The aim of this paper was to provide a more scientific and effective analysis of the fluctuation pattern of the Chinese potato market by extracting the characteristics of the price fluctuation cycle to effectively grasp the characteristics of price changes in the potato market, thus promoting the stable and healthy development of the Chinese potato industry, and to expand the application scenarios of the EEMD model to provide a reference for the study of price fluctuation patterns in other agricultural markets. This study used an ensemble empirical modal decomposition (EEMD) model to examine time-series data on Chinese wholesale potato market prices from January 2005 to December 2021. The results showed that (1) Chinese wholesale potato market prices are characterized by some rigidity, with sharp changes in growth rates; (2) Chinese wholesale potato market prices are dominated by short- and medium-term fluctuations, and the decomposed components can better reflect the characteristics of the original series fluctuations; (3) Chinese wholesale potato market monthly prices have long- and short-term fluctuations with a 6- and 19-month cycle, and are dominated by short-term high-frequency fluctuations; (4) monthly price fluctuations in the Chinese wholesale potato market are more intense in high-frequency than low-frequency fluctuations, and there is a strong correlation between high- and low-frequency fluctuations in precipitation, temperature and potato prices. Finally, suggestions were made for creating and improving a national potato price information platform and strengthening the information early warning mechanism; improving the potato production interest linkage mechanism and enhancing potato farmers’ ability to cope with market and natural risks; and improving the potato reserve system and potato storage facilities.

As global digital transformation accelerates alongside the “dual carbon” goal, understanding how the digital economy can drive the green transformation of traditional energy systems is critical to overcoming resource and environmental constraints. This study examines the impact of digital economy attention on Green Total Factor Energy Efficiency (GTFEE) using panel data from 275 Chinese prefecture-level cities between 2011 and 2022. Three main findings emerged. First, an increase in attention to the digital economy significantly enhances GTFEE, serving as a key driver of improved energy efficiency. Specifically, a one-standard-deviation increase in attention raises GTFEE by 0.276 standard deviations. Second, this effect operates through two pathways: (1) digital technology advancements, such as higher internet penetration; (2) human capital accumulation, reflected in a greater college student ratio; and (3) green innovation, measured by a rise in green patents. Third, regional heterogeneity is evident, with stronger positive effects in eastern and coastal cities, while high-carbon-intensity regions exhibit a suppressed impact. These results underscore the importance of public engagement in digitalization to optimize energy systems. Policymakers should adopt region-specific strategies, such as boosting digital infrastructure in low-carbon areas and supporting structural reforms in high-carbon regions. This study expands our understanding of the digital economy’s role in enhancing energy efficiency and offers policy guidance for the green energy transition.