Evidence on scaling forest restoration from the Atlantic Forest Restoration Pact in Brazil

Domain: earth_science
Published: 2025-05-21
Editorial outcome: major_revision

Abstract
Forest restoration is a vital strategy to combat climate change, preserve biodiversity, and maintain ecosystem services. Yet scarce evidence exists evaluating the impact of forest recovery interventions at scale. This study estimates the impact of the Atlantic Forest Restoration Pact on forest restoration in Brazil. We compare forest change on lands supported by the Pact to change on similar lands, before and after the program started, thereby isolating the causal impact of restoration support. Here we show that the intervention increased restored forest cover by 10-20 percentage points, likely because it helped to overcome key financial and informational barriers faced by private landowners. Larger effects are associated with greater distance to cities and more state-level environmental enforcement. These findings demonstrate that large-scale forest restoration on private land is possible, but that low returns from competing land uses and complementary institutional environments play key roles in supporting these efforts.

Introduction
Forest restoration is a powerful nature-based solution to mitigate climate change1,2, safeguard biodiversity3, and preserve ecosystem services4. Prominent global agreements, like the Bonn Challenge and the Sustainable Development Goals, call for large-scale forest restoration. To succeed, restoration must include private landowners, who own substantial parts of available restorable land globally5–7 and who face competing pressures to use land for agriculture, pasture, or peri-urban development. Many of these private land managers have highly land-dependent and often precarious economic livelihoods8. Even when restoration produces long-term profits or improves local water availability, landowners can face high up-front investments, labor costs, and knowledge barriers that hamper meaningful restoration activity. To reduce these barriers, restoration programs, such as those supported by the Atlantic Forest Restoration Pact (PACTO) in Brazil, offer economic and technical assistance to private landowners9,10. Although such initiatives are expanding rapidly worldwide11, there is little empirical evidence regarding their ability to generate additional forest restoration relative to a plausible comparison group of properties.

We evaluate the forest recovery impacts of PACTO—a large-scale restoration coalition targeting private land in the Brazilian Atlantic Forest (BAF). The BAF is an important setting in which to implement and evaluate large-scale forest restoration because it is sizeable, ecologically significant, and 75% privately owned12. Nearly 90% (roughly 1.1 million km2) of the BAF has been cleared or converted for agriculture, pasture, logging, mining, or urban centers over the last 500 years13. Reversing this trend through restoration is challenging since approximately one-third of South America’s population lives inside the original forest boundaries, and many economic livelihoods are dependent on agricultural and pastoral land use. Conserving and restoring the remaining fragments of the forest may help protect unique species in danger of extinction, in addition to providing important carbon, erosion, and watershed function services14–18.

PACTO is a collective agreement and regionalized knowledge-sharing network of over 250 stakeholders across the 17 states of the BAF, which encompasses nearly 30 degrees of latitude and a wide variety of forest types. The PACTO network includes government agencies, NGOs, companies, and research centers that implement forest restoration by directly assisting private landowners. The pact aims to democratize science-based restoration methods, apply a common monitoring protocol across the region, and assist implementers in securing funding. PACTO implementers, usually NGOs or government agencies, support forest restoration by providing landowners with free or subsidized materials, labor, training, and other activities. Depending on location and ecological conditions, they may support active restoration (i.e., direct seeding or planting young trees), passive restoration (i.e., allowing forests to regenerate naturally), and assisted natural regeneration (i.e., fencing off areas or removing non-native species)19,20.

PACTO operates in the context of Brazil’s federal and state initiatives to protect and enhance forests. The country’s key forest policy is the federal Forest Code, presently formalized in the Native Vegetation Protection Law (NVPL) of 2012. This law requires all landowners in the BAF to set aside 20% of their land as a Legal Reserve of native vegetation conservation, with additional requirements for ecologically-important areas (such as riparian zones) known as “Areas of Permanent Protection” (APPs)21. Since landowner compliance with these requirements is extremely low—there are 6.2 Mha (million hectares) of native vegetation deficits on private land in the BAF22—restoration needs are ubiquitous across the PACTO states. However, the explicit and implicit costs of restoration for private landowners in the BAF are high, particularly for active restoration10. PACTO’s approach recognizes that landowners often need material and knowledge support to restore forest effectively or at all. A core aim of the program is to help landowners comply with the law’s 20% native vegetation minimum19,20.

We use a quasi-experimental approach to estimate the impact of collaboration with PACTO on net forest recovery within private land in six states of the Atlantic Forest from 2000 to 2018 (Fig. 1). Our analysis compares restoration outcomes for land that received support from a PACTO implementer after 2009 to similar land units that did not. PACTO implementers began working with landowners on restoration in 2009, with participation growing over time. Because information on the year of first enrollment is not available, we consider all properties that received support to be treated from 2010 onward. We divide the landscape into 2 ha (hectare) units of analysis and define treatment according to having any land enrolled as well as by the proportion of the unit that overlaps with a PACTO-supported parcel.

The estimation includes two steps. First, we use statistical matching to create a comparison group of land units that were similar to the PACTO group in key aspects driving likely program enrollment and forest change prior to 2009 but that did not participate in PACTO. The choice of matching covariates is informed by economic theory and the results of interviews with stakeholders (see Supplementary Discussion). We select potential matches for the treated grid cells from the pool of units within 0.5-5 km of each treated cell, using propensity score matching within municipality. This control group, constructed via matching, plausibly represents the unobservable counterfactual scenario of what would have occurred on PACTO-assisted land without PACTO program intervention.

Second, we compare outcomes for the PACTO-supported and non-PACTO-supported land over time using difference-in-differences regression analysis. The difference in net forest recovery between the groups after PACTO assistance began (2010–2018) versus before (2000-2009), conditional on included controls, represent the causal effect of PACTO support. Similar methods have been used to evaluate global forest protection efforts such as parks or payments for ecosystem services23–26, but causal evaluations of reforestation programs are rare27.

To understand the potential mechanisms and drivers of restoration and PACTO’s role in the process, we conducted semi-structured field interviews with stakeholders from several points on the restoration supply chain (See Fig. 2, Supplementary Discussion, Supplementary Note 1). These interviews helped to identify key barriers and facilitating conditions relevant to the BAF context, which inform our choice of matching variables. In addition, economic theory and our interviews elucidated important drivers of change and led to testable hypotheses about heterogeneity in program effectiveness. Specifically, we test for differential impacts of PACTO by distance to cities as a proxy for opportunity costs (the foregone profits from alternate uses of land), and across state entities, because the risk of legal sanction for violating minimum native vegetation requirements varies by state and was highlighted in the interviews as a key factor driving restoration uptake and maintenance activities.

In this work, we find evidence of restoration gains attributable to PACTO network support. Between 2010 and 2018, we estimate that net forest recovery increased by 10-20 percentage points on average in PACTO-supported land relative to the counterfactual. Consistent with the financial and legal compliance motives for restoration found in the interviews, we find that impacts were greatest in more remote areas–where opportunity costs are likely to be lower due to higher transport costs for agricultural products–and in states with higher enforcement activity, where the benefits of legal compliance are higher. These findings demonstrate both the promise of forest recovery programs on private land and the importance of the economic and institutional context in scaling-up restoration efforts.

Results
Drivers of restoration success
Figure 2 shows the organizational structure of the PACTO network. Structured interviews with stakeholders in each of these categories (Fig. 2, Supplementary Note 1, Supplementary Discussion) gave us an understanding of the restoration context and drivers of impact. According to these interviews, the decision to restore is primarily financial. This is consistent with a basic economic theory of restoration where a landowner’s choice to restore forest on a given parcel is driven by a comparison of their own private benefits and costs of restoration (see Supplementary Discussion). Indeed, while speaking about the incentives for restoration, one stakeholder noted: “the most painful part of the body is the pocket”. While the landowners themselves have the ultimate decision authority over their land, their decisions are strongly conditioned on the expected financial gains and losses of land-use options, the influence of government regulations and programs, contractual relationships with large companies, and support provided by restoration NGOs and the PACTO leadership council. We describe the interview results and the Forest Code in greater detail in the Supplementary Discussion.

Overall, the benefits of forest restoration include the reduced risk of government sanction because of better compliance with the Forest Code, access to agricultural credit, additional income, and increased environmental quality or ecosystem service flows. Because of the risk of financial sanctions due to violations of the 20% forest coverage mandate in the Forest Code (according to at least one restoration consultant and a state prosecutor that were interviewed), the perceived benefits of forest restoration are related to the probability that the law is enforced. PACTO’s support may help land holders come into compliance with the law, which lowers the risk of fines. In addition, compliance with the Forest Code may also increase producer access to certain government-supported credit programs. For example, article 41 of the Native Vegetation Protection Law states that agricultural credit, agricultural insurance, tax credits, funding, and other benefits are available to landowners as compensation for compliance with forest restoration and conservation requirements of the law. This may be beneficial for some participants. In our interviews, some producers also noted a link between forests and water, both for consumption and for agricultural use, and others noted that over the long term, restoration projects that include agroforestry may help provide additional income from tree crops (see Supplementary Discussion). Reforestation projects may also improve soil stability and reduce erosion or sedimentation18,19.

On the cost side, interview participants articulated a wide range of factors mediating large-scale private land restoration, and detailed how the PACTO collaboration network works to reduce cost in order to make restoration more attractive. There are implicit and explicit costs for each hectare of land restored, which may differ considerably across locations. Because restoration directly precludes highly profitable land uses, like agriculture, and cattle ranching28, the main implicit cost is the foregone profits from alternate uses (the opportunity cost of restoration). In interviews, PACTO leadership and its implementation partners emphasized the strategy of targeting degraded or abandoned land which would have lower opportunity costs, facilitating engagement in restoration. The explicit costs of restoration vary depending on the method. PACTO implementing partners emphasized the high cost of active restoration, which re-creates forest on abandoned pasture or fallow land. This is the dominant form of restoration in the BAF and includes planting and caring for a variety of delicate tree species until they are able to survive, which can take 3–5 years. It requires both capital (seedlings, fencing) and labor (for weeding, planting and maintenance) costs. This cost can reach $8000 US dollars per hectare in the initial investment stage10. NGO implementers associated with PACTO state that they often covered most or all of the direct costs of restoration (Supplementary Discussion), providing a key form of support.

Interviewees also revealed the importance of networks in reducing informational and direct costs. PACTO supports information provision by establishing principles of restoration management and disseminates information to 16 “Regional Units.” Institutions affiliated with PACTO are involved in seedling production and scientific experiments in restoration that are aimed towards cost reduction through technological innovation and strategic use of inputs. This knowledge is valuable, because effective restoration requires complex technical expertise about ecological succession, soil, species, planting techniques, and market values.

Restoration may fail without sufficient attention to the match between local conditions and restoration methods, or the marketability of agroforestry products10. The decentralized nature of PACTO means that implementers have deep local knowledge in addition to strong connections with scientific institutions currently researching restoration methods. Interviewees emphasized that implementers use this knowledge to provide the most effective technical assistance possible, and also to educate landowners about longer-term benefits of restoration on their specific parcel of land.

Participation in PACTO induced additional restoration
The PACTO enrolled areas include 33,011 hectares across six states in the Brazilian Atlantic Forest (Fig. 1). This is comprised of 4697 unique parcels with an average parcel size of seven hectares. Much of this is non-forested farmland (Supplementary Fig. 1). A substantial portion of the parcels (32%) are in Bahia, with 18% of parcels in each Paraná and Espírito Santo, 14% in Rio de Janeiro, 11% in Minas Gerais and 6% in São Paulo.

PACTO began work in a period of high pressure on the Atlantic Forest. Figure 3 demonstrates that net forest recovery was declining in both the land that would receive support from PACTO and the matched control areas before 2009 (after a peak in 2003). However, trends across the enrolled and unenrolled groups are not statistically different prior to the intervention (Supplementary Table 6). After 2009, trends for the PACTO-supported land and the control areas diverge. The decline in net forest recovery reversed among PACTO-supported areas after 2009 (until 2018), while net forest recovery continued to decline among control areas.

After further controlling for potentially confounding differences between matched PACTO-supported and control areas using regression, we find that net forest recovery increased substantially relative to the counterfactual (Figs. 4 and 5). The difference-in-differences estimates with the matched sample show that PACTO increased net forest recovery by 10-20 percentage points on average across 2010–2018 relative to the previous period (Fig. 4 and Supplementary Table 1). The range of estimates corresponds to different definitions of treatment for the analysis units, which depend on the percent of overlap with PACTO areas. When treatment is measured as having any area under restoration in an analysis unit (a 2 ha grid cell), we find that on average PACTO converted 9.9 more percentage points of a cell’s initial non-forested area to restored forest than the counterfactual (Fig. 4a and Supplementary Table 1, column (3)). When treatment is measured by the percent of a cell treated, the measured impact is about 5 percentage points at 25% intensity, 10 percentage points at 50% intensity, 15 percentage points at 75%, and for fully covered parcels, approximately 20 percentage points (panel b of Fig. 4 and Supplementary Table 1, column (6)).

Additionally, we estimate PACTO impacts for specific years using an event-study estimation (Fig. 5, see “Methods”). Here, the estimates trace the dynamic, annual treatment effects of PACTO on net restoration over time, evaluated at the mean proportion treated (35%). We find that effects are positive and statistically significant from 2011-2018. The estimates also show no statistical difference between treatment and control units in the pre-2010 period, confirming that prior to treatment, both types of land were being managed in similar ways. It is important to keep in mind that this graph shows annual net forest recovery, so the total effect of PACTO over the 2010–2018 period is a cumulative function of the point estimates in Fig. 5.

These results are robust to several specification checks. First, our main estimation uses controls drawn from a pool within 5 km around the treated parcel, but excludes possible matches within 500 m of an enrolled grid cell. We use this strategy to identify similar parcels but to minimize possible contamination of estimates due to spillover effects. In particular, we may be concerned that a landowner who is restoring in one area might increase deforestation in another portion of their land, or that their collaboration with PACTO might result in learning or information that affects neighbors’ behavior. We examine alternate specifications where we include all possible cells in the pool of potential matches (Supplementary Table 13) and where we more restrictively exclude potential matches within 1000 m (Supplementary Table 14). We find no difference in the impact estimates across these three samples, indicating a lack of evidence for spillovers immediately adjacent to parcels where PACTO affiliates are working. All of these effects are stable under alternative fixed effect specifications and for alternative measures of forest recovery (Supplementary Tables 1, 10, 11, 12).

Using the marginal effect from the proportional treatment estimation (Supplementary Table 1, column 6), the average treatment intensity in our sample, and the amount of non-forested land in treated cells (66,851 ha), we calculate that the total restoration due to the PACTO intervention was approximately 4600 ha of forest. This level of restoration amounts to 21% of the total restorable land (21,800 ha) in our study sample of fully-treated PACTO parcels. For reasons that we discuss below, this is likely to be a lower bound on the causal effect. In addition, since our sample covers only a small part of the properties enrolled in PACTO, this is also a lower bound of the total impact of PACTO.

Impact heterogeneity consistent with key drivers of success
In light of the restoration dynamics and the key barriers identified in the interviews, we expect heterogeneous impacts of PACTO across space. One prediction is that restoration is more likely to be effective where opportunity costs are lower (see Supplementary Discussion). We test this hypothesis by adding an interaction term between the treatment effect and proximity to the nearest city (measured by 1 divided by 100 km to nearest city) in our baseline specification (Supplementary Fig. 3). This transformation of distance allows for impacts to vary depending on distance to city in a non-linear way, which may capture rapidly decreasing opportunity costs further from cities. Consistent with the hypothesis that restoration is less costly further from markets, we find that the effect of restoration is twice as high at the 95th percentile of distance (around 130 km) as it is at the 5th percentile (less than 1 km).

In addition, interviewees suggested that enforcement of the Forest Code varies by state and property size, creating heterogeneous compliance incentives across space. Landowners experiencing high enforcement will be more likely to restore. To test how the program impact varies by state, we estimate separate regressions with our matched subsample using the most restrictive set of controls (grid cell fixed effects and time-varying covariates). Figure 6 shows the impact by state using the binary treatment variable (coefficients are from Supplementary Table 2). Supplementary Table 3 provides the same estimation using the proportion treated variable. The largest treatment effects are in the states of Bahia and Paraná.

A possible explanation for the variation in the impact across states is that some states impose greater institutional pressure than others. To explore this hypothesis, we calculated the value of forest-related fines divided by the hectares of forest per rural population. This measure will be higher if forest per person tends to be smaller, capturing a mechanical relationship of more fines if there are more properties. It will also be higher if there is more enforcement, all else equal. Figure 6 provides evidence of differential enforcement of the Forest Code using data on forest-related fines from 2000 to 201829. Fines per forest ha per rural capita are highest in Bahia and Paraná, states where we observe higher restoration impacts. We see a similar trend across states for fines per capita or fines per forest ha (Supplementary Fig. 4). Fines alone do not represent the full institutional enforcement picture, as enforcement of the Forest Code is also driven by state governments through regional prosecutors of environmental law and state-level ministries of the environment. We also cannot rule out that these correlations are driven by opportunity costs, which certainly vary across states and may drive some of this pattern. However, these patterns are consistent with institutional contexts shaping the effectiveness of restoration.

Methods
Unit of analysis and data sources
For the forest recovery analysis, the landscape is divided into 2-hectare grid cells, creating a pool of uniformly-sized units. We choose 2-hectare units following the guidance of Avelino et al.36. Treatment is defined by overlap between PACTO parcels and these grid cell units (Fig. 7). Parcel data on PACTO-supported land was provided by the consortium as a geospatial shapefile and included size, location, and shape of each parcel. Data on the specific year of enrollment was not available; we therefore classify all enrolled parcels as treated after the first year of PACTO support in the region (2010 and after). The parcels include data from thirteen implementing organizations, including three companies and ten environmental NGOs.

Since parcels have different shapes, they may pass through only a portion of the square cell that is the unit of analysis, creating partially-treated cells (Fig. 7). The gridding procedure produces 42,056 treated units that have an average treatment intensity of 35%, defined by the extent of area overlap between a parcel and a cell. We use both a binary treatment variable and a continuous treatment (proportion of grid cell enrolled) as alternate measures of PACTO support in our empirical strategy.

We source land cover and forest change variables from the MapBiomas Collection 6, which provides these at a 30m spatial resolution33. The land cover data gives snapshots of land use proportions across parcels in 2000 and 2010. High-level land cover categories include Forest, Farming, Water, Non-Forest Natural Formation, and Non-Vegetated Area. The collection separately classifies natural forest and forest plantations. Forest plantations are allocated to the farming category. Change data include annual forest gain and loss. Specifically, forest losses are suppression of primary forest vegetation and suppression of secondary forest vegetation, and forest gain is recovery of secondary forest vegetation. To ensure forest change events do not reflect transition noise in remote sensing data, MapBiomas applies a per pixel classification trajectory analysis that explicitly validates forest regrowth and deforestation events. The analysis utilizes a 5-year moving leads and lags window to verify change persistence. For example, a pixel classified as forest regrowth in 2015 must have an anthropic land use classification in 2013 and 2014, followed by three years of native vegetation from 2015 to 2017, among other persistence criteria.

The restoration outcome of interest is net forest recovery, which we define using the change data as the sum across years of annual forest gain minus the sum across years of annual forest loss. In our main regression specification, we divide net forest recovery by the baseline non-forested area (as measured in the land cover data at the start of each period) in order to create a proportional measure of restoration. In addition, we consider regressions using the net forest recovery measure in hectares in robustness checks (Supplementary Tables 10, 11).

We downloaded the remote sensing data in early 2022, which meant that forest recovery variables were only available through 2018. We extract forest change within units from 2000 to 2018 in hectares of gain and loss. This is then aggregated to two nine-year periods for the main analysis, a pre-treatment period from 2000 to 2009, and a treatment period from 2010 to 2018. The data are also aggregated by year for the event study. As an additional robustness check, we verify that our main results hold with data from MapBiomas Collection 9, published in August 2024. This most recent collection uses a substantially updated classification scheme, which measures much less forest gain than previous Collections. However, the results remain similar in magnitude even when extended through 2021. The matched raw forest measures over time can be found in Supplementary Fig. 7. Compared to Collection 6, Collection 9 appears to measure less restoration generally. However, the results for the binary and continuous specification are in Supplementary Table 15, which depend on the difference between treatment and control groups, and are nearly equivalent to the main results.

We also collect data on time-invariant covariates that may influence land-use decisions for use in matching and the difference-in-differences regressions. The matching covariates include slope, elevation, land cover type (including the presence of water), bioclimate, geology, initial forest coverage, distance to nearest city, population of nearest city, protected areas, municipality, and state. Slope, elevation, land surface type, and bioclimate data come from the United States Geological Survey database (www.USGS.gov). Protected area boundaries are from the World Database of Protected Areas (WDPA), downloadable at Protected Planet (www.protectedplanet.net/country/BRA), and city data come from GISMAP. We verify that any deviations between WDPA boundaries and the boundaries from the Brazilian System of Protected Areas (SNUC) do not materially affect our methods or results. State and municipal boundaries were downloaded from the Instituto Brasileiro de Geografia e Estatística.

In addition, we compile time-varying data on temperature and precipitation, which are included in the regressions. These are extracted annually at the municipality level from the CHIRPS dataset37. Temperature and precipitation affect both the measurement of the land use outcomes and also the potential success of restoration. We use these variables in our estimation as covariates, but not in the matching analysis. Finally, although we use the data only to establish state-level patterns, we do report summary statistics from annual environmental fines data29. Reported fines are those issued by IBAMA–the Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis, which is the federal agency in Brazil responsible for enforcing environmental law – for flora-related violations affecting native vegetation. Geoprocessing was performed with Python and tools in the ArcGIS Pro v3 console. Further data processing and all statistical analyses were performed with Stata 16.

Pre-estimation matching
Because PACTO implementers target land they identify as ideal for restoration (e.g. degraded agricultural land; abandoned pasture; land owned by individuals or companies thought likely to engage in restoration maintenance), treatment is nonrandom. To account for selection into restoration, we use propensity score matching to construct a counterfactual group that is, on average, similar to the treatment group with respect to observable characteristics in the pre-treatment period. We select matches from a buffer of 0.5-5 km around all treated parcels in order to establish a potential pool of controls from the set of 2 ha units.

Matches are chosen using one-to-one probit propensity score matching with replacement. Propensity scores from probit regressions estimate the probability of treatment for each cell. Matching variables used for probit regression include the following observable characteristics: coverage area in 2000 (for forest, non-forest natural formations, farming, non-vegetated area, and water), forest change area between 2000 and 2010 (for primary vegetation loss, secondary vegetation loss, and secondary vegetation gain), protected areas designated as of 2000, elevation, and slope. Since forest restoration is just one of many land-use options for private landowners, a key identifying assumption of this approach is that control cells represent land with a similar opportunity cost of restoration, where opportunity cost is defined as the economic benefit from the next-best alternative use of the restored land. Such uses might include agriculture, pasture, or urban development. Matching covariates are thus chosen to capture factors that are likely to determine key aspects of opportunity cost, according to both prior studies and our interviews with stakeholders. In addition, to ensure matched pairs are politically and geographically similar, we restrict matching to be within municipality. We do not match on distance to city as there is little variation in this variable within municipalities. As a robustness check, we also implement coarsened exact matching38. We discuss this in greater detail in Supplementary Methods.

Our main sample excludes untreated cells within a 500 m buffer of treated cells in the set of matched controls. The purpose of this is to limit the possibility of contamination of estimates from spatial spillovers. To check robustness, we also conduct estimation on a sample that excludes cells within a 1000 m buffer (Supplementary Table 14) and one that includes matches from all possible cells within the 5 km buffer (Supplementary Table 13).

Matching produces a plausible control group according to balance of covariates and pre-treatment trends. Supplementary Table 5 shows covariate means, normalized differences and variance ratios for the groups after matching. Net recovery in the pre-treatment period is nearly identical across groups. In the treatment period, the two groups diverge. In the treatment group, about 0.155 hectares are recovered, net of loss. This is 3.8 times larger than that of the untreated group on average (see also Fig. 3). Starting forest and non-forest cover are very similar for both periods. Land characteristics and protected area overlap are also well-balanced. On average, the treated and untreated groups have similar slope, elevation, and distance to city. Overall, normalized differences between pre-treatment means show good balance after matching, as defined by a difference below |0.25|39. Supplementary Fig. 2 shows the change in normalized differences between treated and control groups before and after matching. For all matching covariates, standardized differences in means move closer to zero after matching. Supplementary Table 5 also shows that variance ratios of pre-treatment values between treated and untreated groups are all very close to one (indicating distributional similarity) and quite far from ratios that might indicate distributional “imbalance” (ratios like 2 or 0.540).

Difference-in-differences estimation with matched sample
After matching, we apply difference-in-difference regressions with cell fixed effects to estimate the average treatment effect on the treated. This combined spatial matching and difference-in-differences approach is in accordance with similar quasi-experimental causal analysis in spatial forest contexts23–26 and draws from established causal inference methods for conservation interventions41–43. It has been shown that this strategy can replicate experimental estimates44. Equation (1) represents the main estimation strategy:

R imt = \beta 0 + \beta 1 D i T t + \beta 2 T t + V imt \alpha + \lambda i + \epsilon imt (1)

where i denotes cell, m municipality, and t time period (the pre-treatment or treatment decade). The R imt outcome measures proportional forest recovery, calculated as net forest recovery in period t divided by the initial non-forested area at the beginning of period t, for unit i in municipality m. D i is a measurement of treatment (either binary for any part of a unit enrolled in restoration, or the proportion of the cell overlapping with a restoration parcel), and T t is a dummy, which equals 1 if an observation is in the treatment period. V imt includes temperature and precipitation as the time-varying controls. Cell fixed effects are given by \lambda i. Cell fixed effects eliminate the influence of characteristics that do not change over time. These include important determinants of land productivity like slope, elevation, and bioclimate. \epsilon imt denotes the residual. \beta 1 estimates the treatment effect on the treated group, \beta 2 estimates the effect of the time trend.

Standard errors are clustered at the municipality level, allowing cells to be spatially correlated within municipality and across time. We cluster standard errors because it is possible that a single landowner may manage multiple restoration parcels and PACTO implementers may work on several nearby parcels. To check this assumption, we downloaded available property boundaries for the state of Bahia. Not all PACTO parcels could be associated with a identifiable property, but for those that could, on average there were 3-4 parcels per property. Clustering accommodates the possibility of multiple parcels by allowing for correlation of the errors within the clustering unit. Therefore, clustering at the municipality level is a more conservative approach than clustering at the property level. Further, we cannot cluster at the property level since counterfactual units are not grouped by property.

In some specifications we set the fixed effect at the level of the municipality. In this case, Eq. (2) is used for estimation:

R imt = \beta 0 + \beta 1 D i T t + \beta 2 T t + \beta 3 D i + V imt \alpha + \gamma m + \epsilon imt (2)

The interpretation of \beta 1 and \beta 2 are the same as above, but now \beta 3 estimates the difference between treated and untreated groups in the pre-treatment period. In the case of municipality fixed effects (\gamma m) we also include a number of time-invariant spatial controls in the term (V imt) which in the first specification would have been absorbed by the cell fixed effect. These include elevation, slope, distance to nearest city, population of that city, land surface, bioclimate and geology categories, as well as the precipitation and temperature as above.

Stakeholder interviews
In addition to the quantitative analysis, we conducted semi-structured interviews with stakeholders engaged at different levels of the PACTO initiative (see Supplementary Discussion and Supplementary Note 1). The questions included in the interviews were driven by a theoretical framework. The theoretical framework considers the incentives that landowners face as they are choosing how to allocate their land, which are determined by the private benefits and costs of restoration combined with institutional enforcement (Supplementary Discussion). The framework and the interviews contextualize our statistical results, partly explain landowners’ restoration motivations, and help identify mechanisms for heterogeneity in treatment effects.

The purpose of the field interviews was to discuss benefits and costs of restoration with stakeholders in order to understand key barriers or facilitating conditions for restoration and to understand the potential role of PACTO in increasing restoration activity. The leadership of PACTO was both interviewed and helped researchers make contact with other restoration stakeholders. Eleven individuals were interviewed, sometimes jointly, over nine separate meetings in Bahia, Brazil between late-August and mid-September of 2023. All interviews, save one, were conducted in person. Interviewees consisted of two government stakeholders, two NGO stakeholders, one company representative, two landowner consultants, and four landowners. All interviewees are directly associated with forest restoration and PACTO (Fig. 2). Participant identities were anonymized as per IRB regulations for research involving human subjects.

Conversations were semi-structured and took place through an interpreter. They were recorded to be able to verify responses after the interview ended. Subjects included institutional information, experience with restoration, benefits and costs of restoration, legal structures related to land use, and opinions regarding barriers to restoration in Brazil. Government officials, individual producers, and large companies received different variations of the questions. The interview protocol is in Supplementary Note 1. Interview results also informed our choice of matching covariates by identifying key context-specific factors likely to facilitate or be barriers to landowner adoption of restoration projects. Finally, they led to the generation of testable hypotheses about heterogeneity in program effectiveness.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study entitled “Scaling forest restoration in the tropics: evidence from Brazil’s Atlantic Forest Restoration Pact” has performed a robust analysis of the effects of a program of incentives to promote restoration within private land in Brazil. To do that, they have worked with post-matching differences-in-differences estimation to compare the trajectories of parcels of land adhered to the program (treatment) with similar parcels non-adherent (control).\nI believe the work is original and highly relevant to the interface between Conservation Science and Policy, by evaluating and discussing the effectiveness and pathways to upscale ecological restoration, with obvious applications in the real world.\nOne of the key features of this work is having a large dataset of areas undergoing restoration and a clear intervention. They were also careful enough to support their choices of co-variables for matching by interviewing relevant stakeholders.\nOverall, the data and methodology are adequate and give support to the main conclusions and the quality of the study is satisfactory.\nI have, however, some issues that need to be addressed by the authors if this manuscript is to be published.\nThe manuscript lacks definitions of key terms that are important for a broader audience, both terms from the restoration science and from the Brazilian legislations regulating restoration within private land.\nI also think that in some cases there is a disconnection between the main text and the supplementary material. Some information from the supplementary material needs to be brought to the main text but mostly, the Supp. Mat. deserves to be more emphasized in the main text, directing the reader interested in more details.\nI did not find any data or code, so I was not able to evaluate this aspect of the work.\nMy specific comments to the text are as follows:\nMain text\nIntroduction\n1st. paragraph\nIt would be useful to have references of how much land is privately owned to support the claim that forest restoration needs to include it (line 4).\nThe study has not been introduced yet nor the restoration program. This phrase needs to be reformulated. Perharps: restoration programs can offer assistance(…)(line 9)and add some reference, for instance:\nhttps://www.sciencedirect.com/science/article/pii/S0169534722001719\nSimilar to what? To the referred program, similar among themselves in general? I also think there is something missing here", "reviewer_like": "While restoration initiatives are expanding rapidly due to....complete (ref!), there is little evidence regarding their effectiveness to ...deliver what? this need to be cleared said (line 10).\n2nd. paragraph\nBecause the article aims for a broad audience, I suggest some short definition of terms such as natural regeneration and assisted Regeneration. And why this depends on locality.\n“(…)requires private landowners to preserve natural vegetation(…)”: to preserve and restore if this required preserved area is not present. This need to be explained.\nI also suggest mentioning the more up-to-date name: Brazil's Native Vegetation Protection Law, also known as the Forest Code. And refer to the supplementary material for more details.\nAlso include a range. Just substantial is too vague.\n3rd. paragraph\nI think you can be clearer on what do you mean by opportunity costs. Land opportunity cost?\nEnforcement probabilities is also too vague. Legislation enforcement concerning mandatory forests within property...\n4rd. paragraph\n“(…) depending on the proportion of area (…)”. of which area? property? some other domain? not clear?\nI realized later this is explained in the results. But it needs clarification here to, or a reformulation to a more general statement.\nResults\n1st. paragraph\n“(…)By contrast, in the Amazon region much more land is public or in indigenous territories. (…)”. I think this is not very relevant.\n“(…)Nearly 90% (roughly 1.1 million km2) of the BAF (…) over the last 500 years(…)”. Reference?\nI think you should add something about the relevance of reversing this trends for ecosystem services, since so many people live on the BAF, and also to reduce species extinction on this biodiversity hotspot.\n3rd. paragraph\n“(…)A complication for our analysis is that land area units (grid cells) may be partially or fully supported by PACTO (Figure B2) (…)”. It would be easier why this happens with a bit more context. Something like: Because our units of analysis were 2ha in size, a complication of our analysis is…\nLegend Figure 3.\nEstimated is redundant.\nproportion of our units of analysis treated\n4th. paragraph\nExcluding control cells withing 500m of the treatment makes the effect relevant. Could it be also due to some neighbour effect where landowners not enrolled with pacto also decide to undergo restoration when they see benefits from their neighbours?\nLegend Figure 4.\nI would not use the word “plots”. It would also be interesting to explain what the time-varying controls are. Also, instead of mentioning the cell, I would say something like our unit of analysis and explicitly say its size. I would also avoid et cetera.\n5th. paragraph\nIf I understood correctly, 5000 ha is the restoration that can be attributed exclusively to the treatment effect, but there could be more areas restored than that, right? Otherwise, it’s not very clear to me why not computing the total restored areas from the images.\nDrivers of restoration success: benefits and costs to landowners and institutional context\n2nd. Paragraph\nThe benefits are perhaps not obvious so it would be good to give some examples here, like you did for the costs.\n“(…)Brazil’s economic powerhouses (agriculture, cattle, and logging)”. Is it logging or forestry?\n“(…)Since the opportunity cost of land is high (e.g., cattle farming earns an average of $100/ha/year [12]), restoration (…)”.\nThis seems like a low opportunity cost, compared to other agricultural activities. Alo, one could claim that most restoration should for that reason target pasturelands.\nActive restoration also needs to be defined.\n3rd. Paragraph\nI think this paragraph needs further development. What is the role of each of those stakeholders? Why having all of them is important? For ex. why NGOS are necessary?\nSince this is explained further down, maybe a brief explanation on the necessity of all those actors, which will be further developed on the paragraph to come. I also think that part of the reason of having diverse stakeholders is explained in the next paragraph.\n5th. paragraph\nI suggest briefly explaining what a Legal Reserve is and referring to the supplementary material for further details.\nI also I think there is something missing which is the effect of both the FC and the \"Lei da Mata Atlantica\", which makes deforestation illegal. This can be seeing as an additional risk to restoration, since the landowner will not be able to clear the land back again.\n6th. paragraph\nThe CAR requires some context for non-Brazilians and readers unfamiliar with the details of the legislation in general. Same comment as for the Legal Reserve.\nWhat kind of credit? bank loans for agricultural activities? How does this work? be more specific.\nI am not sure what you mean by water restoration.\nHeterogeneity in impacts consistent with key drivers of success\n“(1/km to city)”. I did not understand why you have considered distance as a ratio.\n2nd. Paragraph\nThis effect seems also connected with land opportunity cost. was that included in the model? By isolating the effect of o.c., the effect of the state itself would be more clear.\n3rd. Paragraph\nI wonder if fines per capta is the best way of considering the importance of fines. The number of fines probably depend more on the number of properties -- or the area of the state - than on its population, especially because most of the population is located in cities.\nFigure 6.\nThis figure made me think if the total area under restoration, as part of the PACTO or not, would also follow this pattern. Because a smaller effect could also mean that both treatment and control are more similar .e.g. are restoring areas. Can you test for that?\nAlso review the legend. The text could be improved.\nDiscussion\n1st. paragraph\n“There are two exceptions to this (…).” This is a very strong statement, consider attenuating it.\nAlso, not clear if the second exception is the Chinese program.\n2nd. paragraph\nBe specific about the resolution:\nvia 30m resolution satellite imagery, such as the ones we used for this study...\n“Restoration in later-enrolled cells will (…)”. Review tense.\n“(…) due to concerns about that the measurement error in the outcome is likely to exceed the useful information for these very small parcels.” This sentence is not very clear. It needs rephrasing.\n3rd. paragraph\nOur dataset sounds better.\n4th. paragraph\nBut how about land opportunity cost as in land value? This could be estimated spatially explicitly and probably has very different patterns in different states.\n5th. Paragraph\nThere is a very long and useful discussion in the supplementary material that deserves to be mentioned in here to direct the reader.\nMaterials\n1st. paragraph\nThe fact that multiple parcels can be managed by a single landholder needs to be added to the limitation discussion as landowner’s behaviour can affect the effectiveness.\nMethods\nIn the supplementary material, the authors mentioned that the importance for water provision is also used to choose areas for the PACTO. If this is the case, and also considering that according to the landowners water is associated with forests – what could motivate restoration -- shouldn’t a variable capturing this be included as covariable for matching?\n2nd. paragraph\n“(…) higher(…)” in altitude\n“(…) farther from cities (…)”\nIf the matching was done by municipality, how come the controls could have different municipalities ID than the treatment?\n3rd. Paragraph\nHow about the other co-variables described in 4.4? were they included in the matching or not?\nPost-matching regression\n2nd. And 3rd paragraphs\nMost of those paragraphs would be better placed in Results and Discussion sections.\nSupplementary material\nTable A. My perception is that is more usual to add the notes information together with the table title. In addition, the Note is not very clear, I suggest some rewriting: e.g. it is no clear what (1), (2) and (3) represent.\nExtended methods\n1st. paragraph\nI would say additional methods.\nBe more specific of what you mean by spatial issues.\nMatching and difference in difference estimation\n1st. paragraph\nAdd some reference on the goal of matching.\n“Clearly, the treated group has a much higher propensity for restoration going into the treatment period (…)”. I suggest something like: Even before the treatment event.\n2nd. Paragraph\nIs protected area a binary category? or some proportion? Clarify how this variable was accessed.\n3rd. Paragraph\n“(…) in the denominator of the outcome (…)”\n4th. Paragraph\n“(…) consistent with our main findings (…)”\n5th. Paragraph\n“Cell fixed effects eliminate the influence of observable fixed confounders like slope, elevation, and bioclimate is eliminated as well as that of all unobserved fixed confounders”. This sentence is confusing, consider rewriting.\n6th. Paragraph\n“Clustering also alleviates spatial autocorrelation to the extent that landscapes are vary over larger, rather than finer, areas, like municipalities such that within municipality variation of landscape features is low”. This sentence also needs rewriting.\nFigure B1\nThe classes seem more a reclassification of Mapbiomas than original classes from the database.\nTable B4.\nThe 0.25 threshold is based on what? This threshold seems very important as in the next table, both variables reflecting restoration are the ones that have the largest normalized differences between control and treatment.\nTable B8\nNot clear what are land surface categories, nor geology and bioclimate.\nTable B9\nThere is one column missing.\nSupporting policies: the Forest Code and the CAR\nSome of this information needs to be in the main text and then you can refer to this section for further info.\nHere you need to define what are Legal Reserves and APPs.\nRole of PACTO\nWhy restoration in the tropics is complex needs reference.\nRole of Local NGOs\nI think the case of the rural settlement was never mentioned before so this needs a bit more context.\nLegal Compliance and Enforcement\nSmall landowners are also dismissed of restoring LR, what can help explain their lack of awareness of restoration obligations.\n2nd. Paragraph\nBAF was not defined before in the supp. Mat.\n5th. Paragraph\nI was not aware that there was a size threshold for landowners in the CAR. Is that a fact?\nFigure F5\nIs that restoration or the treatment -- adherence to pac", "reviewer_2": "Nature Communications\nScaling forest restoration in the tropics: evidence from Brazil’s Atlantic Forest Restoration Pact\nGeneral Comments\nThis interesting and relevant research focuses on a well-known and large-scale restoration programme and network in the\nBrazilian Atlantic Forest that aims to promote forest restoration by supporting private landowners in different ways. The work\nis novel and important as it brings to light i) the role of a large-scale restoration programme that could be used as a model\naround the globe and ii) the opportunities and obstacles for these types of initiatives to have positive impacts on restoration.\nThe work shows that the PACTO was responsible for promoting net forest restoration inside its partner areas when\ncompared to control areas. The manuscript also includes an interview component, complementing the results and\ndiscussions around obstacles related to conservation in private lands.\nThe research methods are sound, being considered among the most robust methods for impact evaluation, and the work\npresents interesting results and discussions that are relevant to a wide community. Yet, there are some improvements in text\nflow, clarity of methods and connection of quantitative and qualitative analysis that should be made.\nI detail below some problems and suggestions for improvement.\n1. The text is somehow repetitive. For example, the research objectives were presented in more than one place, written in\ndifferent ways. Some parts of methods (maybe due to methods subsections) are also repetitive and sometimes confusing.\nMore will be mentioned about this below.\n2. Interviews: It is great that the authors included a qualitative analysis based on interviews to complement quantitative ones,\nand I find it a great opportunity to make the article more robust and interesting. However, several improvements need to be\nmade, such as:\na. There is no description of the questions asked in the interviews. These should be stated in the SM so the soundness of\nthis part of the analysis can be evaluated. More details about questions, and how these were applied are important for the\ntype of information that needs to be collected. b. In the article, authors mention that the interviews are for understanding the\ndrivers and mechanisms of PACTO for restoration. But it is not very clear how the quantitative analysis and these will be\nintegrated. Important to make it clear.\nc. There is no information about the interviews in the Methods part of the main article.\nd. Throughout the results, it is hard to know what information comes from the respondents, other articles, or authors\nopinions/perceptions. These need to be referenced.\ne. Results from interviews are mainly described in the SM, and are extremely interesting, adding a soul to the manuscript,\nand allowing the reader to understand the perception of actors in the field. Of course, there is a balance regarding the\namount of information you include in the main manuscript, but I think the main text would benefit immensely by including\nsome more findings from the interviews.\nf. Based on the above, I suggest the authors have a section where they can provide more details about what interviewees\nsaid and relate it to the quantitative results and relate to the context (very well presented in the SM) in the results and maybe\ndiscussion. The amount of information that should be included can be balanced with the removal of repetitive text.\n3. Introduction:\na. 1st Paragraph talks about “private benefits”. Can authors explain what it means?\nb. 2nd paragraph has a description about PACTO, but it is still unclear how it works (it will only become clear later in the\ntext). So the next paragraphs can be better understood, it would be important to include more detailed information about\nPACTO here.\nc. 3rd paragraph. At the end of it, the authors mention opportunity costs and enforcement probabilities, but this only gets\nclear later in the text (and maybe in the SM). Needs some more explanation.\n4. Results\na. 2nd paragraph could use some information that is currently in the SM to be clearer, such as more details on the choice of\nstudying the period before 2009.\nb. Figure 2: can you include in this figure the subset of the areas that were evaluated? It would be interesting to see if they\nare concentrated in one state or region.\nc. I would also suggest the design sampling method with the grid cells presented in SM should also be placed in the main\ntext.\nd. The results for 2019 are quite drastic, particularly as we are seeing data from inside the treated parcels. The authors\nhaven´t discussed anything about that, and although it might be related to the deforestation in the Bolsonaro period, it would\nbe interesting to know how this has affected the areas inside PACTO partners. It might elucidate something about how the\ncontext or current policies can influence a program’s action.\n5. Section on drivers of restoration success (Results)\na. It is not clear where the framework comes from and what it states. I suggest moving some information from the SM.\nb. As mentioned earlier, it is hard to know where the information comes from: interviews, context or literature. I suggest\nincluding more information about the interviews, if necessary, explaining some of the context as done in the SM. Otherwise,\nit is hard to make a connection to the main goal of the article and with the factors that might influence restoration.\nc. Figure 6. Are the environmental fines used in Figure 6 related to forest code compliance only? Or general state-applied\nfines for any environmental aspects?\n6. Discussion: I think it can be improved by connecting better to the findings from the interviews and context.\n7. Materials and methods\na. I suggest restructuring the methods subtopics, as they are quite repetitive and confusing. Do you need an overview\nsection? I would replace it with a figure. My suggestion for the sections is: i) data and study area, ii) forest cover data, iii)\nmatching (including covariates for matching). It is important to differentiate what was covariates for matching and for\nregression, why you chose each of them for both analyses, and why they are different (this is something that gets confused\nthroughout the text and in the SM), iv) difference in differences with details; v) interviews! There is no information about the\ninterview part, from which state they were, questions asked, etc.\nb. 1st paragraph (item 4.2) it is still not clear who own the land and who restores it. It must be stated earlier, and why the\nimportance of explaining roles in the PACTO network.\nc. 2nd paragraph (Methods). You could estimate the number of parcels in the same property using the CAR. It would be\ninteresting to see if the analysis was made with a great number of the parcels belonging to the same owner. Could this\nsomehow impact the analysis, and how?\nd. Why have you used PA coverage as a covariate? Why not exclude the PA areas from the analysis? Also, why haven´t you\nused SNUC data as it is in some cases more reliable than WCMC one? The WCMC one might include also IL, for example.\nWould it be appropriate to include them?\ne. Why a buffer of 500m? Is it enough for spillover effect?"}, "author_response": "Thank you to both reviewers for your very thoughul comments. We think that they have significantly\nimproved the paper and hope that you find this to be the case as well. We have put original comments\nin italics and our responses in unitalicized blue font.\nResponses to reviewer 1:\nThe study entitled “Scaling forest restoration in the tropics: evidence from Brazil’s Atlantic Forest\nRestoration Pact” has performed a robust analysis of the effects of a program of incentives to promote\nrestoration within private land in Brazil. To do that, they have worked with post-matching differences-in-\ndifferences estimation to compare the trajectories of parcels of land adhered to the program (treatment)\nwith similar parcels non-adherent (control).\nI believe the work is original and highly relevant to the interface between Conservation Science and\nPolicy, by evaluating and discussing the effectiveness and pathways to upscale ecological restoration,\nwith obvious applications in the real world.\nOne of the key features of this work is having a large dataset of areas undergoing restoration and a clear\nintervention. They were also careful enough to support their choices of co-variables for matching by\ninterviewing relevant stakeholders.\nOverall, the data and methodology are adequate and give support to the main conclusions and the\nquality of the study is satisfactory.\nI have, however, some issues that need to be addressed by the authors if this manuscript is to be\npublished.\nThe manuscript lacks definitions of key terms that are important for a broader audience, both terms from\nthe restoration science and from the Brazilian legislations regulating restoration within private land.\nI also think that in some cases there is a disconnection between the main text and the supplementary\nmaterial. Some information from the supplementary material needs to be brought to the main text but\nmostly, the Supp. Mat. deserves to be more emphasized in the main text, directing the reader interested\nin more details.\nI did not find any data or code, so I was not able to evaluate this aspect of the work.\nThank you very much for your thoughul and thorough review of the manuscript. We appreciate your\nevaluaon of the contribuons of the work and relevance for policy. Your comments have significantly\nimproved the quality and readability of the paper. We have sought to beter define key terms and to\nimprove the connecon between the main text and the SI by substanally eding the results secon.\nThe text has changed quite significantly as a result of your suggesons, and so we have included both a\nclean version of the final manuscript and a pdf-compare version with the original one. Regarding the\ndata and code availability, we have created a Dataverse entry for the datasets and code, and we plan to\nmake them available upon publicaon. However, we are happy to provide you access to the data and\ncode sooner should you require it for your review. We respond to each of your comments individually\nbelow.\nMain text Introduction 1st.paragraph: It would be useful to have references of how much land is privately\nowned to support the claim that forest restoration needs to include it (line 4).\n\nWe have modified this paragraph to include the following citaons that support this claim:\nMo et al (2023): Esmates the differences between current and potenal carbon stocks, globally, by\nland-use, highlighng pasture and agriculture.\nShyamsundar et al (2023): Esmates the amount of low-cost restorable land that is owned by\nsmallholders.\nSparovek et al (2019): Esmates that 44% of Brazilian land is privately owned.\nThe study has not been introduced yet nor the restoration program.\nThank you for this point. We now define PACTO in the first paragraph and in the abstract.\nThis phrase needs to be reformulated. Perharps: restoration programs can offer assistance(…)(line 9)and\nadd some reference, for instance: https://www.sciencedirect.com/science/article/pii/S0169534722001719\nWe have reformulated the statement on program assistance to reflect that programs offer landowners\nassistance when barriers are high. Further, we have added the following citaons on restoraon\nincenves and costs: Pashkevich et al (2022), Brancalion et al (2019).\nSimilar to what? To the referred program, similar among themselves in general? I also think there is\nsomething missing here"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have carefully read the new revised version of the manuscript and the authors responses to my previous comments. I\nbelieve the manuscript has been substantially improved, and the authors have satisfactorily addressed my concerns.\nHowever, one of the questions I raised on the first round of review was related to the proxy used in the manuscript to access\nland opportunity cost: distance to urban centres. I have argued that this variable could fail to capture some nuances. For\ninstance, different states having very different land opportunity costs as in land value and forgone production at the same\ndistance to an urban centre. While I am convinced by the authors responses and supplementary analysis that using a\ndifferent variable would probably not change the overall results and is not central to this work, they have also mentioned that\nthe main reason for not using revenues from agriculture or land value was the lack of data. I would like to share some\nexamples of previous works on related fields that have managed to deal with the data limitations and to produce spatial\nexplicitly layers of opportunity cost. For instance, by using land value data:\nStrassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs.\nNat Ecol Evol 3, 62–70 (2019).\nLand value data is currently available for different states and land uses from the federal government:\nhttps://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/documentos-tecnicos/vtn\nOr using expected revenues:\nNiemeyer, J., Barros, F. S. M., Silva, D. S., Crouzeilles, R. & Vale, M. M. Planning forest restoration within private land\nholdings with conservation co-benefits at the landscape scale. Science of The Total Environment 135262 (2019)\ndoi:10.1016/j.scitotenv.2019.135262.\nThose methods can guide future analysis or help with sensibility testing for the robustness of using distance to urban centres\nas a proxy.\nIn addition, I have some minor additional comments.\nFirst section of Results\nFirst session of results feels more like methods than results. I suggest incorporating to the previous session. For instance,\nthe 1st paragraph of this session could be merged with the 2nd paragraph of the introduction to justify why there is such a\ncoalition. Also, lots of the 2nd paragraph content was already said on the introduction.\nThen, I suggest a concise paragraph on the introduction right before the last paragraph with the relevant information from\nparagraphs 2 and 3 of this section summarising what was done and then starting the results with the next sessions.\nMaybe even eliminating the subtitle results and starting with:\nDrivers of restoration success: benefits and costs to landowners and institutional context\n(I leave it to the authors whether or nor they consider this last suggestion adequate).\nLegend Figure 1\nYou could include the % of area under PACTO in each state in the legend, e.g. Bahia (32% of parcels) and so on.\nDrivers of restoration success: benefits and costs to landowners and institutional context\nLines 121-123: This sentence should be reframed to sound more like a result than methods. For example: The interviews\ncontributed/gave us an understanding of the restoration environment and drivers of impact. And then be placed on the\nbeginning of the next paragraph.\nLines 127- 128: The sentence directing the reader to the Supp. Mat. would be better right after when the forest code is\nmentioned, between brackets. As it is, it breaks the fluidity of the reading.\nAlso, the following sentence “Because of these rules” is not very clear. Because of the sanctions imposed by the legislation,\nwhich include fines and …, according to (…)?\nFigure 4:\nThe legend on the left panel is partially erased.\nHeterogeneity in impacts consistent with key drivers of success\nLine 243: land or property size rather than landowner size.", "reviewer_3": "This study, “Scaling forest restoration in the tropics: evidence from Brazil’s Atlantic Forest Restoration Pact” presents\ncompelling evidence that assistance to landholders to restore forests on their lands can indeed have influence on\nlandscape-level regeneration. The large dataset is impressive and covers a critical area both for conservation and human\nlivelihoods. I commend the authors on this work and believe it will make a valuable contribution to Nature Communications.\nMy only substantial concerns are that the post-intervention time-scale is short for classification of actual “Forest restoration”\nand the 2019 cutoff date seems arbitrary at this point. I would normally want to see more interrogation of the map products to\ndetermine that they are indeed capturing reforestation satisfactorily. Here I find that the robust statistical approach provides a\nconvincing case that there is indeed a significant treatment effect in growth captured by the maps regardless of any\nmeasurement error. Nonetheless, I would suggest the authors exercise a little more caution in labeling this growth as “forest\nrestoration”. It would also be beneficial to extend the analysis a few years beyond 2019. I assume that this was the last year\navailable when the analysis was first conducted. However, MapBiomas seems to now have maps available through 2022 if\nnot 2024 at the link cited. Extending the analysis a few years would help contextualize the 2019 results – is this year indeed\nan outlier or is it part of a longer trend extending through Bolsonaro’s term? (if so, the impact would indeed largely be\nreversed). A few additional years of analysis would also help to allay concerns that the regrowth is not actually forest or\ncannot yet be perceived in the satellite images if the actual treatment started later. If it is too difficult to rerun the full analysis\nwith the more recent MapBiomas product, I suggest at least running a sub-analysis (included in the appendix) to show that\n2019 is indeed an outlier.\nMinor issues:\nLine 96: “the total area is roughly twice the land area of the District of Columbia in the United States” – I am not sure this is\nthe most helpful illustration of the size for most readers and can perhaps be dropped altogether.\nFigure 3: Note says “treated (red line) and untreated (blue line)” but legend and story have it the other way around.\nFigure 4: “dots indicate this treatment effect evaluated at 25, 50, 75, and 100% coverage of the analysis units in a restoration\nparcel”. Clarify what is meant by “coverage” here. I assumed it meant vegetative coverage until reading the methods much\nlater.\nLine 347: Change “coverage categories” to “land cover categories” for clarity.\nFigure 6: It seems that this could be presented in a single figure, with the fine value as the X axis (and state labels inserted\nwhere they fit along that axis). The only reason for not doing so that I can see is that the clean gradient shown in the top\nfigure would be muddled, but then this gradient is misleading.\nLine 370 – 375: The temperature and precipitation data are discussed here with the matching variables but are not used in\nthe matching analysis. I believe they are used as covariates in the regression analysis. This should be clarified here.\nTable B5: Post-Matching Means: Maybe specify here that Net forest recovery (ha), 2010-2019 is the dependent variable and\nthus not matched here.\nOverall, this is a very nice piece and I thoroughly enjoyed reading it."}, "author_response": "REVIEWERS' COMMENTS and responses\nReviewer #1 (Remarks to the Author):\nDear authors,\nThank you for your thoughtful revisions in response to the previous round of peer review. I have the updated version of your\nmanuscript, and I appreciate the effort you have put into addressing the comments and improving the clarity and rigour of\nyour work.\nI find the study to be well-conducted and the conclusions well-supported by the evidence provided.\nAt this stage, I do not have any further concerns, and I am pleased to recommend your manuscript for publication. I believe it\nwill make a valuable contribution.\nThank you for your additional review of the paper. We are glad you found the revisions to be in\nkeeping with your suggestions and that you found the study to be well-conducted.\nReviewer #3 (Remarks to the Author):\nI have read the revised version of “Scaling forest restoration in the tropics: evidence from Brazil’s Atlantic Forest\nRestoration Pact” and agree that the authors have addressed all of the concerns I expressed in my previous comments except for one\n(regarding Fig 6).\nI appreciate the extra work taken to evaluate the more recent MapBiomas data and think that showing this updated analysis\nadds to the value of this analysis. While I am not as optimistic as the authors about how the new trend line will extrapolate to\nsubsequent years, I understand that this is beyond the scope of this research.\nThank you for reviewing the paper and we are glad you found that we addressed all of your concerns\nexcept one.\nMy only pending concern is that Fig 6A still does not have a quantitative x axis (as seems implied by the point sequence).\nI do not understand why the x axis cannot be the fine amount -- essentially moving Sao Paolo and Minas Gerais before\nEspitito Santo and Rio de Janiero. Not doing so seems misleading (even if B provides the data for the correct interpretation --\nwhy require the reader to do this task mentally?)\nWe agree with the reviewer that there might be other ways to present this figure. Currently, the order\nof states is determined by part a of the figure. In part a, we show the state specific estimates of\nPACTO impact. The left-right order is determined by the magnitude of these point estimates. In part b,\nwe show the value of the related fines per forest hectare for each state. The states are shown using\nthe same order as in part a, which allows for an easy visual comparison between the rank orders in\npart a vs. part b. We believe it is most clear to maintain this way of showing the results, but we have\nsought to better explain the ordering by adding notes to the caption. The caption now specifically says\nthat in part a, states are ordered by the magnitude of the point estimates, and in part b, that states\nare shown in the same left-right order as part a. There is no way of displaying magnitudes on the x-\naxis, since these are associated with states, not values. The magnitudes for each state are measured\non the y-axes. We hope this added text addresses the reviewer’s concern.\nAlso, I was able to access the code at the link now and don't see the code related to the MapBiomas analysis. Can that code\nbe included?\nWe had started the code available from the already processed data. We have now included the code\nthat includes this first basic step of processing to the same Dataverse repository.\nOutside these small requests, I think this manuscript is ready for publication and will make a great contribution to Nature\nCommunications.\nThank you.\nReviewer #3 (Remarks on code availability):\nI do not have Stata, so did not review the code specifics. However, it looks to me that the code related to the MapBiomas\ndata extraction/prep is not included at the link provided. It would be ideal if this code could be included.\nThank you, yes, we have now included this code."}, {"round": 3, "reviewer_comments": {"reviewer_1": "Dear authors,\nThank you for your thoughtful revisions in response to the previous round of peer review. I have the updated version of your\nmanuscript, and I appreciate the effort you have put into addressing the comments and improving the clarity and rigour of\nyour work.\nI find the study to be well-conducted and the conclusions well-supported by the evidence provided.\nAt this stage, I do not have any further concerns, and I am pleased to recommend your manuscript for publication. I believe it\nwill make a valuable contribution.\nBest regards,", "reviewer_3": "I have read the revised version of “Scaling forest restoration in the tropics: evidence from Brazil’s Atlantic Forest Restoration\nPact” and agree that the authors have addressed all of the concerns I expressed in my previous comments except for one\n(regarding Fig 6).\nI appreciate the extra work taken to evaluate the more recent MapBiomas data and think that showing this updated analysis\nadds to the value of this analysis. While I am not as optimistic as the authors about how the new trend line will extrapolate to\nsubsequent years, I understand that this is beyond the scope of this research.\nMy only pending concern is that Fig 6A still does not have a quantitative x axis (as seems implied by the point sequence).\nI do not understand why the x axis cannot be the fine amount -- essentially moving Sao Paolo and Minas Gerais before\nEspitito Santo and Rio de Janiero. Not doing so seems misleading (even if B provides the data for the correct interpretation --\nwhy require the reader to do this task mentally?)\nAlso, I was able to access the code at the link now and don't see the code related to the MapBiomas analysis. Can that code\nbe included?\nOutside these small requests, I think this manuscript is ready for publication and will make a great contribution to Nature\nCommunications.\n(Remarks on code availability)\nI do not have Stata, so did not review the code specifics. However, it looks to me that the code related to the MapBiomas\ndata extraction/prep is not included at the link provided. It would be ideal if this code could be included."}, "author_response": ""}]

Decision letter