Over the last 30 years, more than 150 different drivers of forest area development have been investigated in peer-reviewed statistical analysis of the environmental Kuznets curve for deforestation (EKCd) and the forest transition hypothesis (FTH). However, there is no synthesis which of these drivers significantly contribute to changes in forest land expansion, like deforestation and forest recovery. To fill this gap, we conducted a systematic review of the scientific literature dealing with statistical analysis of drivers of forest area development under the concepts of EKCd and FTH. We referred to peer-reviewed articles, preselected by the evidence and gap map of Tandetzki et al. (2022). From these selected articles we identified 85 relevant studies and extracted the applied model specifications. We found differences among studies in variable specifications of the dependent variable (expressions of forest area development) and the choice of independent variables (drivers) as well as in the choice of geographical scope and the concept used (EKCd and FTH). For further analysis, we extracted all drivers used to explain forest area development in the different studies and assigned them to 12 thematic categories (e.g., income factors or institutional factors). Our results show that the main underlying drivers of deforestation are related to income, demographics, trade, and institutional factors. The forest transition phenomenon is mainly described by drivers directly influencing forest area (e.g., expansion of agricultural land) and demographic trends. The heterogeneity and universality of the concepts of EKCd and FTH is not clearly evident even when separating different study groups. 
By isolating and discussing individual drivers of forest area development, our findings support future research dedicated to the analysis and projection of global forest area dynamics. 

