Function evaluation and simulation optimization of production-living-ecological space in Fuzhou City, China: a multi-source data and multi-model coupling approach

Abstract

Rapid urbanization and socioeconomic growth have unprecedentedly driven spatial restructuring and functional conflicts in territorial space. Fine-scale identification, functional assessment, and multi-scenario simulation of production-living-ecological space (PLES) are essential for territorial spatial governance and regional sustainability. In this study, by integrating remote sensing imagery and point-of-interest data, the spatiotemporal distribution of PLES types was mapped by a hierarchical identification strategy in Fuzhou city of China during 2012–2024. The PLES function was quantitatively evaluated using multi-source data and a comprehensive index system, with its driving mechanism examined via the GeoDetector model. Multi-scenario simulations of PLES future development were conducted by employing a coupled multi-model framework integrating artificial neural network (ANN), cellular automaton (CA), and multi-objective programming (MOP). The results indicated that the ecological-dominated space remained dominant and stable, occupying approximately 80% of the study area, while living space expanded significantly by 180.11 km 2 from 2012 to 2024. The comprehensive function of PLES increased by 5.42%, with living and production functions rising by 67.45% and 30.98%, respectively. The functional evolution of PLES was jointly driven by natural and socioeconomic factors, which interact through nonlinear or two-factor enhancement effects. By 2040, the living priority scenario would achieve maximal functional optimization, enhancing the PLES comprehensive function by 37.51% relative to the 2024 level. This study provides a replicable method for fine-scale PLES research and supports evidence-based spatial layout governance and quality-oriented sustainable development.