Estimating the Economic Value of Carbon Sequestration by Sago Palm (Metroxylon sagu Rottb.) in Thailand
Bibliographic record
Abstract
This study seeks to quantify the carbon sequestration values of sago palm plantations in southern Thailand, an essential yet economically overlooked component in climate change mitigation strategies on agricultural land.This overlooked aspect emphasizes the importance of living trees and soil carbon stocks.Firstly, carbon sequestration in living trees was determined using growth predictions from prior studies, coupled with an allometric equation to estimate both above-ground (AGB) and below-ground biomass (BGB).Secondly, the soil organic carbon (SOC) stock was computed utilizing the core method to calculate bulk density and organic carbon, following Walkley and Black's methodology.A total of ten soil samples were collected from sago palm plantations for this purpose.The carbon sequestration value was derived from the sum of carbon dioxide (CO2) sequestration in living trees and the SOC stock in the soil, subsequently multiplied by the prevailing price of carbon trading in Thailand's official carbon market.Our results corroborate that sago palm plantations can sequester CO2 uninterruptedly for 50 years, eliminating the need for replanting.The carbon sequestration values in living trees were found to increase annually and remain consistent from the tenth year onwards, with an average carbon sequestration value ranging from 1,571-20,046 Baht ha -1 y -1 (42-581 USD) (based on the exchange rate from Baht to USD as of April 10, 2023).Notably, the Thang Poon 1 sago palm plantation demonstrated the highest SOC stock sequestration value of 79,173 Baht ha -1 y -1 , thereby yielding the highest Net Present Value (NPV) compared to other plantations.These findings can aid in shaping policy recommendations for optimal farming management practices to augment both financial returns and ecosystem service benefits.Our study underscores the importance of understanding the benefits of sago palm cultivation compared to monoculture crops, which can inform more sustainable decision-making processes amongst agriculturalists and policymakers, particularly in selecting plant species with the highest potential for climate change mitigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".