Understanding industrial land allocation in rural regions of China: spatiotemporal patterns and driving mechanisms

Industrial land allocation in rural regions (RILA) is crucial for promoting rural economic development and advancing comprehensive rural revitalization. However, existing research lacks a comprehensive understanding of the evolutionary characteristics of RILA, much less its driving mechanisms. Therefore, this study innovatively evaluated and identified RILA on a large scale from the perspectives of scale, quantity, and type, constructing a “four-force” driving mechanism framework for RILA in China based on natural conditions, economic development, infrastructure, and the social environment. Then, we integrated the Random Forest regression model and the Multi-Scale Geographically Weighted Regression model to systematically analyze the spatiotemporal patterns and driving mechanisms of RILA in China. The results indicated that from 2007 to 2022, the mean value of the RILA scale (RS) fluctuated and increased from 150.49 to 272.58 ha, while the quantity of RILA (RQ) gradually rose from 51.21 to 81.02 parcels. Different types of RILA also manifested a yearly growth trend. Spatially, RS gradually shifted from the distribution east of the Hu Huanyong Line to the inland northwest. However, RQ primarily concentrated in the regions east of Hu's Line, particularly in the southeastern coastal regions. Notably, the spatial distribution characteristics varied across RILA types. Changes in the scale, quantity, and types of RILA were influenced by the four directional forces of natural conditions, economic development, infrastructure, and the social environment, respectively. The critical factors influencing RILA were power infrastructure (PI), urbanization (UR), regional land prices (LP), policy support (PS), water resources (WR), elevation (EL), and road density (RD), with PI having the highest explanatory power. PI, PS, and WR had positive impacts on RILA, while UR, LP, and EL exerted negative influences, and RD exhibited an inverted U-shaped trend. The impact of these key drivers on the scale, quantity, and types of RILA displayed significant spatial non-stationarity and certain gradient effects. This study could provide a reference for the sustainable utilization of rural industrial land.