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| High-quality Development of Rural Industries in China: Index Construction, Measurement Analysis, and Spatial Evolution |
| Hu Weibin1, Li Xiaxin1, Yang Qiming2, Song Wenhao3, Ruan Jianqing1 |
1.China Academy for Rural Development, Zhejiang University, Hangzhou 310058, China
2.School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China
3.College of Finance, Nanjing Agricultural University, Nanjing 210095, China |
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Abstract The high-quality development of rural industries is crucial for building a strong agricultural nation and achieving common prosperity. Currently, China’s rural industries face multiple challenges, including a homogeneous industrial structure, inefficient organizational systems, and constrained factor mobility. Accurately assessing the current state of high-quality development in rural industries, precisely identifying the key bottlenecks and pathways for upgrading, and subsequently breaking through these development constraints constitute a significant task for advancing the rural revitalization, accelerating the construction of an agricultural powerhouse, and steadily promoting common prosperity in the new era. Existing research has largely been confined to the preliminary stage of designing indicator systems for the high-quality rural industrial development, lacking systematic measurement and spatial evolution analysis. Moreover, constrained by the timeliness and granularity of traditional macro-level statistical data, it has been difficult to accurately reveal the complex connotations, regional heterogeneity, and dynamic evolutionary characteristics of high-quality rural industrial development.To address this gap, this study constructs a comprehensive evaluation indicator system for the high-quality development of rural industries, encompassing nine dimensions. Utilizing meso- and micro-data from the China Academy for Rural Development-Qiyan China Agri-research Database (CCAD) of Zhejiang University and publicly available national data, the study analyzes the level and spatiotemporal evolution characteristics of the high-quality rural industrial development in China. The findings indicate that from 2018 to 2022, the comprehensive index of high-quality rural industrial development in China showed a steady increase, demonstrating strong developmental momentum. However, significant regional disparities persist, with eastern regions maintaining a leading position in rural industrial development, while central and western regions exhibit considerable potential for the improvement. Furthermore, the high-quality development at the county level in China has achieved growth in both quality and scale, yet imbalances remain among the three systems of “industry, production, and management”. Spatial agglomeration and convergence analyses reveal positive spatial clustering characteristics in rural industrial development, but regional disparities in the level of high-quality development are being widened. Additionally, a structural contradiction of “strong divergence but weak catch-up” exists at the county level, indicating that county-level rural industrial development remains constrained by resource endowments, industrial foundations, and the capacity for factor inflows.The innovations of this study are mainly reflected in the following three aspects. First, regarding the theoretical framework, focusing on the industrial, production, and management systems within the agricultural modernization process, this paper innovatively constructs a comprehensive evaluation system encompassing nine dimensions: integration, clustering, specialization, greening, technologization, servitization, branding, organization, and efficiency. This system expands the evaluation dimensions found in existing research and more systematically reflects the quality level of China’s rural industrial development. Second, in terms of data collection, this study overcomes the limitations of traditional macro-statistical data by utilizing meso- and micro-data from the CCAD database to calculate core indicators. This database offers advantages such as a large sample size, rich indicators, and strong timeliness, providing more reliable data support for the precise measurement of high-quality rural industrial development. Third, in measurement and analysis, by applying scientific measurement methods like integrated subjective-objective weighting and spatial econometric models, this study thoroughly reveals the spatial differentiation characteristics and dynamic evolution patterns of rural industrial development in China. This not only provides a data reference for a comprehensive and accurate understanding of the state of high-quality rural industrial development in China but also offers policy insights for supporting the rural industrial development.
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Received: 29 July 2025
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2026, Vol. 56 
