Fig 1.
A generic theory of change for land tenures effects on carbon stocks.
The lines symbolize hypothetical pathways of how governance components and drivers of carbon stocks change influence ITs (orange), OAs (yellow), PAs (green) to result in an expected outcome.
Fig 2.
Panama and the Amazon Basin portions of Colombia, Ecuador, Peru, and Brazil. Land tenure is classified as PAs (green), ITs (orange), OAs (yellow), and Other Land (grey).
Table 1.
PAs and ITs included in the study.
Fig 3.
Workflow to infer the temporal and spatial effect of ITs, OAs, and PAs on carbon stocks.
Fig 4.
The temporal effects of ITs, OAs and PAs on aboveground carbon stocks across neotropical countries in 2003 and 2016.
Significant temporal effects (p < 0.05) are represented as colored bars and percentages, indicating the additional/fewer carbon stocks secured by allocating ITs (orange), OAs (yellow), and PAs (green) relative to the baseline (Other Lands, grey) after controlling for spatial location. Error bars reflect 95% confidence intervals for the baselines and temporal effects.
Fig 5.
Observation units sampled through matching analysis in ITs, OAs, and PAs from Panama.
Table 2.
Mean distance to ITs’, OAs’, and PAs’ boundaries of observation units sampled through matching analysis by country and land tenure.
Fig 6.
The spatial effect of ITs, OAs’, and PAs on carbon stocks during 2003 and 2016 in neotropical countries.
Significant temporal effects (p < 0.05) are represented as points and percentages, indicating the additional/fewer carbon stocks secured inside the boundaries of ITs (orange), OAs (yellow), and PAs (green) relative to surrounding lands at multiple buffer distances (0–0.5 to 0–15 km). The spatial effects in 2003 are represented by empty points and dashed lines, while in 2016, they are full points and continuous lines. The values in parentheses represent the percentual increase/decrease in spatial effects between 2003 and 2016. Error bars reflect 95% confidence intervals for the temporal effects.