As mentioned above, the model has two main emissions matrices, which track all emissions in the economy. The second emissions matrix tracks emissions in all economy activities (except deforestation, which is dealt with in the transition matrix described above), where emissions are associated to each productive sector and final demand, and can be of two broad types (sources): emissions associated to fuel use and emissions associated to the level of activity of each sector (like fugitive emissions in mining, or CH4 emissions in livestock for example). All emissions are accounted by the original GHG gases, and transformed to CO2 equivalents using the Global Potential Warming for 100 years (GPW-100) coefficients from the IPCC Fifth Assessment Report –SAR (IPCC, 2014). These emissions are in fixed coefficients in either fuel use or the level of sector activity.

# **4. Simulation strategy**

The TERM-BR model was used to perform a series of stylized simulations aiming to illustrate the importance of sectoral technological changes for deforestation, under different scenarios. The first step is to build a baseline from 2015 to 2030, which was used as a reference for the policy shocks. The period 2015-2018 is the historical period, when the model reproduces the observed pattern of the Brazilian economy. The policy scenarios start in 2019, the results of interest being the difference between the baseline and the policy trajectory.

Three main regional broad scenarios were used: a total factor productivity shock (increase) in Brazil, in the Legal Amazon states, and in the South/Southeast Brazil. In the first two cases, the productivity shocks were applied to different broad sectors, one by one: agriculture, mining, services and manufacturing. All activities within those broad sectors received the same productivity shock. An annual 0.5% increase in productivity was considered in the simulations, for each region and sectoral scenario.

# **5. Results**

Table 2 depicts the cumulative impact of a permanent 0.5 percentage point increase in the displayed variables between the years 2019 and 2030. The results show that an increase in productivity across Brazil’s tradable sectors—agriculture, mining, manufacturing—reduces deforestation, both in the Legal Amazon and in Brazil overall. Only productivity gains in the services sector are shown to very modestly raise deforestation. Overall, the model shows that an increase in Brazilian productivity reduces deforestation. This is consistent with Figure 1, where higher national productivity is correlated with lower deforestation. In fact, Table 2 shows that raising productivity even in other parts of Brazil alone would reduce deforestation in the Legal Amazon, consistent with the relatively small size of the Legal Amazon’s economy, which is consequently shaped by broader national developments.

For agriculture, the results in Table 2 tell a nuanced story. When agricultural productivity increases in the Legal Amazon, the Jevons effect emerges, where higher agricultural productivity increases deforestation. This effect as also observed by Cattaneo (2008).At the national level, higher agricultural productivity reduces deforestation. This is consistent with the notion that land markets outside the frontiers of the Legal Amazon are more consolidated—essentially most 8