# **Table 2: FAB analysis for Brazil’s Legal Amazon until 2025**

This table shows an illustration of the FAB matrix analysis. It shows the values from the ambitiousness and feasibility categories and how the two analyses can inform a target setting exercise. Values are expressed as annual forest loss relative to the forest area in the year 2000 and converted into square kilometers in parentheses. For feasibility, relative forest loss is transformed to square kilometers using the forest area of Brazil’s Legal Amazon.

|2021|2022|2023|2024|2025|
|---|---|---|---|---|
|**Most recent value**<br>0.37%<br>(13,235 km<sup>2</sup>)|||||
|**Ambitiousness**|||||
|Average BAU scenario forecast|0.31%<br>(11,178 km<sup>2</sup>)|0.25%<br>(8,738 km<sup>2</sup>)|0.24%<br>(8,469 km<sup>2</sup>)|0.24%<br>(8,470 km<sup>2</sup>)|
|Cutof: low ambitiousness / ambitious|0.30%<br>(10,640 km<sup>2</sup>)|0.25%<br>(8,749 km<sup>2</sup>)|0.24%<br>(8,461 km<sup>2</sup>)|0.24%<br>(8,426 km<sup>2</sup>)|
|Cutof: ambitious / highly ambitious|0.28%<br>(9,802 km<sup>2</sup>)|0.23%<br>(8,122 km<sup>2</sup>)|0.23%<br>(8,016 km<sup>2</sup>)|0.23%<br>(8,083 km<sup>2</sup>)|
|**Feasibility**|||||
|Median development path|0.40%<br>(14,235 km<sup>2</sup>)|0.37%<br>(13,108 km<sup>2</sup>)|0.46%<br>(16,464 km<sup>2</sup>)|0.43%<br>(15,169 km<sup>2</sup>)|
|Cutof: feasible / highly feasible|0.30%<br>(10,689 km<sup>2</sup>)|0.30%<br>(10,636 km<sup>2</sup>)|0.31%<br>(11,260 km<sup>2</sup>)|0.30%<br>(10,769 km<sup>2</sup>)|
|Cutof: low feasibility / feasible|0.21%<br>(7,549 km<sup>2</sup>)|0.21%<br>(7,392 km<sup>2</sup>)|0.26%<br>(9,361 km<sup>2</sup>)|0.21%<br>(7,321 km<sup>2</sup>)|

outcome ranges should be transparent and use evidence-based methodologies. For example, Assunção et al. (2023) describe how recent advances in satellite technology have contributed to monitoring and environmental enforcement in the Amazon. They find that “increasing monitoring and law enforcement by half decreases municipal deforestation by an estimated 25 percent”. The authors used cloud coverage as an instrument for law enforcement and were thereby able to construct a model to estimate counterfactuals for different scenarios. This framework could serve as a benchmark for feasible improvements for other tropical forest countries that consider deforestation-related SPTs paired with similar policy efforts.

# **5.4 Discussion**

We end the exercise in target setting by combining our insights along the ambitiousness and feasibility dimensions into the feasibility-ambitiousness matrix and identifying a promising target. Table 2 summarizes the relevant values from the ambitiousness (Figure 10) and feasibility (Figure 11) analyses. From the ambitiousness analysis, we considered the lower bound of the 90% forecast interval as a potential ambitious target. This translates to a target of an annual forest loss between 0.28% (9,802 km<sup>2</sup> ) in 2022 and 0.23% (8,083 km<sup>2</sup> ) in 2025. From the feasibility analysis, we can see that such a trajectory would lie within the range of feasible outcomes, which taking 2025 as an example ranges from 0.21% (7,321 km<sup>2</sup> ) to 0.30% (10,769 km<sup>2</sup> ).

Hence, the trajectory laid out by the lower bound of the 90% ambitiousness forecast interval is a _reachable_ (ambitious and feasible) target (see Figure 8). So far, we have expressed the target in absolute annual values and not in relation to a baseline outcome. To set a relative target, one would have to define an appropriate ex-ante benchmark and then take the difference between the benchmark trajectory and the absolute target path to calculate the target in relation to the benchmark. In our example, an appropriate benchmark could be the average BAU forecast (see 23