An evidence and gap map of the environmental Kuznets curve and the forest transition hypothesis for estimating forest area development

Abstract

Abstract Understanding forest area development as a function of socio-economic dynamics is of crucial importance for halting deforestation and shaping future options for action. Here, multivariable statistical models can be promising facilitators of knowledge generation. With the aim of enabling the further development of statistical analysis, this study describes the state of research for two prominent concepts used to analyze forest development in terms of socio-economic development: The forest transition hypothesis (FTH) and the environmental Kuznets curve for deforestation (EKCd). In order to learn from the wide variety of studies that differ in their model specification, region, variable selection, and statistical methods used, this study presents a systematic overview of the existing literature. This study is designed as an evidence and gap map (EGM)—a method of systematic literature research according to the RepOrting standards for systematic evidence synthesis process. A search for relevant literature was carried out in two data repositories. A thorough screening of the literature results yielded 46 articles with a total of 141 different model studies for evaluation. Our results reveal that (a) the evidence base for the EKCd is more comprehensive than for FTH, (b) the majority of eligible studies focus on the analysis of data from developing countries, and (b) a statistical analysis of the EKCd and FTH concepts does not provide a unified picture, but rather a variety of interpretations and specifications. Based on the results of this EGM, we can conclude that further investigations in the form of additional clustering and a deeper analysis of the different approaches used to interpret the two concepts is needed. Future studies should extend the study design by determining the relevant variables for future forest agenda setting and the policies that could be effective in halting deforestation by targeting certain baseline variables.