Exploring Forest Sector Research Subjects and Trends from 2000 to 2019 Using Topic Modeling

Abstract

Abstract Purpose of Review This review aims to discover the most common topics and trends in international scientific forest sector research between January 2000 and December 2019 and to test the suitability of a quantitative topic-modeling method to extract topics from the data. The results will be helpful for both researchers and policy decision-makers in identifying emerging research topics and possible research gaps. The analysis framework covers the complete forest wood chain (FWC) with PESTE factors. PESTE is applied to analyze political, economic, social, technological, and ecological/environmental factors affecting the FWC. Recent findings In the last two decades, forests and the forest sector have been impacted by several global changes, policies, and megatrends. Previous systematic syntheses of forest sector research reveal that economic, policy, and social research have remained underrepresented in the forest sector literature. Research areas related to forest ecology and climate change have been increasing. More recently, growth has also been detected in social aspects especially related to the increasing literature on forest ecosystem services. Results A total of 160 topics were extracted from 14,470 abstracts of 15 leading international peer-reviewed forest science journals. The ecological topics of forest resources and technological topics of industry and products were by far the two largest subject areas. Ecological topics increased, while technological topics slightly decreased, during the period between 2000 and 2019. A clear decline in the share of topics concerning end-product markets was detected. Indeed, changes in end markets drive changes in the entire forest wood chain. To support the goal of a transition from a fossil-based economy to a bioeconomy, it will be important to increase academic research on policy impacts, as well as social and ecological sustainability issues to cover all the stages of the FWC more evenly. The topic-modeling method was a useful tool in data mining, but human intelligence is needed to interpret and classify the topics extracted by this approach.