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Description

This study investigates the coordinated development of agricultural digitalization and agricultural new quality productive forces across China, hypothesizing that the interaction between these two dimensions drives regional agricultural modernization. Panel data covering 31 provinces from 2012 to 2023 were collected from official statistical sources, including provincial yearbooks, national agricultural reports, and public databases. The data encompass indicators of digital infrastructure, digital development environment, green development, human capital, new-quality labor, and new-quality objects of labor. To analyze the relationship between agricultural digitalization and new quality productive forces, an improved coupling coordination degree model was applied, allowing for the assessment of both overall coordination and subsystem interactions. The data show that both agricultural digitalization and new quality productive forces have steadily improved over the study period, although overall levels remain relatively low, with new quality productive forces generally leading digitalization. The coupling relationship between the two systems has evolved from disorder to marginal coordination, exhibiting a spatial pattern of “eastern > western” and “coastal > inland.” Regional differences indicate that digitalization has a stronger driving role in eastern China, whereas new quality productive forces dominate in central, western, and northeastern regions. The dataset includes detailed measures of the digital development environment, digital infrastructure, and labor-related quality measures, which have the strongest positive impact on coordination, while green development and human capital show weaker synergies. This data can be used by researchers to assess regional agricultural development, explore the mechanisms driving the interaction between digitalization and productive forces, and evaluate policy interventions aimed at promoting sustainable agricultural modernization. Clear documentation of the variables and their sources allows for replication, comparative studies, and the extension of the analysis to other regions or time periods.

Steps to reproduce

The data used in this study were obtained from the China National Knowledge Infrastructure (CNKI), the China Socio-Economic Big Data Research Platform, and official statistical yearbooks. To analyze the coordinated development of agricultural digitalization and new quality productive forces, an improved coupling coordination degree model was applied. Indicators for both systems were constructed based on the collected data, and the model was used to calculate coupling and coordination degrees at both the overall and subsystem levels. This approach allows other researchers to reproduce the calculations, provided the same indicators and data sources are used. All computations were conducted using standard statistical software following the model workflow described in the study.

Institutions

• Jiangsu University of Science and Technology

  Jiangsu, Zhenjiang

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