Carbon sequestration in forests has increasingly captured the attention of scientists as a strategy for climate change mitigation and environmental sustainability. In this era of huge carbon emissions, being a low-carbon and cost-effective technology, the economic analysis of forest carbon sequestration holds higher importance for the successful implementation and intended outcomes. This study elucidates a scientometric view of the research structure and thematic evolution of economic studies on forest carbon sequestration based on 1439 articles over the time slice of 2001–2021. The bibliographic data have been retrieved from the Dimensions database, which accommodates a large coverage of research publications and also provides easy access to essential scholarly data and information. Vosviewer and Biblioshiny software tools have opted for visualisation and evaluation purposes of bibliometric data. This study employs various measures of bibliometric analysis like co-authorship, bibliographic coupling, citation and keyword analysis to find out the principal articles, authors, journals, most frequent keywords and highest publishing countries and institutions in this field, and the results show that the number of publications has escalated substantially in the last five years, the most cited article and most productive author are Popp A, 2017 (305 citations) and André P C Faaij (11 documents), respectively, Bradford’s law calculates 21 core journals out of total 503 journals, among which Forest Policy and Economics is at the top, and the most productive country and institution are the USA and University of Florida, respectively. The study also investigates key publishing subject categories, and the number of publications covered under each of the Sustainable Development Goals. The overall outcome of this bibliometric study confers an in-depth understanding of the various dimensions of economic analysis on forest carbon sequestration and its development pattern in the last 21 years and also provides emerging themes for future reference.