State of research on carbon sequestration in Bangladesh: a comprehensive review

• Main Authors: Sahadeb Chandra Majumder, Kamrul Islam, Mohammad Mosharraf Hossain
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-01-01
• Series: Geology, Ecology, and Landscapes
• Subjects: Carbon stock estimation; allometric equations; CDM; REDD+; carbon sequestration
• Online Access: http://dx.doi.org/10.1080/24749508.2018.1481656

A deep interest is evident in carbon sequestration modeling in Bangladesh from the development of several allometric equations to estimate carbon sequestration by plants. It is linked to the evolving carbon offsetting approaches, for example, Clean Development Mechanism (CDM) and the REDD+, which require certifiable estimate of carbon captured by trees and forests. This review compiled a snapshot of state of the art in carbon modeling in Bangladesh. More than half of the published research focused on the development of allometric equations and forest carbon estimation. The comparison among available studies was challenging due to the use of different terminologies and assumptions and arbitrary combinations of parameters including age, topography, season, slope, crown diameter, etc. The spatial distribution of reports indicated narrow geographical focus outside forests in Chittagong and Sundarbans. Surprisingly, no attempts were evident to explore carbon stocks at the Chittagong Hill Tracts (CHTs) where majority of pristine forest areas of the country occurs. Bangladesh is likely to reforest the vast deforested areas in CHTs under CDM and REDD+ projects which requires extensive carbon modeling. Majority of the reports used conversion factor to calculate soil carbon instead of analytical estimation which might cause inaccurate estimation of soil carbon. Blue carbon assessment and policy implication of carbon studies are two areas where insufficient attention is evident. Bangladesh apparently needs to conduct wide-scale carbon modeling through the integration of GIS, remote sensing, etc to increase precision and accuracy of carbon stock assessments. .