Analysis of Influencing Factors on Farmers’ Willingness to Pay for the Use of Residential Land Based on Supervised Machine Learning Algorithms

Abstract

Aimed at advancing the reform of the Paid Use of Residential Land, this study investigates the willingness to pay among farmers and its underlying factors. Based on a Logistic Regression analysis of a micro-survey of 450 pieces of data from the Sichuan Province in 2023, we evaluated the effects of three factors, namely individual, regional and cultural forces. Further, Random Forest analysis and SHAP value interpretation refined our insights into these effects. Firstly, the research reveals a significant willingness to pay, with 83.6% of sample farmers being ready to participate in the reform, and 53.1% of them preferring online payment (the funds are mostly expected to be used for village infrastructure improvements). Secondly, the study implies that Individual Force is the most impactful factor, followed by regional and cultural forces. Thirdly, the three factors show different effects on farmers’ willingness to pay from different income groups, i.e., villagers with poorer infrastructure and lower clarity of homestead policy systems tend to be against the reform, whereas farmers with strong urban identity and collective pride support it. Based on these findings, efforts should be made to increase the publicity of Paid Use of Residential Land. Moreover, we should clarify the reform policies, accelerate the development of the online payment platform, use the funds for village infrastructure improvements, and advocate for care-based fee measures for disadvantaged groups.

Keywords

• paid use of rural residential
• willingness
• farmers
• distributed cognition
• machine learning