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However, spatial heterogeneity of dominant forest and peatland cover types can inhibit accurate C storage estimates.
Differences in peat carbon storage estimates between the MoA and WI maps reflect observed differences in peat volumes.
The broad range in carbon storage estimates are mainly caused by differences in peat volume among studies.
CO2 storage estimates are based on the notion that CO2 and acid gas breakthrough for production wells.
Langner et al. [5] evaluated pan-tropical biomass maps and successfully combined them into a framework for deriving REDD+ Tier 1 carbon storage estimates.
In general, forest carbon storage estimates are typically based on well-established allometric biomass equations, with approximately 50% of above-ground dry biomass related to carbon (C) [18].
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Carbon storage estimated in the bamboo stand of present study offers insights into the opportunity of village bamboos in the rural landscape for carbon storage through carbon sequestration.
Our carbon storage estimate for West Indonesia using the MoA maps is 22.6 GtC, remarkably similar to the 23 GtC reported by Dommain et al. [9].
However, only Wahyunto et al. [5 7] provide a national carbon storage estimate based on maps of peat distribution, thickness, and carbon density for Sumatra, Kalimantan and Papua islands.
By far, the largest current fraction of these costs is capture (including compression and dehydration), commonly estimated at $25 60/tCO22 for power plant applications, followed by CO2 transport and storage, estimated at $0 15/t CO2.
Efstratios Rappos, Stephan and Rudlof have proposed integer programming optimization model of data center for determining the optimal allocation of data components among a network of Cloud data servers in such a way that it minimizes the total costs of additional storage, estimated data retrieval costs and network delay penalties [24].
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