Revealing the Driving Factors of Land Disputes in China: New Insights from Machine Learning and Interpretable Methods

Jiayin Li, Bin Tong (), Shukui Tan, Shangjun Zou and Junwen Zhang
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Jiayin Li: School of Marxism, Hubei University, Wuhan 430062, China
Bin Tong: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
Shukui Tan: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
Shangjun Zou: College of Tourism Management, Wuhan Business University, Wuhan 430056, China
Junwen Zhang: College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China

Land, 2025, vol. 14, issue 9, 1-23

Abstract: Land disputes pose a severe challenge for many developing countries worldwide. Understanding the driving factors of land disputes is crucial for social stability and sustainable development. China is one of the countries with the most severe situations of land disputes. This paper evaluates the land dispute intensity ( LDI ) across 30 provinces in China from 2011 to 2022. Using the GBDT model and interpretability methods, this study reexamines the importance of multidimensional variables in LDI , while also uncovering their nonlinear and interaction effects. The results show that LDI across 30 provinces generally and continuously increased after 2014, with this trend being notably curbed after 2019. In terms of the driving factors of LDI , the number of specialized farmers’ cooperatives plays the most critical role (mean |SHAP value| = 0.4). Variables such as share of primary industry, coverage of land transfer service centers, and agricultural product price index also exert a stronger influence on LDI . Clear nonlinear effects on LDI are observed for the agricultural product price index, the number of specialized farmers’ cooperatives, and the mediation rate of non-litigation disputes. In terms of interaction effects, when the mediation rate of non-litigation disputes is lower than 0.9, increases in the number of specialized farmers’ cooperatives and coverage of land transfer service centers tend to enhance their influence on raising LDI . When the ratio of cultivated land transfer is below 0.3, an increase in coverage of land transfer service centers is associated with a stronger effect in reducing LDI . Overall, this study uses the GBDT model, Shapley additive explanation (SHAP), and partial dependency plots (PDPs) to identify the main driving factors of land disputes. This paper can provide valuable references for developing countries and regions worldwide in addressing land disputes and conflicts.

Keywords: land dispute intensity; machine learning; nonlinear effect; interaction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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