Overview of methods
First, we modeled and mapped investible forest carbon, and its climate mitigation potential across the tropics at 1-km resolution. Second, we compared our estimates of investible forest carbon with actual volumes of VCUs reported by 25 real-world VCS forest protection projects. Third, we modeled the relative profitability of investible forest carbon sites to produce a global forest carbon return-on-investment map based on their NPV.
All calculations were based on data dated between 2012 and 2017 and at a resolution of 0.00833 degrees (~1 km). To ensure data standardization, we resampled (bilinear) finer-scaled data where necessary, for example, for data sourced from the European Space Agency – Climate Change Initiative -Land Cover<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 27" title="ESA-CCI. ESA Climate Change Initiative – Land Cover led by UC Louvain (2017).” href=”https://www.nature.com/articles/s41467-021-21560-2#ref-CR27″ id=”ref-link-section-d26938e3126″>27. We only considered tropical forests between ~23.44°N and 23.44°S, and excluded all land cover types that would preclude forests, for example, savannas, bare ground, water, agriculture and urban areas<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 27" title="ESA-CCI. ESA Climate Change Initiative – Land Cover led by UC Louvain (2017).” href=”https://www.nature.com/articles/s41467-021-21560-2#ref-CR27″ id=”ref-link-section-d26938e3130″>27.
Investible forest carbon
We first estimated the total volume of CO2 associated with three carbon pools in tropical forests: aboveground carbon, belowground carbon, and soil organic carbon. Next, we applied key VCS criteria, including additionality, to model and map investible forest carbon across the tropics.
Mapping total volume of CO2 associated with tropical forests
Aboveground carbon
We applied a stoichiometric factor of 0.475 to recent spatial data on aboveground carbon biomass12 (i.e., for period 2012–2016), to convert it from biomass to carbon stock values, based on established carbon accounting methodology3,28,29. We performed an uncertainty analysis to account for potential variability in this stoichiometric factor (see ‘Uncertainty analysis’ section below). We applied a conversion factor of 3.67 to derive the volume of CO2 associated with this carbon pool3.
Belowground carbon
We derived belowground carbon biomass by applying two different allometric equations relating root to shoot biomass30 to the most recent spatial dataset on aboveground carbon biomass12, following established carbon accounting methodology3,28,29. The two equations are: belowground biomass = 0.489 × aboveground biomass^0.89; and belowground biomass = 0.26 × aboveground biomass. We then applied a stoichiometric factor of 0.475 to the estimated belowground carbon biomass to convert it from biomass to carbon stock values. Next, we calculated the mean, minimum and maximum values for belowground carbon based on an uncertainty analysis (see ‘Uncertainty analysis’ section below). We applied a conversion factor of 3.67 to derive the volume of CO2 associated with this carbon pool3.
Soil organic carbon
We also considered soil carbon due to its potentially significant contributions to carbon storage31 and despite potential uncertainties and variability surrounding its measurements32. Specifically, we utilized the organic carbon density of the topsoil layer (0–30 cm) obtained from the European Soil Data Centre33 as it represented the best data available of soil organic carbon. We applied a conversion factor of 3.67 to derive the volume of CO2 associated with this carbon pool3.
Applying VCS criteria to map investible forest carbon
The criterion of additionality is a pre-condition for certifying all carbon credits 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. We derived the volume of forest carbon under threat of loss based on best available proxy data on projected future deforestation rates across the tropics13 (through to the year 2029), and annualized over the prediction period (15 years). We applied this estimated annual deforestation rate to the total volume of CO2 associated with tropical forests as estimated above, to derive the volume of CO2 that would be certifiable and therefore investible under the VCS.
We also assumed a conservative 10-year decay estimate for the belowground carbon pool9.
Additionally, we excluded lands that will likely not be certifiable for other reasons9, including recently deforested areas34 (i.e., for the period 2010–2017), as well as human settlements located within these forests35.
Lastly, we accounted for the VCS requirement to set aside buffer credits of 20% to account for the risk of non-permanence associated with Agriculture, Forestry and Other Land Use projects (AFOLU)9.
Comparing estimates of investible forest carbon to verified carbon units
We compared our estimates of investible forest carbon with actual volumes of VCUs reported by real-world VCS forest protection projects (https://verra.org/).
We identified a set of 25 VCS forest protection projects from across 16 countries that met the following criteria: ii) includes spatial data on project boundary in their project documentation; ii) the project extent is located entirely within the tropics; and 3) has been verified (i.e., either “verified, under verification” or “verification approve”) (Table S2).
We extracted the shapefiles (i.e., geometric polygons) of these VCS projects, and overlay them on our map of investible forest carbon to extract the volume of investible forest carbon (CO2) from our analysis that corresponds to each of the 25 VCS forest project.
We then compared our estimates of investible forest carbon to the volume of VCUs issued between 2005 and 2018 for each VCS project. The number of data points reported per year for each project ranged from 1–10, and generated a total of 111 data points for comparison. We then assessed the degree of correlation (i.e., Pearson’s correlation), relative accuracy (i.e., Root Mean Square Error; RMSE), and statistical difference (i.e., paired t-test) between the two datasets.
Estimating return-on-investment
Based on our map of investible forest carbon, we modeled the relative profitability of investible forest carbon sites to produce a global forest carbon return-on-investment map based on their NPV. We calculated NPV of these returns based on several simplifying assumptions following established values from previous studies19.
First, we estimated the cost of project establishment at $25 ha−1. This was based on a wide 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 research19,36,37.
Second, we estimated an annual maintenance cost of $10 ha−1, which included aspects such as education and communication, monitoring, sustainable livelihoods, marketing, finance and administration19,36,37.
Third, we assumed a constant carbon price of $5.8 t−1CO2 for the first five years. This price was based on an average price of carbon for avoided deforestation projects recently reported by Forest Trends’ Ecosystem Marketplace6 (i.e., for the period 2006–2018). After the first five years, we assumed a 5% price appreciation for subsequent years over a project timeframe of 30 years19.
Based on these criteria, we calculated NPV of annual and accumulated profits over the 30 years, based on a 10% risk-adjusted discount rate.
Separately, we repeated the analysis using a range of starting carbon prices, including $1, $5, $10, $15, $25, $50, $100 t−1CO2, based on cost effectiveness thresholds from previous studies1. In these analyses, other assumptions remain unchanged, including the project establishment and annual maintenance cost, price appreciation, discount rates and timeframe. Based on these criteria and excluding sites that would be unable to breakeven (i.e., yielding net negative NPVs), we calculated the potential profitable forest areas, as a percentage of the total investible forest areas, associated with these different starting carbon prices.
All values of investible carbon and return-on-investment (based on NPV) were summarized to global, regional, and country level estimates (see Table 1). For countries that extend beyond tropical latitudes, we only analyze and present data for their tropical extents. These values were rounded to the nearest 1000 values.
Uncertainty analyses
Stoichiometric factor
Previous studies utilized a range of stoichiometric factors, typically ranging between 0.45 and 0.503,28,29. We account for this variability by first using a stoichiometric factor of 0.475, which was based on the median value across these reference studies3,28,29. We then repeated the analyses with stoichiometric factors of 0.45 and 0.50 to calculate the respective minimum and maximum values of above and belowground carbon per cell.
Root to shoot biomass allometric equations
Many site-specific factors can influence the ratio of root to shoot biomass, resulting in variability of the best-fit allometric equations30. Here, we account for this variability by utilizing the two allometric equations that best matches global data30. This produced two sets of spatially explicit estimates of belowground biomass, from which we calculated the average, minimum and maximum values per cell.
Aboveground biomass
We incorporated uncertainties, reported at standard deviations, which were inherent to the aboveground biomass dataset12.
Leakage effects
We considered three scenarios of leakage, where the protection of an area of forest results in deforestation beyond its borders to the amounts of 10%, 20%, and 30% of the areas’ carbon volume. This reduces the total investible carbon within each cell, thereby causing a decrease in return-on-investment and the climate mitigation potential within profitable areas to 81.9 ± 51.1, 64.6 ± 40.4 and 48.3 ± 30.3%, or 909.1 ± 567.4, 716.7 ± 448.4, 535.8 ± 336.3 MtCO2 yr−1, respectively (Table S1).
Establishment and maintenance costs
We also considered two scenarios of establishment and maintenance cost, where the overall direct cost of protecting areas from deforestation increases by 50% and 100%. We find that this reduces the climate mitigation potential in profitable areas to 80.4 ± 50.4 and 65.7 ± 41.6% or 892.1 ± 559.2 and 728.8 ± 462.2 MtCO2 yr−1 respectively (Table S1).
Opportunity costs
We also considered the potential for alternative land-use such as agriculture or timber extraction to outcompete the value of protecting forests through carbon financing means. Utilizing agricultural rents (based on 18 crops) and timber value as a proxy for opportunity cost38, we excluded areas where opportunity cost exceeds projected net present values. This results in a large decrease in overall climate mitigation potential, almost comparable to the 30% leakage scenario, to 52.3 ± 33.2% or 580.6 ± 368.6 MtCO2 yr−1 within remaining areas.
All analyses were performed in R version 3.6.039, utilizing the package “raster” for processing and calculations of raster layers40. Map visualizations were formed in QGIS<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 41" title="QGIS Development Team. QGIS Geographic Information System, (2019).” href=”https://www.nature.com/articles/s41467-021-21560-2#ref-CR41″ id=”ref-link-section-d26938e3509″>41.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Source: Ecology - nature.com
