Yu Xiaoshuang and Yin Xing: Data Elements and New Quality Productive Forces: Theoretical Logic and Practical Paths
At present, a new round of technological revolution and industrial transformation is accelerating. Global technological innovation has entered a period of high intensity and activity, triggering profound changes in economy and society. Many emerging business formats are constantly surfacing, future industries such as artificial intelligence and gene technology are achieving consistent breakthroughs, and the global economic landscape is being continuously reshaped. The world is facing great changes unseen in a century [1]; Western countries, led by the United States, have strengthened their measures regarding technological hegemony and blockades, leaving the Chinese economy to face both immense challenges and developmental opportunities. Against such a domestic and international background, General Secretary Xi Jinping, rooted in the reality of China’s economic development, put forward the major thesis of developing "new quality productive forces."
With the vigorous development of the digital economy and the accelerating pace of digital industrialization and industrial digitization, data has become a foundational resource and core factor of production for enterprises and even national competition. General Secretary Xi Jinping pointed out the need to "build a digital economy with data as a key factor." In 2019, the Fourth Plenary Session of the 19th CPC Central Committee included data as a factor of production for the first time; since then, the state has attached increasing importance to the vital role of data in economic strategic layout. In March 2020, the Opinions of the CPC Central Committee and the State Council on Building a More Perfect System and Mechanism for the Market-Based Allocation of Factors pointed out the need to accelerate the cultivation of the data factor market. In December 2021, the “14th Five-Year Plan” for Digital Economy Development issued by the State Council proposed to fully release the value and activate the potential of data factors. In December 2022, the Opinions of the CPC Central Committee and the State Council on Building Basic Data Systems to Better Play 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 Element ×” Three-Year Action Plan (2024–2026), jointly issued by 17 departments including the National Data Bureau, made detailed work deployments regarding the multiplier effect of data factors. This series of national-level policies fully demonstrates the important role of data factors in current economic development. This is a matter of 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 promoting the building of a great power and national rejuvenation through Chinese-path modernization.
Data factors empower new materials, new energy, new equipment, and the new industries and new drivers they spawn, 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. Given this, as the core factor of the digital economy, data has logically become the core factor of new quality productive forces. At present, the academic community's analysis of the relationship between data factors and new quality productive forces mainly involves the following perspectives: first, discussing the mechanism by which data factors empower new quality productive forces from the perspective of the multiplier effect; second, researching the relationship between the two by exploring how data factors promote the development of new quality productive forces through social production processes such as production, circulation, distribution, and consumption; third, exploring the relationship from the perspective of total factor productivity, which is the core hallmark of new quality productive forces; and fourth, exploring how to drive the market-oriented allocation of data factors through application scenarios to promote the development of new quality productive forces. Generally speaking, current academic research on new quality productive forces is in a stage of comprehensive expansion, and the discussion on the relationship between the two remains to be deepened. Based on Marxist political economy and proceeding from the techno-economic characteristics of data and data factors themselves, this article explores how the integrated development of data factors and traditional factors of production in various links of the total process of social production forms new quality productive forces. It discusses from a micro level how data factors promote the generation of industries of new quality productive forces, and finally puts forward suggestions for practical paths for data factors to accelerate the development of new quality productive forces.
I. Techno-economic Characteristics of Data Factors
"Data" has become a "high-frequency word" in today's human society. Whether in scientific research literature, government work reports, or daily discourse, "data" is ubiquitous and ever-present. The concept of data emerged only in the era of information civilization, developing out of numbers, digits, and numerical computation; the development of digits 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 as a basis for various statistics, calculations, scientific research, or technical designs. From the birth of the first computer in 1946 to the present, the connotation of the concept of "data" has expanded; it refers not only to numerical values, but its scope is much larger than digits. This article draws on the following definition of data: "Data-as-basis [2] includes quantities, values, and digits, and acts as a substrate that can be transformed into information." Data has become increasingly important precisely because it contains information and knowledge and can be processed through computer technology.
Data itself possesses the following characteristics. First, virtuality and infinity. The virtuality of data is manifested in its non-physicality; data must rely on physical entities to exist. This non-physicality allows data to be copied, disseminated, and shared infinitely by people. Although the costs of collection, cleaning, storage, and analytical application are high, the marginal cost of its repetitive use approaches zero. Second, reproducibility and shareability. As a carrier of information, once data is generated, it can be copied, transmitted, and shared by people at different times and spaces and on different physical carriers. With the rapid development of cloud computing today, this reproducibility and shareability have been greatly expanded, allowing people to store or use data from the cloud at any time. Third, non-rivalry and non-depletability. Both the objective form of data's existence and its natural attribute of being infinitely reproducible at low cost determine that the same data can be utilized by multiple subjects simultaneously without mutual interference, possessing non-rivalry and non-depletability. Fourth, timeliness. People produce data every day; the progress and development of digital technology and changes in the social environment inevitably cause the value of the information carried by data to gradually decay over time.
Precisely because of these characteristics of data itself, data factors possess the following techno-economic characteristics.
First, the intersectional permeability of data factors. Factors of production form specific structures through combination and coordination. As production content enriches and economic structures become complex, these types of factors have expanded from land and labor and continue to evolve into various combinations. The intersectional permeability of data is mainly reflected in two aspects: first, data can influence other factors of production during its flow through various links, continuously improving the quality of traditional factors and increasing their output efficiency—the integration of data factors with other factors can achieve complementary resource advantages; second, each link generates new data to promote the improvement of data factors themselves.
Second, exclusivity. In theory, because data is characterized by virtual infinity, reproducibility, and shareability, it should not be exclusive. However, differences in ownership rights directly create this exclusivity, as seen in corporate data and personal data. Moreover, even if most of this data is public or its ownership is unclear, exclusivity can be caused by the differing data collection and storage capabilities of different subjects. "When the scale of data is large enough and the content is complex and extensive enough, the data factor of production exhibits high exclusivity; enterprises and institutions possessing data will choose to 'hoard' rather than share it." It should be noted that collected data generally needs to be cleaned and analyzed before it can be put into use, which also creates exclusivity.
Third, positive and negative externalities. "Externality" refers to the phenomenon where the economic behavior of an enterprise causes other departments outside the enterprise to suffer losses or obtain benefits. "Externalities" can produce positive or negative effects. Positive effects occur when the spillover of benefits generated by an enterprise's economic behavior benefits other enterprises while its own benefits decrease; negative effects occur when the escape of costs generated by an enterprise's economic behavior causes other enterprises to suffer losses while its own losses decrease. The externality of data encompasses both positive and negative externalities. For enterprises, the positive externality of data is first reflected in the improved production efficiency of data-collecting enterprises. Digital platforms and some traditional industries—such as finance, media, and manufacturing enterprises, especially large Internet companies—possess massive amounts of user data. This data, including records of user browsing, clicking, searching, and consuming, is used to promote innovation and improve operations and service quality, thereby expanding the user scale. The negative externality of data is manifested in data falsification and data abuse. For example, the irregular use of consumer data by enterprises infringes on consumer privacy rights. The non-rivalry of data factors allows the personal information of a data subject to be collected and used illegally by multiple actual data holders across different real-world scenarios, directly infringing upon the data subject's privacy rights, increasing the probability of privacy leaks, and harming the individual welfare of consumers. Furthermore, the phenomenon of data falsification in the digital era greatly harms society. Big data "price discrimination against existing customers" [3] is a typical behavior of personal data abuse and a manifestation of the negative externality of data factors. For individuals, the negative externality of data is mainly manifested as the infringement of privacy and damage to welfare by enterprises and other entities; for the state, the negative externality of data may affect national security.
Fourth, increasing returns to scale. Data is the carrier of information; the more voluminous and comprehensive the data, the more people can mine valuable content from it. More data is conducive to discovering the latent knowledge behind the data, finding the connections between things, and more comprehensively reflecting an object, which helps us make accurate judgments. Therefore, data factors possess the characteristic of increasing returns to scale.
Fifth, ambiguity of property rights. There is a certain degree of ambiguity in the ownership of data factors of production, and the distribution of ownership and various outputs between enterprises and consumers is not yet clear. In the process of using various information and communication technology (ICT) products and services provided by companies like Internet firms, consumers generate a large amount of data. This data is often directly collected and organized by enterprises, and consumers objectively have no opportunity to dispose of or use this data.
Precisely because of the techno-economic characteristics of data and data factors, when combined with other factors of production and applied to various aspects of social production and life, they produce multiplier effects, innovation effects, and spillover effects. Consequently, they become increasingly important in economic life and play an irreplaceable role in the generation of new quality productive forces.
II. The Micro-Generation of Data Factors and New Quality Productive Forces
General Secretary Xi Jinping pointed out, "The vast ocean of data is like the oil resources of industrial society; it contains huge productive forces and business opportunities. Whoever masters big data technology will master the resources and the initiative for development." Data factors play a key role in the generation of new quality productive forces. Exploring the internal mechanism by which data factors drive the generation of new quality productive forces is the prerequisite for gaining insight into the developmental logic and grasping the key drivers of the transformation of productive forces.
Due to their own techno-economic characteristics and multiple effects, data factors have developed into a new type of production factor, performing digital transformation and combinatorial upgrades on traditional production factors and penetrating every link of social reproduction, thereby powerfully promoting the formation of new quality productive forces. The internal logic of data factors driving the realization of new quality productive forces is shown in Figure 1.
(i) The integrated development of data factors with other production factors spawns new quality productive forces
After data became an independent factor of production, it developed close and complex connections with other factors. In the specific production process, the types, proportional relationships, and methods of combining production factors all affect production efficiency. Data factors have brought profound impacts and widespread shocks to different production factors. By integrating with other factors, optimizing factor combinations, and improving factor quality, data factors gradually reveal their own value.
The first is the integration of data factors with the labor factor. First, data factors have improved the quality of laborers. On one hand, the use of data factors itself requires laborers to master more knowledge and skills; on the other hand, in this interactive process, the innovative qualities of laborers are also enhanced. Data factors can enable laborers to cultivate data-driven innovative thinking; by analyzing large amounts of data, laborers discover potential problems and opportunities and propose innovative solutions, thereby improving labor productivity. Second, more precise labor process control has emerged. In current economic life, sensors are ubiquitous in production and daily life, which means that enterprises' monitoring and control of the labor process are more comprehensive, accurate, and real-time, thereby enhancing labor productivity.
Next is the integration of data elements with technical elements. A symbiotic relationship exists between the two, where they mutually reinforce one another. Data elements serve as a critical foundation for technological innovation. Massive volumes of data provide training samples and verification data for the development of technologies such as Artificial Intelligence (AI) and machine learning, driving continuous technical progress. For example, deep learning algorithms require large-scale data to train models to improve accuracy and generalization capabilities. Conversely, technical elements provide the means and tools for the development and application of data elements. Advanced information technologies—such as big data processing, cloud computing, and blockchain—provide technical support for data storage, processing, analysis, and sharing, thereby enhancing the value and utilization efficiency of data.
Finally, there is the integration of data elements with management elements. Data elements contain a wealth of information and knowledge. The integration of rich data resources and advanced data analysis capabilities with management elements can enhance the scientific rigor of managerial decision-making. This facilitates efficient communication and smooth operation within organizations, allowing enterprises to better meet customer needs, improve the quality of products and services, and innovate business models, thereby distinguishing themselves in market competition.
Furthermore, as an independent factor of production, the data element also directly participates in various stages of the economic cycle as a new quality object of labor.
(2) Data elements permeate all stages of the economic cycle to promote the formation of new quality productive forces
The application of data elements acts upon all aspects of social production, triggering comprehensive transformations in the economic sphere and subsequently promoting the realization of new quality productive forces. Data elements play a role in the production, circulation, distribution, and consumption stages of the economic cycle, smoothing connections, facilitating circulation, and driving the formation of new quality productive forces.
First is the permeation of data elements in the production stage. In this stage, this permeation is primarily manifested in three areas. First, data elements integrate with traditional productive forces, promoting the "intelligentization" and "greening" of traditional factors, spawning new productive factors, and pushing the entire production system toward digitalization and networking. This drives the transformation of traditional production models toward high quality, high efficiency, and high technology. While improving traditional productive factors, data also acts as a "lubricant" to coordinate the various links of production, smoothing upstream and downstream relationships and achieving real-time intelligent management of the supply chain. Second, data elements participate in the production process as independent factors of production, giving rise to new services and products. Currently, the efficient, centralized management and intelligent utilization of data are at the core of the transformation of productive forces. Third, as the core link between the digital economy and the real economy, data elements effectively promote the integration of both, fostering healthy economic development.
Second is the permeation of data elements in the circulation stage. In this stage, the use of data elements can effectively reduce logistics costs and optimize the supply chain. As data elements flow through every link of the industrial chain, they can effectively break "data silos" [4] under traditional circulation models, efficiently connecting people, goods, and events across the upstream and downstream. This achieves timely and effective communication and solves long-standing issues such as information blockages and information asymmetry, thereby improving circulation efficiency and reducing costs. The use of data elements shifts the supply chain system toward digital development, effectively coordinating various subjects within the chain and optimizing both individual supply chains and supply chain clusters. Taking the logistics sector as an example, the utilization of data elements enables enterprises to refine logistics routes and select the most appropriate service providers, thus increasing efficiency and reducing costs. Driven by data elements, new business forms and models like cross-border e-commerce and "smart logistics" have emerged, injecting new momentum into the exertion of new quality productive forces.
Third is the permeation of data in the distribution stage. The application of data elements helps optimize resource allocation. On one hand, data elements optimize the distribution of the means of production, rationally mobilizing various factors to maximize effects; on the other hand, the use of data elements can ensure that the fruits of social development benefit all people more broadly, achieving value sharing. This is because the full utilization of data elements can effectively resolve the digital divide reflecting our current era, narrowing the digital dividend gap between different regions and groups. The reproducibility of data elements allows for the balancing of equity while improving the efficiency of distribution activities. The flow and empowerment of data across all links of economic activity can drive participating subjects to form a "community of interests," driving the multi-chain, cross-chain, and intra-chain integration of the industrial, value, and innovation chains, resulting in a broader and deeper "value community."
Finally, there is the permeation of data in the consumption stage. Driven by the development of productive forces, human demands are increasingly moving toward diversification, differentiation, and personalization. The application of digital technology can better satisfy these diverse needs. The collection and analysis of data regarding consumer behavior and preferences help enterprises provide better services, including precision marketing, personalized recommendations, optimized consumption experiences, improved decision-making and trust, and the expansion of consumption boundaries and potential, as well as improved market efficiency and fairness. Against the backdrop of the rapid development of the internet platform economy, data elements have increasingly become the key factor connecting industry, products, and the consumer end, driving the 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 equilibrium between supply and demand.
(3) Data elements act upon the three elements of the productive forces to drive qualitative change
General Secretary Xi Jinping has pointed out that "data, as a new type of factor of production, has a major impact on the transformation of traditional modes of production." In Marx's view, "the transformation of the mode of production takes the labor power as the starting point in manufacture, and the instruments of labor as the starting point in large-scale industry." [5] The means of production are the technical means and conditions that trigger changes in the mode of production. With the development of science and technology, data elements have become new means of production and new instruments of labor, triggering qualitative changes in the productive forces and leading to transformations in the mode of production. New quality productive forces are modern, nascent productive forces 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 the productive forces have been affected and have undergone qualitative changes.
First, data elements promote the formation of new-type laborers. Laborers are the most active factor in the productive forces; only when combined with laborers can factors of production be transformed into actual productive forces. With the development of the new round of technological revolution, intelligent manufacturing and AI will replace more physical and simple labor, giving rise to new human-machine collaborative models. These new production conditions require laborers to master more skills and knowledge. Data elements permeate all walks of life; because the scale of data is vast and its structure complex, laborers are required to cultivate "data thinking" and enhance their digital literacy to become new-type laborers. The 14th Five-Year Plan for Digital Economy Development states the need to "improve the digital literacy and skills of the entire population," meaning laborers must enhance their labor capabilities to match new modes of production.
Second, data elements give rise to new instruments of labor. The emergence of massive data has made data analysis tools indispensable instruments of labor in production and life. Data analysis tools are not only a means to release the value of data elements but are also essential labor tools for digital production in the big data era. Big data technologies such as AI algorithms, cloud computing, and blockchain can effectively collect, store, process, and analyze massive data; the immense value hidden within also drives the continuous iteration of digital technology. Therefore, technologies related to data collection and processing constitute the new instruments of labor in the digital economy era.
Data elements drive the "digital-intelligent" (数智化) transformation of traditional instruments of labor, releasing their immense value, transforming the forms of the means of production, updating labor tools and their methods of operation, and spawning new quality instruments of labor. On 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—transforming work scenarios into "all-scenario" applications and diversifying transaction models. This helps enterprises understand market demand, optimize production plans, improve efficiency, and facilitate industrial chain synergy, providing a convenient, secure, and efficient foundation of means of production for industrial chain cooperation. On the other hand, through digital technology, AI, and fusion technologies, data elements create network platforms and derive new instruments of labor such as intelligent and automated assembly lines and smart manufacturing equipment. This reduces manual operation, improves production safety, enhances digitalization and intelligence, reduces resource consumption, accelerates the "green" transformation of production, and updates the methods by which labor tools function, thereby generating new quality instruments of labor.
Third, data elements foster new objects of labor. Objects of labor refer to the things upon which laborers act, either directly or through instruments of labor, including natural and man-made objects. Different levels of productive forces have corresponding objects of labor. On one hand, data elements can participate in material production as objects of labor themselves, such as data resources, data services, and data products. On the other hand, the integration of data elements with traditional factors of production changes the status of objects of labor and expands their scope. The widespread use of data can expand the variety and quantity of traditional objects of labor, generating more intelligent objects and increasing the added value of original objects, thereby expanding the fields of production and promoting the progress of productive forces.
III. The Industrial Generation of Data Elements and New Quality Productive Forces
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, particularly by spawning new industries, new models, and new kinetic energy through disruptive and frontier technologies to develop new quality productive forces." The developmental permeation of data elements provides the foundation and carrier for the formation of new quality productive forces. In the process of interaction with these productive forces, data elements give rise to new industries, construct new models, upgrade traditional industries, and optimize production modes.
(1) Spawning new industries and constructing new models
The widespread application of data elements has birthed new industries. Typical representatives include the big data industry, the AI industry, and the blockchain industry. The big data industry mainly involves data collection, storage, analysis, and mining. Big data analysis companies use various tools and algorithms to conduct in-depth mining of massive data to discover latent patterns, trends, and correlations. For instance, through the analysis of consumer behavior data, enterprises can better understand customer needs and optimize products; financial institutions can perform risk assessments and investment decisions through market data analysis. Data mining can also be applied to fields like healthcare, transportation, and energy. The AI industry is heavily dependent on high-quality data supply; data elements provide the training material for machine learning and deep learning. Improvements in image recognition, voice recognition, and natural language processing are inseparable from massive data support. Blockchain technology can ensure the security of data storage and transmission, ensuring authenticity and integrity. Of course, the combination of data elements and digital technologies with different sectors has also spawned industries like "smart agriculture" and "smart healthcare," demonstrating a morphological evolution of "Original Industry + New Technology = New Industry."
Data elements have also spawned new models. First are new production models. One aspect is the transformation from traditional production to intelligent production. Driven by data elements, the status of consumers and producers has changed, shifting from the producer-led model of the past to a consumer-led model, where consumers can influence the production process to a greater extent to achieve personalized customization. Second, the widespread use of sensor equipment allows for the comprehensive collection of data throughout the production process, enabling precise control and mastery of every link, reducing errors, and improving the efficiency of the use of means of production.
Secondly, there are new business models. The development of the digital economy has promoted the integration of online and offline spheres, enabling enterprises to respond rapidly to market demands and technological shifts by launching new services and functions—the platform model being the most typical example. Notable digital business platforms include Taobao, Pinduoduo, Douyin, and Xiaohongshu. This type of business model integrates various offline resources onto online platforms, converging massive numbers of suppliers and demanders. Both parties can interface quickly and accurately to satisfy "short, frequent, and fast" demands. Within this model, data elements make resource sharing more efficient and convenient. Shared mobility models, represented by bike-sharing and car-sharing, utilize Internet of Things (IoT) technology and big data analysis to achieve real-time positioning, reservation, usage, and management of vehicles. Platforms can conduct intelligent dispatching based on user demand and vehicle distribution to improve utilization rates. In the field of shared accommodation, platforms provide personalized recommendations by collecting and analyzing data such as housing information and user reviews. Meanwhile, hosts and renters can conduct secure and convenient transactions through the platform. The sharing economy model makes full use of idle resources, reduces waste, and provides users with more flexible and convenient service options.
Finally, there are new organizational models. Data elements catalyze transformations in corporate organizational structures, shifting them toward platform-based and networked forms. Data elements promote the sharing and transparency of internal information, significantly reducing collaboration costs for employees and breaking down information silos [6] between departments, thereby enabling resource sharing and efficient decision-making. The development of digital technology has made remote collaboration and off-site work a reality, making corporate organizations more flexible and efficient. Furthermore, with the extensive use of digital equipment, enterprises are able to exercise comprehensive supervision over laborers.
(2) Upgrading Traditional Industries and Optimizing Production Models
The integrated development of data elements with other factors of production effectively promotes the optimization and upgrading of traditional industries. On one hand, it facilitates deep connectivity between various sectors, realizing the digital and intelligent transformation of traditional industries, promoting the optimization and reorganization of industrial chains, and pushing forward the transformation of the economic structure. On the other hand, it drives internal innovation within industries, promoting the construction of a more efficient and flexible production system, thereby advancing the development of new quality productive forces. Specific illustrations using manufacturing, agriculture, and commerce follow.
The use of data elements drives the adjustment of traditional manufacturing from past mechanization toward automation and intelligence; this also signifies the digital transformation of enterprises, giving rise to many new products or services. The upgrading of manufacturing toward automation and intelligence implies an increasing degree of data involvement. By installing sensors and other devices on production equipment, manufacturing can easily achieve data acquisition across the entire process, chain, and life cycle of production. Through data mining, analysis, and real-time mastery, enterprises can, first, monitor the production process in real-time, predicting equipment failures to perform maintenance in advance, thereby reducing the risk of production interruptions and ensuring continuity. Second, they can continuously optimize production parameters based on data analysis to improve product quality. For example, automobile manufacturers precisely adjust production parameters by analyzing production line data to ensure stable vehicle quality. Additionally, enterprises can connect production-side data with consumption-side data, allowing traditional firms to better meet the personalized needs of consumers, subsequently reducing inventory pressure and lowering costs.
The application of data elements drives agriculture toward intelligent and precision agriculture. Farmers and agricultural technicians can use technologies such as sensors and satellite remote sensing to collect data on soil, climate, and crop growth. By analyzing this data, farmers can understand farmland conditions precisely, achieving precision fertilization, irrigation, and pest and disease control. The consumer market can utilize data to establish agricultural product traceability systems, recording data throughout the entire process from planting and processing to transportation and sales. By analyzing historical market data and consumption trends, farmers and agricultural enterprises can better predict market demand and arrange planting plans rationally to avoid blind production.
The application of data elements drives transformations in commercial models, turning past offline models into current platform models. Platforms can collect consumer shopping histories, browsing records, and other data to conduct precise user profiling. Based on these profiles, retailers can push personalized product recommendations and promotional information to consumers, enhancing marketing effectiveness.
IV. Practical Paths for Data Elements to Accelerate the Development of New Quality Productive Forces
The Decision of the Central Committee of the Communist Party of China on Further Comprehensively Deepening Reform and Advancing Chinese-path Modernization (hereinafter referred to as the "Decision") [7] points out that education, science and technology, and talent are the foundational and strategic supports for Chinese-path modernization. Only by coordinately advancing the integrated development of education, science and technology, and talent can we better develop the productive forces. Furthermore, it is necessary to improve the mechanisms and systems linking the talent chain, technology chain, and industrial chain, using systemic institutional frameworks to leverage data elements in assisting the development of new quality productive forces.
(1) Actively Cultivating Digital-Intelligent Technical Talent and Optimizing Talent Cultivation Mechanisms
The momentum of economic development stems from innovation, and innovation requires talent. Currently, the problem of insufficient supply of high-quality "digital-intelligent" (数智) [8] talent in China is quite prominent; the scale of talent cannot meet the demands for high-quality digital-intelligent personnel brought about by the rapid iterative updates of digital industries and the digital transformation of traditional industries. In view of this, China must strengthen the cultivation of digital-intelligent technical talent, optimize talent cultivation mechanisms, and guide workers from traditional industries with surplus capacity to transition into digital-intelligent talent. This will not only alleviate employment issues but also provide the necessary human directorship for new quality productive forces.
First, established and sound talent cultivation models must be developed. One priority is for higher education institutions to optimize discipline settings based on the latest trends in scientific development and establish different cultivation models according to the requirements for developing new quality productive forces. On one hand, universities must cultivate sophisticated scientific and technological innovation talent. Through the collaborative model of industry, academia, and research, they should strengthen university-enterprise cooperation, jointly establishing artificial intelligence and big data industrial colleges and various digital technology experimental platforms with high-tech enterprises to enhance the cultivation of high-level talent and the output of innovative results. On the other hand, universities must train application-oriented talent who are proficient in new digital-intelligent technologies, strengthening the cultivation of students' practical abilities and enhancing the role of vocational colleges in training data-related talent.
Second, vocational training and education must be strengthened to improve the quality of talent. Laborers should be provided with timely data-related training content, such as data mining and analysis and knowledge related to artificial intelligence, to improve their data literacy. The continuous emergence of new models and business formats brought by emerging technologies also requires laborers to possess lifelong learning capabilities; relevant institutions should promptly strengthen vocational training according to the development of digital-intelligent technology.
Third, the universal education of digital-intelligent technology must be strengthened to alleviate the digital divide. The rapid development of science and technology is increasingly changing people's modes of production and life, while also quietly triggering a huge chasm between populations of different nationalities and regions. All competition between nations is, in the final analysis, a competition of the quality of their citizens; it is currently highly necessary for China to improve the digital-intelligent literacy of the entire populace. Therefore, enterprises, the government, and society should actively promote universal education in digital-intelligent technology so that the fruits of development benefit a wider range of people.
(2) Constructing and Improving the Market System for Data Elements
The "Decision" proposes the construction of a unified national market [9], the cultivation of a national integrated technology and data market, and the refinement of mechanisms where the market evaluates the contributions of production factors—such as labor, capital, land, knowledge, technology, management, and data—and determines remuneration based on those contributions. A market system is an objective organic system composed of market elements; it is an organic whole consisting of commodity markets (such as consumer goods), factor markets (such as capital, labor, technology, information, and real estate), and specialized trading markets (such as futures, auctions, and property rights) that are interconnected and mutually conditional. Currently, China's data element market is not yet fully open, which may lead to an insufficient supply of data elements and hinder the positive role of data as a new type of production factor. Data is the core of the emergence of new quality productive forces; therefore, China must strengthen data infrastructure construction, promote the opening of public data, cultivate data element entities, and improve the supply mechanism for data resources.
First, improve data infrastructure. The supply and use of data elements require corresponding digital infrastructure and equipment. Therefore, China needs to proceed from the perspective of a unified national market to advance the construction and perfection of digital infrastructure—such as information and communication technology, big data, and the IoT—to provide sufficient technical support and hardware guarantees for the production and supply of data elements. Furthermore, 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. Localities must particularly avoid the blind establishment of cloud computing data centers and prevent the construction of related infrastructure from turning into "land GDP" [10]. Instead, they should coordinate the arrangement of data infrastructure within the framework of a unified national market to build a system of facilities with regional synergy and complete functions.
Second, establish basic standards for the marketization of data elements. Currently, data elements do not yet possess mature market-based standards like traditional factors do. This requires us to accelerate the establishment of fundamental standards for the confirmation of rights (确权), pricing, and asset valuation of data elements, laying the foundation for the smooth circulation of data. One task is to accelerate the establishment of unified data standards, strengthening the formulation and coordination of data standards across domains and departments to promote the standardization and normalization of data formats. Another is to conduct in-depth research to determine the allocation of ownership and trading rules for data elements, which is the basis for confirming data rights. Meanwhile, China should accelerate the introduction of data element pricing models and trading tools that adapt to market demand and establish unified standards for data asset statistics, accounting, and bookkeeping to provide clear basic standards for the marketization of data elements.
Third, accelerate the improvement of the data element trading market system. Although China has made progress in the construction of data trading markets, it has not yet established a multi-layered market system capable of meeting the rapidly growing demand for data element development. Therefore, China should pick up the pace in constructing a data element trading market system with diversified market dimensions. This includes establishing a unified data element trading platform at the national level while setting up regional data trading hubs in eligible areas; promoting mutual reinforcement between enterprise-led over-the-counter (OTC) markets and government-led exchange-based markets; developing healthy competition between primary and secondary data element markets in an orderly manner; and coordinately planning and pilot-testing the integrated development of comprehensive and specialized data element markets.
(3) Accelerating the Construction of a Modern Industrial System
A modernized industrial system is the core objective for cultivating and developing new quality productive forces. We must target the developmental needs of new quality productive forces, remove obstacles to industrial development, and drive the deep transformation and upgrading of traditional industries. We must arrange industrial chains around innovation chains, utilize breakthrough and frontier technologies to give birth to strategic emerging industries and future industries [11], and promote the development of new quality productive forces.
First, we must transform and upgrade traditional industries, driving them toward high-end, intelligent, and green development. We should strive to form a society-wide digital industrial ecosystem as early as possible to provide broad space for the development of new quality productive forces. China should reconstruct the division of labor within industrial chains through the integration of the physical economy and the digital economy, 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.
Second, we must cultivate and strengthen emerging industries, promoting the deep integration of digital technology and the physical economy. China should implement the "Data Element ×" (数据要素×) [12] action plan to enhance the capabilities of the physical economy, continuously improving the scientific, precise, and systemic nature of services for the physical economy. This will provide powerful guidance and technical support for the modernization of the physical economy, forming a modernized industrial system dominated by new industries and facilitating the development and prosperity of new quality productive forces.
Finally, we must accelerate the optimization of the path for converting cutting-edge technology into practical applications and strengthen the forward-looking strategic layout of future industries. One aspect is to strengthen collaboration between scientific-technological and industrial departments, utilizing frontier technological capabilities to meet major needs and drive scenario-based applications, creating a coherent "research–verification–incubation–application–promotion" innovation and conversion system. Another is to forecast and evaluate the trends of global technological frontiers and future industries. Combining China's resource conditions, industrial base, and economic development goals, we should plan a roadmap for future industries, focusing on directions such as future manufacturing, future energy, future space, and future materials. We should vigorously develop digital frontier industries such as quantum computing and brain-computer interfaces to ensure China seizes the initiative in future development and establishes a complete set of supporting policies for future industrial growth.
(The first author is a doctoral student at the School of Marxism, University of Chinese Academy of Social Sciences; the second author is an associate professor at the School of Marxism, Shanghai Maritime University.)