Forest Quality Dynamic Change and Its Driving Factors Accompanied by Forest Transition in China

Abstract

As ecological and environmental issues have received continuous attention, forest transition has gradually become the frontier and a hot issue, which have implications for biodiversity and ecosystem functioning. In this study, the spatial-temporal dynamics and the spatial determinants of forest quality were investigated using spatial econometric regression models at the province level, which contained 31 provinces, autonomous regions, and municipalities in China. The results showed that forest area, forest volume, forest coverage, and forest quality have greatly increased as of 2018, but uneven forest distribution is an important feature of forest adaptation to the environment. The global Moran’s I value was greater than 0.3, and forest quality of the province level had a positive spatial correlation and exhibited obvious spatial clustering characteristics. In particular, the spatial expansion of forest quality had shown an accelerated concentration. The most suitable model for empirical analysis and interpretation was the Spatial Durbin Model (SDM) with fixed effects. The average annual precipitation and the area ratio of the collective forest were positively correlated with forested quality (significance level 1%). Ultimately, this framework could guide future research, describe actual and potential changes in forest quality associated with forest transitions, and promote management plans that incorporate forest area changes.

Keywords

• forest quality
• driving factors
• spatial econometric regression
• spatial autocorrelation
• SDM
• Getis-Ord Gi*