Potential Loss of Biomass Carbon

Author(s):  
Arun Jyoti Nath ◽  
Biplab Brahma ◽  
Ashesh Kumar Das
2018 ◽  
Vol 115 ◽  
pp. 88-96 ◽  
Author(s):  
Biplab Brahma ◽  
Arun Jyoti Nath ◽  
Gudeta W. Sileshi ◽  
Ashesh Kumar Das

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.


2002 ◽  
Vol 21 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Jack L. Krogstad ◽  
Mark H. Taylor ◽  
Maribeth J. Stock

This experimental study investigates the efficacy of lawyers' letters in providing auditors with corroborating evidence about litigation contingencies. Fifty second- and third-year law students indicate their willingness to provide auditors with estimates of the likelihoods of unfavorable outcomes and potential damages for two realistic litigation cases. The findings indicate that (1) the potential loss of attorney-client privilege and (2) likelihoods of unfavorable litigation outcomes that approach auditors' lower bound for accrual both may inhibit lawyers' responses to auditors. Overall, these findings raise doubts about the efficacy of this widely utilized auditing procedure and lead to concerns about whether litigation contingencies and corresponding losses may be underreported in financial statements.


Author(s):  
Li Dai ◽  
Yufang Zhang ◽  
Lei Wang ◽  
Shuanli Zheng ◽  
Wenqiang Xu

The natural mountain forests in northwest China are recognized as a substantial carbon pool and play an important role in local fragile ecosystems. This study used inventory data and detailed field measurements covering different forest age groups (young, middle-aged, near-mature, mature, old-growth forest), structure of forest (tree, herb, litter and soil layer) and trees (leaves, branches, trunks and root) to estimate biomass, carbon content ratio, carbon density and carbon storage in Altai forest ecosystems. The results showed that the average biomass of the Altai Mountains forest ecosystems was 126.67 t·hm−2, and the descending order of the value was tree layer (120.84 t·hm−2) > herb layer (4.22 t·hm−2) > litter layer (1.61 t·hm−2). Among the tree parts, trunks, roots, leaves and branches accounted for 50%, 22%, 16% and 12% of the total tree biomass, respectively. The average carbon content ratio was 0.49 (range: 0.41–0.52). The average carbon density of forest ecosystems was 205.72 t·hm−2, and the carbon storage of the forest ecosystems was 131.35 Tg (standard deviation: 31.01) inside study area. Soil had the highest carbon storage (65.98%), followed by tree (32.81%), herb (0.78%) and litter (0.43%) layers. Forest age has significant effect on biomass, carbon content ratio, carbon density and carbon storage. The carbon density of forest ecosystems in study area was spatially distributed higher in the south and lower in north, which is influenced by climate, topography, soil types and dominant tree species.


Nano Select ◽  
2021 ◽  
Author(s):  
Ying Lou ◽  
Xinyu Hao ◽  
Lei Liao ◽  
Kaiyou Zhang ◽  
Shuoping Chen ◽  
...  

Ionics ◽  
2021 ◽  
Vol 27 (3) ◽  
pp. 1025-1039
Author(s):  
Yi Li ◽  
Hechang Shi ◽  
Ce Liang ◽  
Kaifeng Yu

2021 ◽  
Vol 374 ◽  
pp. 137920
Author(s):  
Pengfei Wang ◽  
Zhe Gong ◽  
Ke Ye ◽  
Yinyi Gao ◽  
Kai Zhu ◽  
...  

Author(s):  
Athanase R. Cyamweshi ◽  
Shem Kuyah ◽  
Athanase Mukuralinda ◽  
Catherine W. Muthuri

AbstractAlnus acuminata Kunth. (alnus) is widely used in agroforestry systems across the globe and is believed to provide multiple ecosystem services; however, evidence is lacking in agroforestry literature to support the perceived benefits, particularly in Rwanda. To understand carbon sequestration potential and other benefits of alnus, a household survey, tree inventory and destructive sampling were conducted in north-western Rwanda. Over 75% of the respondents had alnus trees in their farms. The trees provide stakes for climbing beans, firewood and timber. They also improve soil fertility and control soil erosion. Farmers had between 130 and 161 alnus trees per hectare with an average height of 7.7 ± 0.59 m and diameter at breast height of 16.3 ± 1.39 cm. The largest biomass proportion was found in stems (70.5%) while branches and leaves stock about 16.5 and 13% of the total biomass, respectively. At farm level, aboveground biomass of alnus trees was estimated to be 27.2 ± 0.7 Mg ha−1 representing 13.6 Mg of carbon (C) per hectare. Biomass carbon increased with tree size, from 7.1 ± 0.2 Mg C ha−1 in 3 years old trees to 34.4 ± 2.2 Mg C ha−1 in 10 years old trees. The converse was observed with elevation; biomass carbon decreased with increasing elevation from 21.4 ± 1.29 Mg C ha−1 at low (2011–2110 m) to 9.6 ± 0.75 Mg C ha−1 in the high elevation (> 2510 m). In conclusion, alnus agroforestry significantly contributes to carbon sequestration, although the magnitude of these benefits varies with tree age and elevation. Planting alnus trees on farms can meet local needs for stakes for climbing beans, wood and soil fertility improvement, as well as the global need for regulation of climate change.


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