Understanding the potential of reforestation as a nature-based climate solution

Full description

The maps in this dataset
were produced from existing datasets to determine the climate mitigation
potential of reforestation in Southeast Asia under various constraints, namely
biophysical, financial, land-use and operational constraints through to the year
2030. This was done for three main forest types: peatswamp, mangrove and terrestrial forests. All
calculations were based on data dated between 2013–2019 and at a resolution of
0.01 degrees (~1 km).

Biophysical
constraints. Biophysical constraints were firstly
determined by identifying degraded forest areas: maximum threshold of 35 MgCha^-1
above-ground carbon for terrestrial forests^1,2, indications of
clearings for peatswamp forests^3,4 and changes in Landsat pixels
over time for mangrove forests⁵ from a pantropical above-ground
carbon layer⁶. We then focus on degraded areas that are low in
biomass due to natural biophysical settings, by masking out ‘forest’ or
‘woodland’ areas that were previously identified as degraded from the Potential
Natural Vegetation (PNV) map⁷. We also masked out current landcover
areas that would preclude reforestation, such as bare ground, industrial land, large
scale agriculture, water and urban areas^8,9. Lastly, we estimated
the climate mitigation potential of each raster cell in the biophysical
constraint layer based on the different forest types and subtypes according to
the PNV map and IPCC classifications^3,5,7,10. This was calculated as
the sum of carbon dioxide likely to be sequestered due to aboveground biomass
growth and avoided business-as-usual (BAU) flux annualised to 2030 (see Table
S3 for details and key references). Climate mitigation potential for areas of
smallholder agriculture – defined as agricultural areas of less than 2 ha –
identified within the layer nevertheless, were taken as forests and its carbon
gain was calculated as the difference between croplands and natural forests¹¹.

Financial
constraints. Financial constraints were determined by
two components: direct cost of reforestation and the opportunity cost based on revenue lost
from agricultural production. Direct costs of reforestation (including
planning, planting and maintenance) across Southeast Asia were specified by
forest type^12,13 and
adjusted to each country based on relative hourly wages¹⁴ and gross
domestic product per capita¹⁵. The opportunity cost based on revenue
lost from agricultural production in Southeast Asia were derived from spatially
explicit crop rents of the 17 most economically important crops based on
production in 2017, considering only crops produced in >1% of the country’s
land area¹⁶. The maximum crop rent for each cell was then
identified, indicating the maximum agriculture revenue lost due to
reforestation. All costs were adjusted to 2018 USD. The low estimate of reforestation costs was based purely on direct
cost. The moderate estimate was based
on both direct and opportunity cost from foregone agricultural rent weighted by
crop development potential index¹⁷. The high estimate was based on the direct and full opportunity cost. We
thus calculated the cost of reforestation per ton of carbon dioxide equivalent
mitigated, utilising the biophysical constraints layer and omitting all areas >
100 USD MgCO₂e^-1 to limit reforestation to cost-effective
areas ^18,19,20.

Land-use
constraints. There are two levels of land-use
constraints: more permissive one,
which only excluded reforestation on smallholder agriculture lands (any raster
cell that possessed agriculture lands ≤ 2 ha) with high estimated yield¹⁷,
and a less permissive one which
excluded reforestation on all smallholder agriculture lands.

Operational
constraints. Four operational constraints were
applied to account for the practical considerations that may influence the
long-term viability of reforested sites. These include proximity to seed
sources (SS), protection status (PA), deforestation risk (DR) and accessibility
for monitoring and management (AM). SS was determined by utilising a 2-km
buffer from the nearest existing forest edge as a proxy for propagule sources^21-24 to support natural
regeneration. Reforestation and thus climate mitigation potential is thus
constrained to areas in relative proximity to seed sources. For PA, we
constrained reforestation to legally protected areas²⁵, namely those
of IUCN categories I-VI, estimating the climate mitigation potential in areas
with some form of protection status. For DR, we constrained reforestation to
areas with acceptable likelihood of transition to deforested areas i.e. ≥ 0.5
probability of deforestation²⁶ (medium to high potential) from a
spatially explicit layer predicting tree cover loss to 2029, estimating the
climate mitigation potential in areas with acceptable deforestation risk. We
also considered AM to account for the need for continued monitoring and
management associated with post-planting site upkeep, thus, limiting
reforestation areas to within a day’s travelling time to the nearest cities²⁷
and estimated the climate mitigation potential for these areas.

Uncertainties across
estimations of climate mitigation potential were derived from the range of
values associated with the aboveground carbon gain and the BAU flux reported in
our literature review (see Table S3 for details), where the minimum and maximum
climate mitigation potential across each forest type were calculated for each
specific study^10,28
or collated across a number of studies^29-31. This produced a
total of 111 maps, which represented the mean, minimum and maximum climate
mitigation potential of each of the constrained reforestation estimations.

Further details for this
dataset are presented in Zeng et. al.