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.

Continuous climate change has become one of the challenges faced by the world, drawing much attention from governments. The forest industry is the main focus of strategic initiatives to realize a circular and green economy and achieve low emissions. In order to explore the relationship between forest resource endowment and green economic growth, this study represents the first attempt to verify how forest resource endowment affects green economic growth (GEG), with a theoretical analysis and an explanation of the effects of both. The following results were obtained: (1) The GEG of the lagging period can improve the GEG of the current period in all regions, which verifies the sustainability of GEG in China. (2) The regression coefficient of forest resource endowment is a negative primary term (except for the eastern region) and a positive quadratic term, indicating that there is a U-shaped nonlinear relationship between forest resource endowment and GEG in the national, central, and western regions. (3) Forest resource endowments inhibit green economic growth by crowding out human capital from high-tech industries and through such effects as “Dutch disease”. The elasticity coefficient of the eastern region is positive but insignificant, while the forest resource inhibition effect is larger in the central and western regions, but the elasticity coefficient is significantly lower for the central region than the western region. (4) Human capital significantly contributes to GEG in the east and has a negative effect on GEG in the nation and the central and western regions. A possible reason for this is that there are more highly qualified people in the east than in the central and western regions; environmental regulation inhibits GEG in the nation and the east and has a negative but insignificant effect on GEG in the central and western regions, whereas green finance can promote GEG in all regions, and its promotion effect is highest in the east and lowest in the west, where it does not pass the significance test.

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.