ISSN:
1573-1596
Keywords:
biomass
;
carbon offsets
;
logging damage
;
Malaysia
;
monitoring program
;
simulation model
;
tropical forest
Source:
Springer Online Journal Archives 1860-2000
Topics:
Energy, Environment Protection, Nuclear Power Engineering
,
Geography
Notes:
Abstract The Reduced-Impact Logging Project, a pilot carbon offset project, was initiated in 1992 when a power company provided funds to a timber concessionaire to implement timber-harvesting guidelines in dipterocarp forest. The rationale for the offset is that when logging damage is reduced, more, carbon is retained in living trees, and, because soil damage is minimized, forest productivity remains high. To estimate the carbon benefit associated with implementation of harvesting guidelines, a monitoring program was developed based on 1) field studies for measuring carbon stocks and flows; 2) a computer model of forest carbon dynamics for simulating various combinations of harvesting intensity and damage; and, 3) a projection model for calculating carbon balance over the project lifespan. Seventy-five percent of the carbon stored in this forest is in biomass, and of this, 59% is in large, trees (≧6- cm, diameter); consequently, reliable estimates of variables related to large trees are critical to the estimate of carbon benefits. Allometric methods for estimating belowground biomass are recommended over pit-sampling methods because of low cost-effectiveness of obtaining precise estimates of woody root biomass. Sensitivity analyses of variables used in the simulation model suggest that maintenance of ecosystem productivity has a large influence on long-term carbon storage in the forest. Projections of differences in carbon stores between the reduced-impact and conventional logging sites rely on assumptions about tree mortality, growth, and recruitment; published, data for comparable sites in Malaysia are probably appropriate for estimating forest recovery from conventional but not reduced-impact logging. Continuing field work is expected to provide the data needed to evaluate assumptions of the models.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02437204
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