trading rules for data elements, which is the basis for data rights confirmation. At the same time, China should accelerate the introduction of data element pricing models and trading tools that meet market demand, and establish unified standards for the statistics, accounting, and entry of data assets, so as to provide clear basic standards for the marketization of data elements.

其次，建立数据要素市场化基础标准。目前数据要素还未能像传统要素那样具备成熟的市场化基础标准，这就需要我们加快建立健全数据要素的确权、定价及资产评估等基础性标准，为数据要素的畅通循环打好基础。一是加快建立统一数据标准，加强跨领域、跨部门的数据标准制定和协调工作，推动数据格式的标准化和规范化;二是深入研究并确定数据要素的权属分配和交易规则，这是数据确权的基础。同时，我国应加快推出适应市场需求的数据要素定价模式和交易工具，并建立统一的数据资产统计、核算和入账标准，以便为数据要素市场化提供清晰的基础标准。

Third, accelerate the improvement of the data element trading market system. Although China has made progress in building a data trading market, it has not yet established a multi-level market system that can meet the rapidly growing development needs of data elements. Therefore, China should accelerate its pace and build a data element trading market system with diversified market dimensions, including establishing a unified data element trading platform at the national level, while setting up regional data trading hubs in qualified areas; promoting mutual reinforcement between enterprise-led over-the-counter trading markets and government-led on-exchange trading markets; orderly developing benign competition between primary and secondary data element markets; and coordinating the planning and piloting of the coordinated development of comprehensive and specialized data element markets.

再次，加快健全数据要素交易市场体系。我国虽然在数据交易市场建设方面已取得进展，但尚未建立起一个能够满足迅速增长的数据要素发展需求的多层次市场体系。因此，我国应加快步伐，构建一个多元化市场维度的数据要素交易市场体系，包括在国家级层面上设立统一的数据要素交易平台，同时在有条件的地区建立区域性数据交易枢纽;推动企业主导的场外交易市场与政府主导的场内交易市场相互促进;有序地发展一级和二级数据要素市场的良性竞争;统筹规划和试点推进综合与专业数据要素市场的协调发展。

(III) Accelerate the construction of a modern industrial system

(三)加快构建现代产业化体系

A modern industrial system is the core goal of cultivating and developing new quality productive forces. We should address the development needs of new quality productive forces, remove obstacles to industrial development, promote the deep transformation and upgrading of traditional industries, lay out the industrial chain around the innovation chain, use breakthrough technologies and cutting-edge technologies to give rise to strategic emerging industries and future industries, and promote the development of new quality productive forces.

现代化产业体系是培育和发展新质生产力的核心目标。我们要针对新质生产力的发展需求，清除产业发展的障碍，推动传统产业进行深度的转型升级，围绕创新链来布局产业链，利用突破性技术和前沿技术催生战略性新兴产业和未来产业，促进新质生产力的发展。

First, it is necessary to transform and upgrade traditional industries, promote their development towards high-end, intelligent, and green directions, form a digital industrial ecosystem across the whole society as soon as possible, and provide broad space for the development of new quality productive forces. China should, through the integration of the real economy and the digital economy, restructure the division of labor within the industrial chain, promote the deep integration of data elements, digital technologies, and digital application scenarios, accelerate the digital transformation process of traditional industries, build a modern industrial system, and form digital industry clusters with international competitiveness.

首先，要改造升级传统产业，推动传统产业向高端化、智能化、绿色化发展，早日形成全社会范围内的数字化产业生态系统，为新质生产力的发展提供广阔的空间。我国应通过实体经济与数字经济融合，重新构建产业链中的分工结构，推动数据要素、数字技术与数字应用场景的深度整合，加速传统产业的数字化转型进程，打造现代产业体系，并形成具有国际竞争力的数字产业集群。

Second, it is necessary to cultivate and strengthen emerging industries, and promote the deep integration of digital technology and the real economy. China should implement the “Data Elements ×” action plan, enhance the capacity of the real economy, continuously improve the scientificity, precision, and systematicity of serving the real economy, provide strong guidance and technical support for the modernization of the real economy, form a modern industrial system led by new industries, and contribute to the development and prosperity of new quality productive forces.

其次，要培育壮大新兴产业，促进数字技术和实体经济深度融合。我国应实施“数据要素×”行动计划，增强实体经济的能力，不断提高服务实体经济的科学性、精确性和系统性，为实体经济现代化提供强有力的引导和技术支持，形成新产业为主导的现代化产业体系，助力新质生产力的发展繁荣。

Finally, it is necessary to accelerate the optimization of pathways for transforming cutting-edge technologies, and enhance forward-looking strategic layout for future industries. First, strengthen collaboration between the science and technology sectors and the industrial sector, use cutting-edge technological capabilities to meet important needs and scenario-driven demands, and build a coherent innovation and transformation system of “research—verification—incubation—application—promotion.” Second, forecast and evaluate the development trends of global technology frontiers and future industries, combine them with China’s resource conditions, industrial base, and economic development goals, plan the roadmap for the development of future industries, focus on directions such as future manufacturing, future energy, future space, and future materials, vigorously develop digital frontier industries such as quantum computers and brain-computer interfaces, ensure that China seizes the first-mover advantage in future development, and establish and improve supporting policies for the development of future industries.

最后，要加速优化尖端科技转化路径，增强对未来产业的前瞻性战略性布局。一是强化科技与产业部门的协作，利用前沿技术能力来满足重要需求和场景驱动，打造一个连贯的“科研—验证—孵化—应用—推广”的创新和转化体系;二是对全球科技前沿和未来产业的发展趋势进行预测和评估，结合我国的资源条件、产业基础和经济发展目标，规划未来产业的发展路线图，重点关注未来制造、未来能源、未来空间、未来材料等方向，大力发展量子计算机、脑机接口等数字前沿产业，确保我国掌握未来发展的先机，建立和完善未来产业发展的配套政策。

(The authors are, respectively, a doctoral student at the School of Marxism, University of Chinese Academy of Social Sciences; and an associate professor at the School of Marxism, Shanghai Maritime University.)

（作者系中国社会科学院大学马克思主义学院博士研究生；上海海事大学马克思主义学院副教授）

Online editor: Ma Jingren

网络编辑：马京仁