China’s rapid urbanization and evolving agricultural practices have driven significant changes in cultivated land utilization, characterized by non-agriculturalization (NA) and non-grain utilization (NGU) transformation. Understanding the spatial patterns and driving mechanisms of these transformations is critical for formulating effective cultivated land management and protection policies. Previous studies have treated the non-agriculturalization (NA) and non-grain utilization (NGU) of cultivated land as distinct phenomena with no correlation. Therefore, this study constructs a theoretical framework to explore the correlation between NA and NGU and examines their interaction patterns using Ezhou City in China as a case study. Spatial econometric models and multinomial logistic regression analyses reveal distinct trade-offs and synergies between NA and NGU, which are shaped by locational, socioeconomic, natural, and policy factors. Urban areas exhibit higher NA rates due to economic development, while rural areas favor NGU for improved land use efficiency and profitability. Suburban zones demonstrate a coordinated transformation, where both processes coexist synergistically. The findings, which are also verified by another two case study areas, highlight the existence of spatial correlations between NA and NGU transformations of cultivated land. They also underscore the necessity for region-specific policies to balance food security with economic growth and dietary transformation. This study helps to elucidate the complex mechanisms underlying different types of cultivated land use transitions and offers new perspectives for the formulation of cultivated land use and protection policies for global cities.

This study investigates whether the intensity of human activities conducted by urban populations and carried by urban land follows a wave-shaped diffusion rule using a harmonized DMSP-like NTL dataset during 1992–2018 in 234 cities of China. The results show that variations in the intensity of human activities are diffused in a wave-shaped manner from the urban center to the periphery in cities of different sizes and structures. The results demonstrate that variations in the intensity of human activity also exhibit a wave-shaped diffusion pattern, which is best modeled by a Gaussian function with an average R² of 0.79 and standard deviation of 0.36 across all fitted functions. The outward movement of these waves in monocentric cities with an urban population <8 million occurred at a pace of ~0.5–1.0 km per year, reaching an average distance of ~18 km from the urban centers. While the pace decreased to ~0.2–0.6 km per year in larger or polycentric cities, the average distance of the waves from the urban centers increased to ~22–25 km in these larger cities. In addition, a process-pattern link between the distance-decayed rule and the wave-shaped rule of human activity dynamics was established. Moreover, a spatiotemporal Gaussian function was further discussed to enable modelers to forecast future variations in the intensity of human activities. The disclosed wave-shape rule and model can benefit the simulation of urban dynamics if integrated with other simulation technologies, such as agent-based models and cellular automata.

Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in 2020 by comparing ten land cover datasets of varying resolutions from 500 to 10 m, using the equivalent factor method. Significant disagreements in ESV estimates are identified, revealing spatial heterogeneity and large inconsistencies among estimates from different datasets, even with high spatial resolution (10 m). Across all counties, the typical discrepancy in ESV estimates between any two datasets reaches 3503 CNY/ha, and the ESV estimates for each county show an average coefficient of variation (CV) of 0.186 across the ten datasets, indicating considerable inconsistency attributable to dataset selection. The results highlight that ESV evaluations based on the CLCD, Globeland30, and GLC-FCS30 datasets demonstrate higher consistency and reliability, making them suitable for regional ecosystem service valuation. Both the landscape configurations and the area disparities of different land types have significant impacts on ESV disagreement. This study provides valuable insights into the applicability of different datasets for ESV evaluation, thereby enhancing the reliability of ESV assessments and supporting informed decision making in ecosystem management.