The quality and scale of urbanization development are the two main aspects in China’s current urbanization process. By measuring and analyzing the level differences in these two aspects, the healthy development of China’s urbanization and urban–rural integration will be promoted. Based on the quality of urbanization and the scale of urbanization, this paper constructs an evaluation index system for urbanization coordination level. On this basis, this paper analyzes the spatial correlation, spatial difference, and spatial pattern evolution characteristics of urbanization coordination level in 286 sample cities nationwide from 2005 to 2015. Then, by introducing the spatial econometric regression model, this paper discusses the driving mechanism of the spatial and temporal evolution of urbanization coordination level. The results show that: (1) The level of coordination between urbanization quality and urbanization scale shows a strong spatial correlation in space, which is consistent with the actual development status; (2) the level of urbanization coordination shows a trend of evolution from northeast to southwest in the evolution of spatial pattern, but the extent of change is small; and (3) the spatial and temporal pattern of urbanization coordination level is affected by different driving forces, of which internal source is the primary impact factor, followed by administrative level and investment level. In addition, the level of urbanization coordination has a positive spillover effect on the level of urbanization coordination in adjacent areas.

The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhanced thematic mapper plus (ETM+), Landsat operational land imager (OLI), national forest inventory (NFI)) was used to estimate forest biomass. Particularly, mean absolute error (MAE) and relative error (RE) were used to assess and quantify the uncertainty of each pretreatment, while the coefficient of determination (R²) was employed to evaluate the accuracy of the model. The results obtained show that random forest (RF) and 10-fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments. In this study, there was a considerable uncertainty in biomass estimation using original OLI and ETM+ images from. Uncertainty was lower after data processing, emphasizing the importance of pretreatments for improving accuracy in biomass estimation. Further, the effects of three pretreatments on uncertainty of biomass estimation were objectively quantified. In this study (results of test sample), a 33.70% uncertainty was found in biomass estimation using original images from the OLI, and a 34.28% uncertainty in ETM+. Radiometric calibration slightly increased the uncertainty of biomass estimation (OLI increased by 1.38%, ETM+ increased by 2.08%). Moreover, atmospheric correction (5.56% for OLI, 4.41% for ETM+) and terrain correction (1.00% for OLI, 1.67% for ETM+) significantly reduced uncertainty for OLI and ETM+, respectively. This is an important development in the field of improving the accuracy of biomass estimation by remote sensing. Notably, the three pretreatments presented the same trend in uncertainty during biomass estimation using OLI and ETM+. This may exhibit the same effects in other optical images. This article aims to quantify uncertainty in pretreatment and to analyze the resultant effects to provide a theoretical basis for improving the accuracy of biomass estimation.

Coordinating the interrelationships among economic growth, land resource utilization, and carbon emissions is critical for promoting high-quality economic growth and achieving sustainable urban progress. According to the gravity model and the Tapio decoupling model, this study examined the decoupling relationships of economic growth with land expansion and carbon emissions in Zhejiang Province during the period of 2002–2017. We found that (1) The economic gravity center and the built-up area gravity center generally shifted towards the northwest; however, the carbon emission gravity center initially shifted towards the northwest and then towards the southwest. The spatial coupling between the economic gravity center and the built-up area gravity center exhibited a tendency of ‘first weakening, then strengthening, and last weakening’, whereas the spatial coupling between the economic gravity center and the carbon emission gravity center displayed a tendency of ‘first strengthening and then weakening’. (2) The decoupling of economic growth and land expansion is weak at every stage with effective controls on land expansion. However, in recent years, the phenomenon of ‘expansive negative decoupling’ has become prevalent in counties and cities surrounding the central city. The decoupling of economic growth and carbon emissions steadily increased at each stage, first ‘expansive coupling and expansive negative decoupling’, then ‘weak decoupling’, and finally ‘strong decoupling’. The urban low-carbon transformation effect is remarkable. (3) Zhejiang Province should prioritize addressing the regional imbalance and state instability in the decoupling relationships. It is crucial to comprehensively consider the natural resource endowment, macro-policy factors, and urban development orientation of counties and cities while implementing differentiated planning and control strategies, which will promote regional coordination and comprehensive, high-quality development in all areas.

Using panel data from 11 prefecture-level cities in Zhejiang Province (2014–2023), this study applies the entropy method, spatial autocorrelation analysis, and an obstacle-factor diagnosis model to examine the spatiotemporal evolution, regional disparities, and constraints on urban–rural integration. The results show a steady upward trend in urban–rural integration alongside significant regional disparities. This reveals a complex pattern marked by the coexistence of convergence and divergence. Spatially, a clear “northeast–high, southwest–low” pattern is observed, with local adjustments within a stable framework, reflecting a “stable core and entrenched low-value areas.” Spatial agglomeration is characterized by “dual-core agglomeration with a predominantly non-significant periphery,” dominated by homogeneous “high–high” and “low–low” clusters, with no statistically significant spatial outliers. Obstacle factor diagnosis indicates markedly uneven constraining effects across subsystems, with spatial integration exhibiting the highest degree of obstacles. The composition of primary obstacle factors is highly stable, and obstacle structures differ significantly across city tiers. These findings elucidate the spatiotemporal evolution and core constraints of urban–rural integration in Zhejiang, offering a theoretical and decision-making basis for advancing high-quality urban–rural integration in the region.

Internal mechanisms and laws exist in the evolution of cities, and the power law is widely applied in multiple areas in the real world. It is crucial to optimize the urban-scale systems through explanation studies of the urban-scale distribution pattern from the perspective of regional differences in risk attitudes. Based on computer simulation technologies, this study explores the influence of regional differences in risk attitudes of micro decision-makers on the power law through setting scenarios of same attitudes with quantitative differences and mixed multi-attitudes. In this case, we selected six provinces in China to verify the scale characteristic of the real world. The results show that the settlement scale is heavily influenced by risk attitudes with a larger slope, which are more pronounced in the mixed multi-attitudes scenario. The increase in the mixed-scale benefits less affects the utility of risk attitudes, where the slope value of the aversion attitudes has smaller variation. The averse model has a larger primary ratio than the others. However, the primary ratio does not reveal a significant bias towards large and small in the mixed multi-attitude scenario. In the six provinces, the advantageous areas with higher economic and cultural levels show larger-scale agglomeration characteristics similar to the impact of seeking attitudes. The primacy ratio increases with the variation degree in urban scales, especially in economically disadvantaged areas.