Future environmental and agricultural impacts of Brazil’s Forest Code

The role of improving the enforcement of Brazil’s Forest Code in reducing deforestation in the Amazon has been highlighted in many studies. However, in a context of strong political pressure for loosening environmental protections, the future impacts of a nationwide implementation of the Forest Code on both environment and agriculture remain poorly understood. Here, we present a spatially explicit assessment of Brazil’s 2012 Forest Code through the year 2050; specifically, we use a partial equilibrium economic model that provides a globally consistent national modeling framework with detailed representation of the agricultural sector and spatially explicit land-use change. We test for the combined or isolated impacts of the different measures of the Forest Code, including deforestation control and obligatory forest restoration with or without environmental reserve quotas. Our results show that, if rigorously enforced, the Forest Code could prevent a net loss of 53.4 million hectares (Mha) of forest and native vegetation by 2050, 43.1 Mha (81%) of which are in the Amazon alone. The control of illegal deforestation promotes the largest environmental benefits, but the obligatory restoration of illegally deforested areas creates 12.9 Mha of new forested area. Environmental reserve quotas further protect 5.8 Mha of undisturbed natural vegetation. Compared to a scenario without the Forest Code, by 2050, cropland area is only reduced by 4% and the cattle herd by 8%. Our results show that compliance with the Forest Code requires an increase in cattle productivity of 56% over four decades, with a combination of a higher use of supplements and an adoption of semi-intensive pasture management. We estimate that the enforcement of the Forest Code could contribute up to 1.03 PgCO2e to the ambitious GHG emissions reduction target set by Brazil for 2030.


Introduction
Over recent decades, Brazil has become one of the top global producers and exporters of several agricultural commodities: it is the largest sugar and beef exporter, the second largest maize exporter and the third largest soybean exporter [1,2]. This is possible because of the expansion of production area and gains in productivity. It is estimated that the average farm productivity increased by 2.55% per year between 1985 and 2006 [3]. Investment in infrastructure and the transformation of low-fertility soils into highly productive areas through the development of new technologies have also been key for the expansion of cultivated area in the Cerrado and Amazon biomes [3,4]. As global demand for agricultural commodities, which is driven by population and income growth, is poised to increase in the coming decades [5][6][7], the amount of production that will result from additional land conversion in Brazil remains unclear [8].
The large-scale deforestation in the Brazilian Amazon in the mid-2000s, with a 27,772 km 2 deforestation peak in 2004 [9], is correlated to the expansion of pasture for cattle ranching and, to a lesser extent, soy [10−13]. In 2005, land-use, land-use change and forestry (LULUCF) activities accounted for approximately 80% of Brazil's greenhouse gas (GHG) gross emissions [14,15] with the deforestation in the Amazon representing the lion's share of the Brazil's LULUCF emissions. The situation has changed since 2005, with an 83% reduction in deforestation in the Brazilian Amazon between 2004 and 2012 to reach 4,656 km 2 [9]. This sharp reduction resulted from the combination of improved satellite monitoring systems, the creation of new protected areas [16], the interventions in critical food supply chains [17], and the enhanced enforcement of the Forest Code (FC) through imposing fines, restricting access to rural credits [18], confiscating cattle and machinery, and even implementing prison sentences for lawbreakers [12, 19−22].
In the Paris Agreement, Brazil committed to reduce its GHG emissions by 37% below 2005 levels by 2025 and to reach a 43% reduction by 2030 [23]. Brazil's Nationally Determined Contributions (NDC) mentions the enforcement of the Forest Code as a key mitigation measure. However, the fact that 2016 encompassed the highest deforestation level in four years, with a 29% increase compared to 2015 and a 75% increase compared to 2012 [9], raised some new concerns about the enforcement of the Forest Code. Among its main provisions, the Forest Code identifies the minimum percentage of forest to be preserved, which is called the Legal Reserve (LR) and varies across the six biomes (figure S1 available at stacks.iop. org/ERL/13/074021/mmedia), on each property; the LR ranges from 80% in the Amazon biome to 20% in the Atlantic Forest, and it designates environmentally sensitive areas, such as riversides and hilltops, as areas of permanent preservation (APP). These measures correspond to vast areas since it is estimated that private properties cover 67% of the Brazilian territory [24] and contain more than 50% of Brazil's native vegetation [25]. However, enforcement has been a major issue; in 2005, in the Amazon region of Mato Grosso state, 82% of the farms surveyed were not in compliance with the Forest Code [26].
The 2012 revision of the Forest Code included the obligation that illegally deforested areas be restored at the landowners' expense, but it provided amnesty for small farms (from 20 ha in southern Brazil to 440 ha in the Amazon). The provision of an environmental reserve quota system (Portuguese acronym: CRA), which is a tradable legal title of forest surpluses that can be purchased to offset environmental debts in the same biome, could make it less costly to conserve forests in areas with less agricultural return and less fragmented conservation of the remaining native vegetation [27].
However, five years after the last revision, the Forest Code remains contested by both the agribusiness lobby, which still considers it a barrier to economic development, and the environmentalists, who consider the current code to be a step backward vis-à-vis the previous legislation [25].
We quantify the future impacts of Brazil's Forest Code, the country's main environmental law to reduce deforestation, on both the agricultural sector and the environment through the year 2050. The rigorously enforced Brazil's Forest Code scenario includes the full control of illegal deforestation, the amnesty of legal reserve debts from small farms, the environmental reserve quota mechanism, and the mandatory restoration of legal reserve debts. We use the recursive dynamic, global, bottom-up partial equilibrium model GLOBIOM [28,29,30]. GLOBIOM-Brazil includes a series of refinements that reflect Brazil's specificities [31]. The model computes consumption and trade for each of the 30 regions of the world; it also computes production and land use at the 50 km × 50 km grid level for the most important crops and animal products in Brazil. In this framework, deforestation depends on the feedback between future agricultural demand and biophysical and regulatory constraints on land. This is the main difference from other studies, where deforestation was first estimated separately, often on the basis of historical trends, and then spatially allocated using land characteristics [10, 32−35]. Other approaches where deforestation is computed based on the expansion of agriculture have usually focused on only one commodity and did not take into account market feedback [36,37].
Moreover, this study disentangles the impacts of two key measures of the Forest Code: the control of illegal deforestation and the restoration of illegally converted areas. To this end, we investigate the impact of an uneven enforcement of the Forest Code through alternative scenarios in which the control of illegal deforestation is either enforced only in the Atlantic Forest biome, or in the Atlantic Forest and the Amazon biomes, or fully enforced in the whole country. These scenarios highlight the role of the control of illegal deforestation and the potential leakage into other biomes. We also evaluate the effect of an imperfect enforcement of the Forest Code in the Amazon and the Cerrado biomes by generating scenarios that take into account the historical compliance with this environmental law. In April 2017, approximately 83% of the private properties were registered [38] in the GIS-based Environmental Cadastre (Portuguese acronym: CAR). To test for the restoration obligation of previously illegally converted areas, we use a map of the native vegetation debts, which was produced based on the CAR information [39]. Since the environmental reserve quota mechanism is still under discussion, we also run an alternative scenario with the full restoration obligation, i.e. without possible compensation from environmental surpluses elsewhere.
The trade-offs between environmental conservation and agricultural production across different scenarios are highlighted.

Methods
The GLOBIOM-Brazil model adapts IIASA's global biosphere management model (GLOBIOM) to the Brazilian context. It is a global partial equilibrium model that simulates the competition for land among the main sectors of the land-use economy (i.e. forestry, agriculture and bioenergy) that are subjected to resource, technology and policy restrictions. GLOBIOM-Brazil is recursively run for 10 year time steps, starting at the baseline year of 2000 and continuing to the year 2050. The model simulates the competition for land at the pixel level by maximizing the sum of consumer and producer surpluses. The geographically explicit representation of the model is a uniform grid of 0.5 • by 0.5 • amounting to 3001 pixels in Brazil, and it has a spatial resolution of approximately 50 km × 50 km at the equator.
The model considers international trade and exogenous drivers, such as gross domestic product (GDP) growth, population growth, and dietary trends. Population and GDP changes follow the assumptions from the 'middle-of-the-road' Shared Socioeconomic Pathway (SSP2) [40]. Production is endogenously adjusted to meet the demand for all 30 economic regions, which include Brazil. The equilibrium quantities and prices are obtained for each region and product as the result of the optimization procedure. The model optimizes over six land-use classes (see figure S2 and table S1). The final demand, processing quantities, prices, and trade are computed at the regional level.
The model simulates 18 crop products, five forestry products and seven livestock products. Crop productivities are defined by the biophysical model EPIC [41] for each crop and management system (i.e. subsistence, low-input rainfed, high-input rainfed, and high-input irrigated). The model also endogenously adjusts the productivity by changing the management system from low to high input. Livestock production systems cover five different species (bovines, sheep, goats, pigs and poultry). Ruminants are raised according to eight livestock production systems, ranging from grazing-humid to mixed-arid [42]. Intensification or extensification of livestock production and feed substitution is performed by making changes among the production systems. Particularly in Brazil, a semi-intensive cattle ranching production system is also allowed [30]. The RUMINANT model is used to estimate bovine and small ruminant productivity and feed requirements [42,43]. Feeds consist of grass, crop residues, grain concentrates, and other feed stuff.
The projections presented in this study are based on a consistent 2000 land-cover and land-use map of Brazil. This map combines information from official statistics on crop and livestock production, from maps of protected areas, and from different satellite images for the base year 2000 (see figure S3). We use a detailed and up-to-date representation of the national transport infrastructure (see figure S4) with a discrimination of transportation costs per product type (i.e. solid, liquid and grain) and destination (e.g. nearest state capital, internal consumption, nearest seaport, or external markets).
Due to the lack of information on property boundaries, we calculate the LR surpluses for each pixel (roughly 50 km × 50 km) as the amount of native vegetation that exceeds the legal reserve. The LR is calculated by multiplying the amount of land in a pixel by the percentage of the LR requirement in that pixel (see figures S5 and S6). We thus obtain the total number of hectares of native vegetation which should be protected in each pixel according to the LR. Enforcement costs are not considered. Passive forest restoration is assumed, and it is also assumed there are no direct costs (including the opportunity cost of taking land out of production) imposed on the farm owners in terms of legal reserve restoration. Environmental debts, downscaled to 50 km × 50 km pixels (see figure S7), are based on CAR data downloaded in December 2016 [39]. The total environmental debts amount to 18.7 million hectares (Mha) in Brazil, 10.8 Mha of LR debts and 7.9 Mha of APP debts. Consolidated environmental debts are calculated by considering the amnesty of small farms [39].
Given the uncertainties regarding the future use of public areas in the state of Amazonas, we assume that only 20% of the unclaimed public lands in this state will be designated as private properties and, thus, be part of the CAR database. Then, only 20% of forest surpluses in this region are considered in our environmental reserve quota stock estimates. Without this assumption, the amount of forest surpluses in the Amazonas state alone would be more than enough to compensate all the LR debts within the whole Amazon biome, which could distort the CRA market. Another source of uncertainty is related to the debt offset mechanism. First, we assume that environmental debts will be compensated by the quota system only in cells with deficits overlapping soybean and sugarcane production; this assumption is due to the profitability of these crops [44] and the agroecological restrictions of sugarcane production. Second, we assume that cells with larger deficits are compensated first, and cells with larger surpluses are used first to offset the debts within the same biome. This assumption can be justified by the fact that areas with larger deficits are more likely to have higher opportunity costs. In these areas, landowners are more inclined to buy quotas and keep their land in production, rather than converting them to restored forest. On the other hand, areas with larger surpluses are more likely to have lower opportunity costs, and the corresponding landowners are more willing to sell their available quotas rather than suppress the production of excess vegetation.
Emissions from the land-use change and forestry (LUCF) sector are calculated from the endogenous land-use changes projected by the model and the different biomass maps. The carbon content from forests and native vegetation is taken from Brazil's Third Emissions Inventory [45]. The carbon content in the biomass of short-rotation plantations comes from Havlik et al [28]. The biomass map of Ruesch and Gibbs [46] is used for pasture and non-productive land. The release of carbon as CO 2 from the terrestrial biosphere to the atmosphere occurs in one simulation period (i.e. a 10 year time step) of deforestation and other land-use changes. By contrast, CO 2 removal from the atmosphere by forest regrowth varies from a few years to several decades. We defined different carbon uptake rates from forest regrowth according to each biome (see SI). We compared the model results for the first period of simulation, i.e. 2000−2010, with Brazil's official statistics as a baseline for model validation (see figures S8-S13). Accumulated deforestation from PRODES/INPE [9] between 2001 and 2010 in the Amazon biome amounts to 16.53 Mha; in comparison, our model projects 16.45 Mha for the same period and region (see figure 1). Differences were concentrated around the Xingu area and along road BR-163 in the state of Pará and are probably due to need of further improvements in the local transportation network. More importantly, the model captures the trends in deforestation and agricultural expansion in Brazil between 2000 and 2010 without using historical deforestation as input data, which enhances confidence in the future land-use changes projected by the model. For more details, see SI.

Forest code scenarios
The FC scenario is a command-and-control scenario that attempts to capture the future impacts of all key provisions of a rigorously enforced Brazil's Forest Code. It includes the full control of illegal deforestation after 2010, the amnesty of LR debts for small farms (SFA) before 2010, the environmental reserve quota mechanism after 2020, and the mandatory restoration of LR debts after 2020. Legal deforestation or conversion of LR surpluses is allowed at all times in all biomes, with the exception of the Atlantic Forest, which is protected by more restrictive legislation. The LR debts not waived by the SFA are fully paid by the farm owner, either by purchasing CRA quotas from the LR surpluses in the same biome or by taking illegally converted areas out of agricultural production for native vegetation restoration.
Seven additional scenarios were designed to investigate a gradient of environmental protection around the Forest Code. The counterfactual analysis is a scenario without control of illegal deforestation in all biomes (except for the Atlantic Forest) and without any requirement for forest restoration. The no forest code (NoFC) scenario allows both legal and illegal deforestation at all times, which is driven by the demand for agricultural commodities, and does not include any policy restrictions. This type of scenario is important for evaluating the losses and gains of an unsustainable future without the enforcement of the Forest Code. Building upon the NoFC scenario, illegal deforestation control (IDC) is extended from the Atlantic Forest to the Amazon biome (IDCAmazon). Then, we expand the illegal deforestation control to the entire country (IDCBrazil). Three additional scenarios were built upon the NoFC to test different levels of compliance with the Forest Code regarding the IDC. In these scenarios the illegal deforestation  NoFC  full  no  no  no  no  no  IDCAmazon  full  full  no  no  no  no  IDCBrazil  full  full  full  full  no  no  IDCImperfect1,2,3  full  partial  partial  no  no  no  FC  full  full  full  full  yes  yes  FCnoCRA  full  full  full  full  control is imperfect or partial, and covers the Amazon and the Cerrado biomes. A probability of enforcement of the IDC is calculated per grid cell (see figure  S14) and it is used as an index to restrict or not the illegal deforestation (IDCImperfect1). The probably of enforcement is increased by 25% (IDCImperfect2) and also by 50% (IDCImperfect3), and kept constant during the period 2010−2050. See SI for more information. Finally, we investigate the role of obligatory forest restoration with illegal deforestation control but without any compensation mechanism from the environmental reserve quota system (FCnoCRA). Table 1 shows an overview of the different scenarios.  the Atlantic Forest (28%). Within the Cerrado, 42% of this expansion will occur in the Matopiba region (a region in the states of Maranhão, Tocantins, Piauí and Bahia, located along the border between the Cerrado and the Caatinga biomes) and is led by soybeans and maize. The decrease in pasture area is also concentrated in the Cerrado and the Atlantic Forest biomes ( figure 3(b)), showing that cattle ranching intensification spares land for cropland expansion and decreases the pressure of native vegetation conversion. Compared to the NoFC scenario, the FC scenario projects that 68% less forest and native vegetation will be converted to pasture and that 39% less forest will be impacted by cropland expansion. On the other hand, the FC scenario doubles the use of non-productive areas between 2010 and 2050 (figure S16). From 2010-2050, the Amazon biome has the highest relative growth of cattle heads per ha among the other biomes (70%), followed by the Atlantic Forest (43%) and the Cerrado (37%) biomes (figure S13). This cattle ranching intensification under the FC scenario is possible due to the combination of an 8% increase in the cattle herd growing in mixed grass and crop-based feed systems, which produce more meat per cattle head. Also, in 2050 43% of the cattle herd is maintained in semi-intensive managed pastures, which supports more cattle heads per hectare (figure S17). In spite of the overall decrease in pasture area in Brazil, between 2010 and 2050, pastures still expand in the Amazon by 55% over the LR surpluses (legal deforestation). The FC scenario projects that the cattle herd will increase by 164% in this biome, from 41-108 MTLU, during the same period. By 2050, 48% of Brazilian cattle will be kept in the Amazon. Since the expansion of cattle ranching is historically linked to deforestation in this biome, enforcing compliance with the environmental laws is critical to avoid a new surge in forest clearing [47].

Agricultural gains and environmental losses of rigorously implementing the forest code
In summary, by 2050, the agricultural gains obtained by not enforcing the Forest Code (NoFC) in Brazil include an increase of 4% in crop area and an increase of 8% in the cattle herd. On the environmental side, the lack of enforcement of the Forest Code between 2010 and 2050 results in an accumulated deforestation of 65.5 Mha without any forest restoration. Figure 4   protected biomes (in terms of LR requirements), such as the Cerrado and the Caatinga, when the law is enforced only in the Amazon [6,25,48]. Extending the illegal deforestation control to the entirety of Brazil's territory (IDCBrazil) results in a further accumulated deforestation reduction of 29%, from 34.1 Mha to 24.3 Mha, between 2010 and 2050. This extension is particularly important to avoid leakage effects into the Cerrado biome ( figure 4(c)). When the ban on illegal deforestation across Brazil is complemented with the additional provisions of the Forest Code (i.e. the FC scenario), deforestation levels remain approximately the same, but 12.9 Mha of forest are restored ( figure 4(d)). It is important to mention that the loss of dry forests in the Caatinga biome accounts for 8.1 Mha between 2010 and 2050 under the FC scenario; of this loss, 64% is due to pasture expansion, and 36% is due to cropland expansion. Due to water availability constraints (the rainy season is short and irregular, and the region is prone to frequent droughts), agricultural expansion in the Caatinga is limited to its historical trends in our simulations.
Although the Forest Code reduces the native vegetation losses in Brazil, it does not prevent 25 Mha of deforestation between 2010 and 2050. This legal conversion is located mostly in areas with large forest surpluses in the Amazon, the Caatinga and the east of Cerrado, where the last undisturbed remnants of this biome are located ( figure 4(d)). Approximately 65% of this deforestation allowed by the law is due to pasture expansion, especially in the Amazon biome ( figure 5(a)), and 35% is due to cropland expansion, especially in the Cerrado biome within the Matopiba region ( figure 5(b)). The adoption of zero supply chain agreements by the private sector, similar to the soy and the cattle moratoria in both the Amazon and the Cerrado biomes, would prevent this legal deforestation. Under the FC scenario, the CRA compensates for approximately 5.8 Mha (figure S18) of the LR debts, which decreases the area of forest restoration from 18.7-12.9 Mha. Our FCnoCRA scenario, where there is no compensation of any environmental debt, restores 18.7 Mha of LR debts (see figure S19). However, the FCnoCRA projects an increase of 1.4 Mha of deforestation by 2050 when compared to the FC scenario. This occurs because the quotas protect native vegetation from the legal conversion allowed by the law while keeping the already illegally converted areas in production. This emphasizes that environmental reserve quotas, if well implemented, can play important roles in the conservation of pristine native vegetation remnants. For the agricultural output, because the quotas generally transfer production from one site to another, the impact of the CRA in crop areas and on cattle herds is small and mostly related to productivity gradients within the biomes. The additional 5.8 Mha of forest restoration in the FCnoCRA scenario makes the net forest area of this scenario the highest among all the others, even with the increase in deforestation.

Evaluating different levels of compliance with the forest code
Different levels of compliance with the Forest Code were tested by implementing an imperfect or partial enforcement of the illegal deforestation control (IDCImperfect1, IDCImperfect2 and IDCImperfect3 scenarios). In these scenarios, the maximum amount of illegal deforestation depends on the probability of enforcement: the lower the probability of enforcement, the higher the maximum amount of illegal deforestation (see SI for more details). Figure 6 From the production side, increasing the level of illegal deforestation control as in the IDCImperfect scenarios has a positive impact in the process of cattle ranching intensification already observed in the FC scenario. In other words, as shown in figure S21, an increasingly enforced Forest Code leads to a reduction in pasture area by 2050 with a very little impact in the number of heads of the Brazilian bovine herd. Compared to the FC scenario, by 2050, cropland area is only reduced by less than 1.5% in the IDCImperfect scenarios.

Discussion and conclusions
Historically, the expansion of cropland and pasture in Brazil has occurred at the expense of pristine native vegetation and the environmental services they provide, including carbon storage and biodiversity conservation. Given the increasing global demand for agricultural products and the competitiveness of Brazilian agricultural production compared to other regions of the world, our results show that the agricultural sector will continue to grow in Brazil in upcoming decades. We show that Brazil's revised 2012 Forest Code is a key tool for helping to reconcile the conflicting goals of environmental conservation and agricultural production growth. If the Forest Code is not fully implemented and rigorously enforced, which would ensure that the LRs and APPs are preserved and that the native vegetation areas that have been illegally deforested are restored or compensated, deforestation will rapidly increase, especially in the Amazon, with meager economic gains. An imperfect enforcement of the illegal deforestation control based on historical Forest Code compliance levels, as in the IDCImperfect1 scenario, prevents only 4.8 Mha of native vegetation loss in Brazil when compared to the NoFC scenario. Increasing the probability of enforcement in the Amazon and the Cerrado biomes (IDCImperfect3 scenario), the avoided native vegetation loss jumps to 18.1 Mha, which points to the importance of increasing the budget of the IBAMA (Brazilian Institute of the Environment and Renewable Natural Resources) to expand command-and-control actions in these regions. If illegal deforestation is controlled only in the Amazon biome, our results show leakage to the other biomes i.e. a 3 Mha increase in native vegetation conversion in the Cerrado biome and a 3.8 Mha increase in conversion in the Caatinga biome between 2010-2050. Finally, full enforcement of the Forest Code could lead to 12.9 Mha of restored area.
In the Amazon, due to its huge size, complex land tenure structure and continuous expansion of cattle ranching, the projected decrease in deforestation remains vulnerable. A comparison between the NoFC and the FC scenarios shows that, between 2010 to 2050, cumulative deforestation in the Amazon could increase by almost 40 Mha if the fight against illegal deforestation is not stopped. The recent spike in the Amazon's deforestation rate demonstrates that pressure remains high despite the private sector's zero deforestation agreements, which are similar to the soy moratorium, and even with the current governmental presence, illegal deforestation occurs. The removal or reduction of that enforcement effort would likely result in greater forest losses. In terms of emissions, the average deforestation rate per decade of 6.3 Mha in the Amazon in the future without forest restoration, as projected by the IDCImperfect3, will project a reduction in the LUCF emissions by 2030 of 0.68 PgCO 2 e yr −1 compared to 2010, 66% of the projected emissions reduction (1.03 PgCO 2 e yr −1 ) for the FC scenario. These results highlight the importance of a rigorously enforced Forest Code for Brazil to achieve its international goals of emissions reduction.
As part of the NDC submitted for the COP Paris 2015, Brazil pledged to restore 12 Mha of forests by 2020, which is comparable with the 12.9 Mha of restored area in our simulations. Our study does not address how to achieve this restoration. We assume passive restoration in our simulations, but in reality, restoration might need some investments to work. A recent publication suggests that in Minas Gerais state, only 36% of the deficits could be restored using passive restoration and that the restoration of the highly degraded areas would more than double the restoration costs [49]. The cost of restoration and the lack of technical know-how might be a real challenge for poor farmers. In the Atlantic Forest biome, since 2000, many environmental NGOs have been willing to compensate part or all of the restoration costs; however, their lack of enforcement did not give farmers clear incentives to comply with the law [50].
Primary forests have a higher biodiversity value and carbon stocks than areas of regrowth, as it can take up to 300 years for biodiversity to be restored when a forest regenerates [51]. Therefore, the implementation of quotas has important implications on biodiversity, especially in the Cerrado, which is a biodiversity hotspot. Depending on how the CRA market is going to work (which has yet to be decided by a complementary law to be voted on by the Parliament), it could either protect and conserve environmentally important areas, rewarding law-abiding landowners, or simply legalize illegally deforested areas in exchange for low-conservation value areas [52]. The title price must be profitable for both creditors and debtors to avoid leakages and speculation. Moreover, farmers need to be informed about the existence and the functioning of this mechanism [53]. It is important that the quota market is quickly regulated because important areas in terms of conservation purposes may disappear if this measure takes too long to be implemented.
In the past, in many parts of Brazil, there has been a widespread sentiment among rural producers that the old Forest Code was unrealistically restrictive, providing insufficiently convincing reasons to comply with it [27]. Here, we show that, although Brazil's 2012 Forest Code is not perfect, there are both economic and environmental benefits for producers and other stakeholders to support it. On the economic side, the enforcement of the Forest Code accelerates agricultural intensification, and the small reduction in overall production might be compensated by higher market prices. The near completion of the rural Environmental Cadastre (CAR) is crucial but is not sufficient to guarantee the enforcement of the Forest Code. In addition, there must be political will and resources in the federal government to cross-check the information of the CAR, to carefully monitor the implementation of the law, and to rigorously enforce its application across the entire country. If Brazil succeeds in this endeavor, there will be multiple benefits for its citizens, and it will establish a useful model for other developing countries facing similar challenges.