This paper adopts the super-efficient DEA (data envelopment analysis) model to measure the forestry eco-efficiency (FECO) of 30 Chinese provinces and cities from 2008 to 2021, and then introduces the Tobit model to explore the influencing factors of FECO to better understand the sustainable development level of forestry. It draws the following conclusions: (1) The average value of FECO in China is 0.504, which is still at a low level, and the FECO of each region has significant regional heterogeneity; the provinces with higher FECO are mainly concentrated in the eastern region, while the FECO of the central and western regions is lower; (2) In terms of the main factors affecting FECO in China, the regression coefficients of market-based environmental regulations are significantly positive in the national, eastern and central regions, while they are significantly negative in the western region. The coefficient of impact of scientific research funding investment on forestry industry eco-efficiency is negative and shows a significant promotion effect in the eastern region, but the elasticity coefficient in the central and western regions is negative but not significant. Economic development has a positive but insignificant effect on FECO, with the eastern region showing a positive correlation, while the central and western regions are insignificant. Industrial structure has a significant negative effect on FECO in the national, eastern and central regions, but the effect of industrial structure on FECO in the western region is not significant. The effect of foreign direct investment on FECO was negative for the national, central and western regions, but the central region did not pass the significance test, while the eastern region reflected a significant promotion effect.

Green development is an inevitable choice for sustainable development under the constraints of environmental resources. This paper attempts to explore the connotation of urban land green utilization efficiency (LGUE) and reveal its spatial differentiation characteristics. This study adopts the super-SBM model to measure LGUE from 2009 to 2022 and analyzes the spatiotemporal variation rules. Then, the study reveals the spatial influencing factors of LGUE, drawing the following conclusions: (1) the average efficiency value of LGUE at the national level is still at a low level, but it is on an upward trend. There are significant differences in LGUE among the eastern, central, and western regions, with the highest LGUE in the eastern region and the lowest in the western region. (2) The spatial distribution of LGUE in various cities across the country is not entirely random but shows significant spatial autocorrelation characteristics. The improvement in LGUE in a region can improve the surrounding region’s LGUE. (3) Economic development level promotes the improvement of local city LGUE, but its impact on LGUE of surrounding neighboring cities is not significant; local city industrial structure upgrading can improve LGUE in both local and neighboring cities; foreign investment in local cities can promote LGUE in both local and neighboring cities; the increase in population density will hinder LGUE in local cities but improve surrounding cities LGUE. The intervention degree of local city government will suppress the improvement of LGUE in both local and neighboring cities.