Information metrics for data components based on information entropy: data pricing and its application analysis for electric energy statistics

In the era of the digital economy, data has seen its value assessment and pricing remain a research hotspot as a critical factor of production. Currently, research on information value assessment and pricing primarily focuses on data quality, demand, and market policies, with a notable lack of data-driven quantitative studies. This paper explores pricing for data components based on information entropy and proposes a corresponding pricing mechanism. As data's pivotal role in the digital economy becomes increasingly evident, data components, which serve as an “intermediate state" connecting data supply and demand, are of great significance. First, the paper reviews the current research on information value assessment and pricing, finding that this assessment poses challenges to both economics and data science, and elaborates on the importance of establishing an improved pricing structure. Second, it defines corresponding mathematical models for data components and constructs, emphasizing their role in resource development, measurement, and pricing. Furthermore, through a measurement and pricing model based on data components, it investigates the intrinsic information within data components and the differences in their information content compared to traditional information communication methods. Finally, this paper includes an empirical study on the pricing of electricity data to validate the effectiveness of the proposed method. It underscores the significance of data components in achieving the efficient allocation of data elements and outlines future directions in their development. This paper will provide novel insights into achieving the efficient allocation of data elements, offering theoretical support and practical pathways for the development of the digital economy as well as the circulation and sharing of data elements.