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Humanities and Social Sciences Communications (2026) Cite this article
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Abstract
Civil society organizations (CSOs) play a crucial role in enhancing citizen political participation and are a significant component of civil society. Analyzing the associations between socioeconomic and institutional characteristics and the number of officially registered CSOs in China helps to understand the development status of civil society within this context. This study employs the XGBoost model from machine learning to analyze publicly available data from 31 provinces in China between 2013 and 2022, aiming to explore the predictors associated with variation in the number of officially registered CSOs in China. The study finds that GDP, end-of-year resident population, number of legal entities, end-of-year number of urban basic medical insurance enrollees, number of internet broadband access users, number of students enrolled in higher education institutions, and government spending on social security and employment are the most important predictors of the number of officially registered CSOs in China. These findings provide empirical evidence to inform policy strategies aimed at fostering the sustainable development of CSOs and contribute to understanding the institutional and socioeconomic conditions associated with the distribution of officially registered CSOs in China.
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Xie, Z., Xie, Y. & Duan, Z. Predicting the number of officially registered civil society organizations in China: a machine-learning analysis of panel data, 2013–2022. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-08676-w
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DOI: https://doi.org/10.1057/s41599-026-08676-w