The production efficiency of forest ecosystem product values (CEFVs) quantifies the extent to which ecological advantages are converted into economic gains, offering a reference for balancing forest protection and economic development. This study employs an integrated analytical framework to provide policy insights for sustainable development. The Super-SBM model is used to evaluate CEFVs in Shaanxi Province in China from 2010 to 2022, examining its evolution and spatial patterns. Regional disparities are investigated by the Dagum Gini coefficient, while the Spatial Durbin Model (SDM) explores influencing factors and spatial spillover effects. Findings include: (1) The average CEFVs in Shaanxi was 0.734, with a spatial pattern of “Northern Shaanxi > Guanzhong > Southern Shaanxi”. Temporally, CEFVs exhibited a phased fluctuation under the combined influence of policy accumulation and external shocks, peaking in 2021 before declining. Although 57% of counties experienced growth, 62% had not reached the high-efficiency level by 2022, reflecting weak growth quality and resilience. (2) Regional disparities in CEFVs follow a “W-shaped” trend, with the intensity of transvariation as the main source of spatial imbalance. (3) Precipitation, economic development, government intervention, and industrial structure have a positive spillover effect, whereas per capita income and the development level of primary industry exert negative spatial spillover effects. These findings, particularly the identification of divergent spatial spillovers and the trend in regional disparities, offer a scientific basis for formulating differentiated regional policies. The study offers insights for developing countries to balance economic development and environmental protection. • Developed a CEFVs evaluation system incorporating ecological capital and environmental costs. • Revealed spatiotemporal evolution and phased fluctuations of CEFVs across Shaanxi. • Quantified regional disparities, showing cross-regional overlaps drive spatial inequality. • Identified spatial spillovers of natural and economic factors, with positive government and negative income effects.
Huang et al. (Sun,) studied this question.