Based on global bilateral six-digit HS code trade data of wood forest products from 2000 to 2022, we constructed a three-dimensional panel dataset combining year, importing country, and exporting country, and used a staggered difference-in-differences (DID) model to systematically examine the impact of regional trade agreements (RTAs) on the export of wood forest products and the mechanisms through which they operate. The results show that the signing of RTAs exerts a significant promoting effect on the export market share, extensive margin, and quantity margin of wood forest products, while exerting a significant negative effect on the price margin. Heterogeneity analysis indicates that the impact of signing RTAs on the ternary margin of wood forest product exports varies significantly with product characteristics, bilateral distance, and the type of trade agreement. Mechanism analysis shows that RTAs produce these effects by reducing bilateral trade costs and strengthening bilateral social ties. Accordingly, this paper proposes policy recommendations to strengthen the RTA network, optimize the bilateral trade environment, and enhance the depth of environmental provisions in RTAs to promote the green and high-quality development of wood forest products.

This study examines the relationship between industrial agglomeration and carbon emissions in China’s forestry industry, using panel data from 30 provincial-level regions between 2009 and 2020. The industrial agglomeration level is measured by the Location Quotient (LQ), which is calculated based on regional employment shares to reflect the concentration of the forest products industry. This study finds that LQ exhibits a multiplicative effect—meaning that its influence on carbon emissions amplifies through interactive mechanisms of scale, technology diffusion, and spatial concentration. Four carbon indicators—carbon emissions from export products, carbon emission intensity, energy intensity, and energy structure cleanliness—are analyzed. Employing a threshold regression model, the study identifies nonlinear effects of agglomeration on carbon outcomes. The estimated threshold value (LQ = 0.7122) divides the process into three stages: (1) an embryonic stage (LQ < 0.7122) with rising emissions and declining efficiency; (2) a growth stage (around LQ ≈ 0.7122) with simultaneous increases in emissions and efficiency; and (3) a mature stage (LQ > 0.7122) where emissions decline as efficiency improves. These results reveal that the environmental effects of forestry industrial agglomeration evolve nonlinearly across development stages.