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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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Forest carbon prospecting for climate change mitigation: Version 1.0

Authors: Koh, Lian Pin; Zeng, Yiwen; Sarira, Tasya Vadya; Siman, Kelly;

Forest carbon prospecting for climate change mitigation: Version 1.0

Abstract

This data package includes the two 1-km resolution global maps (.tif) of tropical forests between ~23.44°N and 23.44°S produced from the study: 1) investible forest carbon (in tCO2e ha-1y-1) and 2) forest carbon return-on-investment (Net Present Value in USD ha-1y-1) over a 30-year timeframe. It also includes the R script to reproduce these layers and their uncertainties. Investible Forest Carbon: The investible forest carbon map was produced based on the total volume of CO2e associated with the three main carbon pools in the tropics, namely aboveground carbon, belowground carbon and soil organic carbon. This is followed by the application of key Verified Carbon Standard (VCS) criteria including additionality, to determine the magnitude and areas of investible forest carbon across the tropics. Aboveground carbon. A stoichiometric factor of 0.475 was applied to recent spatial data on aboveground carbon biomass to obtain carbon stock based on established carbon accounting methodologies. An uncertainty analyses was also performed to account for potential variability in stoichiometric factor. Subsequently, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO2e associated with this carbon pool. Belowground carbon. Belowground carbon biomass was firstly derived by applying two allometric equations relating to root to shoot biomass to the most recent spatial dataset on aboveground carbon biomass following established carbon accounting methodologies. The two equations are: Belowground biomass = 0.489×aboveground biomass^0.89; and Belowground biomass = 0.26×aboveground biomass A stoichiometric factor of 0.475 was subsequently applied to the estimated belowground carbon biomass to obtain the carbon stock. An uncertainty analyses was then performed to determine the mean, minimum and maximum values for belowground carbon. Following that, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO2e associated with this carbon pool. Soil Organic Carbon. Organic carbon density of the topsoil layer (0-30 cm) was obtained from the European Soil Data Centre as it represented the best data available for soil organic carbon. A conversion factor of 3.67 was subsequently applied to derive the volume of CO2e associated with this carbon pool. Applying VCS criteria. The criterion of additionality is a pre-condition for carbon credits to be certified under the VCS. This implies that only the volume of forest carbon that are under imminent threat of decline or loss if left unprotected by a conservation intervention can be certified under the VCS. The volume of forest carbon under threat of loss was based on the best available data on predicted deforestation rates across the tropics (through to the year 2029), and annualized over predicted 15-year period. The estimated annual deforestation rates was then applied to the total volume of CO2e associated with tropical forests as estimated above, deriving the volume of CO2e that would be certifiable and thus investible under the VCS. In addition, a conservative 10-year decay estimate was assumed for the estimate of the belowground carbon pool, and lands that will likely not be certifiable for other reasons, including recently deforested areas (i.e. for the period of 2010-2017), a well as human settlements, were excluded. Lastly, the VCS requirement to set aside buffer credits of 20% was accounted for to consider the risk of non-permanence associated with Agriculture, Forestry and Other Land Use (AFOLU) projects. Return-on-Investment. From the investible forest carbon map, the relative profitability of these areas was then modelled to produce a global forest carbon return-on-investment map based on their NPV. The NPV of returns were based on several simplifying assumptions following established values from previous studies. Cost of project establishment. The cost of project establishment was estimated to be at $25 ha-1. This was based on a range of costs that are key to the development of a project, including but not limited to project design, governance and planning, enforcement, zonation, land tenure and acquisition, surveying and research. Cost for annual maintenance. The cost for annual maintenance was estimated to be $10 ha-1, which included aspects such as in education and communication, monitoring, sustainable livelihoods, marketing, finance and administration. Carbon price. A constant carbon price of $5.8 t-1CO­2e for the first five years was applied. This price was based on an average price of carbon for avoided deforestation projects reported recently by Forest Trends’ Ecosystem Marketplace (i.e. for the period 2006 – 2018). Subsequently, a 5% price appreciation was applied annually over a project timeframe of 30 years. Discount rate. We calculated NPV of annual and accumulated profits over 30 years based on a 10% risk-adjusted discount rate. Further details for these datasets and their uncertainties are presented in Koh et. al. For questions or issues on the spatial data layers, please contact Yiwen Zeng (zengyiwen@nus.edu.sg).

Related Organizations
Keywords

Carbon stocks, Climate change mitigation, Carbon finance

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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