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Enabling large-scale forest restoration in Minas Gerais state, Brazil

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Environ. Res. Lett. 12 (2017) 044022 https://doi.org/10.1088/1748-9326/aa6658

LETTER

Enabling large-scale forest restoration in Minas Gerais state,Brazil

Felipe S M Nunes1,5, Britaldo S Soares-Filho2, Raoni Rajo3 and Frank Merry4

1 Fundao Estadual do Meio AmbienteFEAM, Gerncia de Energia e Mudanas ClimticasGEMUC. Belo Horizonte, MinasGerais, CEP 31630-900, Brazil

2 Universidade Federal de Minas GeraisUFMG, Centro de Sensoriamento RemotoCSR. Av. Antnio Carlos, 6627, BeloHorizonte, MG, CEP 31270-900, Brazil

3 Universidade Federal de Minas GeraisUFMG, Laboratrio de Gesto de Servios AmbientaisLAGESA, Av. Antnio Carlos,6627 Pampulha, Belo Horizonte, MG, Brazil

4 Conservation Strategy Fund, Washington DC, United States of America5 Author to whom any correspondence should be addressed.

E-mail: [email protected]

Keywords: passive restoration, assisted natural regeneration, Brazils Forest Code, spatial optimization model, Dinamica EGO

Supplementary material for this article is available online

AbstractLarge-scale forest restoration is a cornerstone of Brazils new Forest Code and a key element inits National Determined Contribution (NDC) to emissions reduction. But the path to this targetremains unclear due to a lack of information on its economics and implementation challenges.Here, we begin to fill this gap by developing a spatially-explicit model for Minas Gerais state thatestimates the costs and benefits of native vegetation regeneration under different restorationapproaches. Our results show that 36% (0.7 million ha) of the Forest Code debt in Minas Geraiscan be restored using only passive restoration, at a cost of US$ 175 47 million. Adding low-cost assisted natural regeneration would increase that number to 75% (1.5 million ha) at a costof US$ 776 137 million over a 20 yr period. This would result in a potential sequestration of284 MtCO2e. However, including the intensive planting methods needed to restore the remaining25% of highly degraded areasto fully solve the Forest Code debt and result in a potentialsequestration of 345 MtCO2ewould more than double the costs to US$ 1.7 0.3 billion. Ourresults emphasize the need to implement regional policies that take advantage of the naturalregeneration potential as well as prioritize the restoration of areas key to ecosystem services.

1. Introduction

Brazil has recently made two significant overlappingcommitments to reducing greenhouse gas emissionsfrom land use change. In the first, part of its revisedForest Code (FC), although granting amnesty to someprevious deforestation, has determined that anestimated 24 million hectares (Mha) of private landsmust have native vegetation restored or offsetted tosolve the FC debt past illegal deforestation (Soares-Filho et al 2016). The second, presented as part of itsNationally Determined Contribution (NDC) tomitigate climate change, establishes a target ofrestoring or reforesting 12 Mha by 2030 (Brazil2015). If even partially implemented, these commit-ments will position Brazil as a world leader in forest

2017 IOP Publishing Ltd

restoration and reforestation. However, the challengesto meet these targets, the latter an area equivalent insize to England, are significant.

Chief amongst the implementation hurdles for theshort term is a lack of economic information,including private and public costs, at a jurisdictionallevel. There are some local restoration estimatesavailable that range from US$ 700 (IIS 2015) to morethan US$ 4500 per hectare (Rodrigues et al 2009). But,since these costs may be prohibitive to most individuallandowners, the identification of low cost opportu-nities is of paramount importance to effectiveimplementation and adaptive management of climatechange commitments and policy targets. To helpovercome this hurdle, we quantify the naturalregeneration potential across the state of Minas

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Environ. Res. Lett. 12 (2017) 044022

Gerais, Brazil, providing estimates of costs for large-scale restoration of native vegetation under differentrestoration methods. Our study also estimatesenvironmental co-benefits in the form of priorityareas relevant to ecosystem services, such as carbonsequestration, water, and biodiversity.

1.1. Forest restoration methodsReforestation and forest restoration have been widelyrecognized as an important action to mitigate climatechange, enhance ecosystem services, improve foresthabitat and thus biodiversity, and sustain the live-lihoods of traditional populations (Wunscher et al2008, Birch et al 2010,Wendland et al 2010, Nunes et al2012,Locatelli et al2015,Alexander et al2016).As such,reforestation and native vegetation recovery has gainedmomentum within sustainable development andclimate change mitigation strategies (SER 2004,Stanturf et al 2014, Nunez-Mir et al 2015). Indeed,forest ecosystems may regenerate to previous foreststate once barriers to natural regeneration are removed(Holz and Placci 2005). Under suitable conditions,natural regenerationenables the self-organizingprocessof species colonization to initiate and create a recoverytrajectory (Chazdon and Uriarte 2016). Furthermore,natural regeneration is a spontaneous long-termecological process that occurs in stages, which can bemanaged or assisted to sustain local biodiversity andbiotic interactions (Chazdon 2008).

Restoration can be classified into three groups:passive, intermediate and active. Passive restoration isbased on a natural succession process, implyingminimal human intervention (Holl and Aide 2011).This approach generally involves only the isolation ofan area to allow for natural or unassisted nativevegetation regeneration. Natural regeneration isaffected by local resource availability, prior land useintensity, and dispersal of propagules (i.e. seeds andsprouts) (Rodrigues et al 2011, Pereira et al 2013,Chazdon 2014, Chazdon and Guariguata 2016). In thisrespect, abandoned pasturelands with high localresource availability near preserved forest remnantsmay be restored passively at a relatively low cost. Thepassive recovery process, however, can take place veryslowly or be inhibited in degraded agroecosystems(Brancalion et al 2016).

As an intermediate step, there are techniques thatexpedite, rather than replace, natural successionalprocesses by removing or reducing barriers to naturalregeneration also referred to as Assisted NaturalRegeneration (ANR) and may include, for example,the prevention and control of fire and invasive species(Corbin and Holl 2012, Evans et al 2015). AlthoughANR techniques may be less effective than replantingfor enhancing floristic diversity at the initial stages,they offer significant cost advantages when comparedto planting seedlings, which can make them a strategicchoice for larger scale interventions (Shono et al 2007,Bechara et al 2016). Nevertheless, they seldom work if

2

applied to deeply degraded sites or areas previouslysubmitted to intense land use, which may have alreadysurpassed an ecological threshold (Lamb et al 2005,Chazdon 2008, Chazdon 2013).

To deal with those areas, active restoration isrequired. Active restoration is generally carried outthrough interventionist practices, such as sowing andplanting seedlings, in order to set a desired restorationtrajectory (Rodrigues et al 2011). In some cases,plantations covering the entire area as well astechniques involving the planting small patches oftrees (partial planting) to serve as focal areas forrecovery have been recommended (Rodrigues et al2011, Corbin and Holl 2012, Bechara et al 2016,Brancalion et al 2016). This increased silviculturalinvestment, while suitable to recover difficult sit-uations, can affect the bottom line of the large-scaleproject. Common planting approaches utilized in theBrazilian Atlantic Forest, for example, range from US$3000 to over US$ 4500 per hectare (Rodrigues et al2009, BNDES 2015). All of these methods can becombined to vary the level of intervention according tothe site favorability, management goals, and availablefinancial resources.

Indeed, the success or failure of a restoration projectis a matter of finding the correct combination ofrestoration methods (Prach and Hobbs 2008, Clewelland McDonald 2009). In tropical areas, passive,intermediate and active methods have been proposed(IMAFLORA 2008, Cury and Carvalho 2011, TNC2013), but the cost-effectiveness of these methods canvary greatly across sites depending on the availability offinancial and human resources, degree of ecologicaldegradation, and natural regeneration potential(Rodrigues et al 2011, Rezende et al 2015). In addition,economically profitable restorationmodels basedon theexploitation of timber and non-timber forest products(Latawiec et al 2015, BIOFLORA 2015) from nativespecies have been proposed but scientific and practicalknowledge gaps remain (Silva 2013).

Despite its economic and environmental advan-tages, natural regeneration (either passive or assisted)is often neglected when reforestation and restorationpolicies are formulated. This is particularly importantbecause, if done effectively, natural regeneration couldfree up limited financial resources to be applied inareas where more costly and intensive methods areneeded (Chazdon and Guariguata 2016, Chazdon andUriarte 2016).

1.2. Opportunities for large-scale restoration inMinas GeraisOccupying approximately 7% of Brazils territory,Minas Gerais is the second most populous state, thecountrys third largest economy and the second inagricultural value product (Cepea 2015). Nevertheless,the State still holds a vast natural capital. Nativevegetation covers 17 Mha or 31% of the State (Soares-Filho et al2013a), encompassing threeBrazilian biomes,

Priority areas forecosystem services

Landscape context

distance to nativevegentationremnants

Size of fragments

Site favorability fornatural regeneration

Elevation

Landforms

Climate

Land-use history

Intensity ofprevious land use

Histogramequalization

Naturalregenerationfavorability

Land use

FC balance

Land pricesLand-use

opportunitycosts

Public costsPrivate costs

Restorationmethods

Restorationcosts

FC debt solved

Priority areasfor restoration

Carbonsequestration

Potentialvegetationbiomass

Biodiversity

Water resourcesprotection

Simulatedrestored area

Marginal abatmentcost curves

Passive..............Active

21 3

2a1a

3a2b

2c

1b

FC implementation analysis Costs and benefits

Natural regeneration potential calculation

Figure 1. Modeling flowchart highlighting the main analysis modules (dashed lines) and their steps and inputs.

Environ. Res. Lett. 12 (2017) 044022

i.e. Cerrado, Atlantic Forest, and Caatinga. Although asignificant agricultural producer, croplands shrunk inMinas Gerais by 13% between 1996 and 2006 (IBGE2006) resulting in abandoned areas that now are undervarious stages of natural regeneration.

Minas Gerais needs one of largest restorationefforts in Brazil to comply with the Forest Code.Soares-Filho et al (2016) estimate there to beapproximately 2 Mha of restoration needed in theState. These include an estimated 0.7 Mha in riparianbuffer areas and 1.3 Mha of Legal Reserve, a fraction ofthe landholding that must legally be maintained asnative vegetation. Solving the FC debt in Minas Geraisis also pivotal for the success of the National Plan forRecovering Native Vegetation (PLANAVEG), whichseeks to recover 12.5 Mha nationally in 20 yr as part ofBrazils NDC policies.

2. Methods and material

2.1. General approachWe first began by using a suite of physiographic,climate and land use data to map the naturalregeneration favorability. Favorability ranges can beinterpreted as the local level of effort needed to fosterrestoration of the native vegetation through naturalregeneration processes. The favorability map, togetherwith maps of land use, land prices and the FC balance(levels of compliance), is used as inputs for a spatialoptimization model that computes the natural

3

regeneration potential for each micro-watershed atthe 12th-order (ANA 2010). To pinpoint keyecological restoration zones, we superimposed poten-tial restoration areas on maps of priority areas forenhancing ecosystem services, including carbonsequestration (Soares-Filho et al 2016), water resour-ces protection (ANA 2013) and biodiversity (ZEEMG2006). Spatial analyses were performed using Dina-mica EGO freeware (Soares-Filho et al 2013b).

To comply with the FC, landowners must enroll inan Environmental Compliance Program (ECP), whichregulates the use of different vegetation recoverymethods ranging from passive restoration to a mix ofnative and exotic species plantations. We estimated thecosts and benefits of a range of restoration methods,including passive restoration (PASRE), an intermedi-ate method (ANR), and two active methods (PAR-PLAN and TOTPLAN) to solve the FC debt across theState. To calculate the total restoration costs, weincluded the private implementation and maintenancecosts of each restoration method and the publicgovernment budget needed to monitor and verify therestoration actions. In addition to private and publiccosts, we estimated the land-use opportunity costs asthey also represent a potential obstacle to the FCimplementation (Stickler et al 2013). We thenestimated the cost-effectiveness of each method bycomparing the achieved levels of FC compliance withcosts as well as the respective potential benefit ofcarbon sequestration. Results are presented asmarginal abatement cost curves (figure 1).

Environ. Res. Lett. 12 (2017) 044022

2.2. DataOur dataset comes from various sources (table S1available at stacks.iop.org/ERL/12/044022/mmedia).The restoration implementation and maintenancecosts were gathered through interviews with techni-cians employed by the State environmental institu-tions (table S2). Other costs, such as the average freightprice of seedlings, technical consultants (table S3), andgovernment costs, were obtained from the State RuralTechnical Assistance Agency and the State ForestService (tables S4 and S5).

2.3. Quantifying the natural regeneration potentialOur analysis begins by mapping the landscape factorsthat have been identified to facilitate passive restoration.These include: 1) the landscape context, e.g. thesurrounding land use matrix that may serve as animportant source of propagules; 2) site favorability fornatural regeneration, such as elevation, landform, andclimate; and 3) land-use history. We translated thesefactors into the following spatial variables: (1a) distanceto native vegetation remnants, (1b) size of fragments,(2a) elevation, (2b) landforms, (2c) climate, and (3a)intensity of previous land use (figure 1).

Over the landscape, sources of propagules innearby forest fragments, especially in large forestremnants, favor natural regeneration (Martins et al2014a). To estimate the local influence of thesurrounding matrix, the model calculates the Euclid-ean distance to fragments of native vegetation andthen normalizes these values into a standard range offavorability (1a). In addition, the model estimates theregion of influence for each fragment of nativevegetation based on its size, assigning all map cells toits nearest fragment (1b). We then multiplied eachfavorability value by the size of the nearest fragment.Thus, areas equidistant from fragments of nativevegetation may have different favorability of naturalregeneration due to the size of the nearest fragment.

Regarding site favorability for natural regenera-tion, differences in elevation contribute to thedispersal of propagules as it favors the local seedavailability in lower areas (2) (Martins et al 2014a).Thus, to calculate the influence of elevation, wesuperimposed a hilltop map from Soares-Filho et al(2014) on the land use map in order to identify hilltopscovered in native vegetation and then calculated thedistance to these features (2a). Next, we identifiedlandform forms that favor natural regeneration (2b).In general, concave forms and low-lying topographicareas (accumulation areas) contain higher soilmoisture and nutrients that can contribute to theestablishment of propagules (Martins et al 2014a). Inthis manner, we generated a slope map and calculateda cumulative flow map using an elevation map (NASA2015) and a flow direction map. The resulting mapindicates the cumulative flow received in a cell used topinpoint accumulation areas. The model thencategorizes ranges of favorability (see supplementary

4

materialsection 2.1). Similarly, areas with higherrainfall patterns positively influence the rate of naturalregeneration (Holl and Aide 2011, Martins et al2014a). We used a 30 yr annual average precipitationmap for determining the local influence of climate(INMET 2015).

The rate of forest recovery is affected by the level oflocal degradation, as well as prior land use intensitythrough, for example, soil quality or seed dispersal (Holland Aide 2011). To quantify the influence of land-usehistory we used the map of historical land use between1940 and 2012 from Dias et al (2016) to estimate theprevious intensity of land use (3 and 3a). The modelgenerates probability (favorability) maps of naturalregeneration potential for each factor by using ahistogram equalization approach (Gonzalez andWoods2008) (see supplementarymaterialsection2.2).Thesemapswere thenmultiplied, andonce again equalized, togenerate an integrated favorability map (1100) for thepotential of natural regeneration. As a result, our finespatial resolution approach (60 m60 m) enables theassessment of the integrated influence of key landscapefeatures on the local natural regeneration potential asindicated by ecological restoration studies and technicalmanuals for Brazilian biomes (IMAFLORA 2008,Rodrigues et al 2011, Martins et al 2014a, Martinset al 2014b, BIOFLORA 2015).

2.4. Analyzing forest restoration under the FCimplementationThe 60 m 60 m spatial resolution land cover map(figure S1) used as input for simulating restorationareas comes from Soares-Filho et al (2014). Weoverlaid this map with a land use map (Soares-Filhoet al 2016) and the FC balance map (Soares-Filho et al2014) to identify pasturelands below the FC compli-ance. The model is constrained to allocate restorationon pasturelands only, due to their low land prices incomparison with croplands (Soares-Filho et al 2016).The model also excludes future areas of agriculturalexpansion, projected for 2030 by the OTIMIZAGROmodel (Soares-Filho et al 2016), from consideration.The model then allocates the amount of restorationrequired by the FC within a micro-watershed(figure S2) selecting the appropriate restorationmethod according to the level of natural regenerationfavorability calculated previously (table 1). The set ofmethods selected constitutes an increasing gradient ofeffort to conduct a restoration project based on therange of natural regeneration potential. The practicesand techniques included per restoration method, aswell as average costs and standard deviations are listedin the supplementary material (table S2).

2.5. Calculating costs and benefitsPrivate costs were estimated per hectare for the fourrestoration methods. We included two years ofmaintenance costs beyond the initial implementationcosts, resulting in a three-year disbursement schedule

https://stacks.iop.org/ERL/12/044022/mmedia

Table 2. Restoration methods and private costs ofimplementation and maintenance.

Restoration

methods

Private costs of implementation and

maintenance per hectare (US$)

Passive restoration 639 172Assisted natural

regeneration

1230 172

Partial planting 2568 487Total planting 3631 941

Table 1. Allocation of restoration methods and their main techniques based on the range of favorability for natural regeneration.

Restoration methods Main techniques Range of favorability for natural reg. (0100)

Passive restoration Site isolation from human disturbances > 75

Assisted natural regeneration Resprout protection and control of invasive species 50 to 75

Partial planting Planting seedlings in islands (small patches) 25 to 50

Total planting Planting seedlings covering the entire area < 25

Environ. Res. Lett. 12 (2017) 044022

(table 2). We assumed that all restoration projects needspecialized technical support at a cost of 2% of thetotal value (table S3). Standard deviations arecalculated from the price ranges based on differencesin fencing options and seedling spacing per hectare.The cost of fencing also depends on the shape and sizeof a restoration parcel. We assumed that the legalreserve restoration areas are approximately square andfenced on three sides, on average, and the riparianrestoration areas are linear shape and are fenced ontwo sides, on average. The cost of fencing the legalreserve varies from US$ 811 per ha for parcels ofbetween 0 and 20 ha, and US$ 247 per ha for parcels ofmore than 20 ha, and increases linearly with the lengthof the riparian recovery.

A discount rate of 8% was used in calculating NetPresent Values (NPV) (World Bank 2010) over a 20 yrperiod required in the ECP. We projected total privatecosts under the assumption that 10% of the FCcompliance targets will be met every 2 yr, as requiredby the law. To account for verification and monitoringcosts, which must be carried out by the stategovernment, we included an additional budget forthe public effort. To estimate the public costs, weadded preliminary government costs of land useregistry validation and onsite verification (table S4) aswell as administrative costs obtained from the stateBolsa Verde Program (table S5). The costs are alsodiscounted annually. Brazilian currency was convertedto US$ using the mean exchange rate of 2015 (1 US$3.33 R$). The opportunity costs were calculated as thedifference between pastureland prices and forestedland prices (figures S3 and S4). To compose the globalbudget, we summed the private and public costs, andthen added the opportunity costs of compliance.

2.6. Prioritizing areas to enhance ecosystem servicesWe estimated the potential benefits of forest restora-tion in terms of FC compliance and carbon

5

sequestration. To do so, the model deducts the areasappropriate for each restoration method from the totalarea requiring restoration, thus calculating the potentialpercentage of compliance attained by applying each oneof the four methods. To estimate potential carbonsequestration, we laid a map of potential vegetationbiomass from Soares-Filho et al (2016) over the areasrestored under each method to quantify the carbonsequestration over a 20 yr period (figure S5). Weassumed a recovery threshold of 44% of the potentialbiomass for the20yrof restorationperiodandabiomasscarbon content of 50% (MCTI 2015).

We superimposed the map of simulated restoredareas (see supplementary materialsection 2.3) onthe map of potential vegetation biomass (figure S5),the map of areas under water stress (figure S6), and themaps of priority areas for fauna and flora protection(figures S7 and S8) to pinpoint priority restorationareas for enhancing ecosystem services.

3. Results

We estimate that approximately 30% (8 Mha) of thetotal pasturelands in Minas Gerais holds medium tohigh natural regeneration potential. Of this total, 5.7Mha are located in the Atlantic Forest, 2.2 Mha occurin the Cerrado, and 0.1 Mha in the Caatinga (figure 2).The intersection of these areas with the map of the FCbalance shows that roughly 36% (0.7 Mha) of the FCdebt could be solved using PASRE only and 75% (1.5Mha) by adding ANR (figure 3). These areas wouldrepresent 6% and 12% of the Brazils total NDCrestoration target. The remaining 25% of the FCrequirement in Minas Gerais (2% of Brazils total) islocated in regions with low natural regenerationpotential and thus need the employment of morecostly methods such as PARPLAN and TOTPLAN(figure 4).

Private costs to meet the PASRE and ANR targetswould amount to US$ 175 47 and US$ 715 135million, respectively (table 3). Although covering asmall fraction of the FC debt, the costs of PARPLANand TOTPLAN represent an additional 55% to thetotal private costs. The total private cost, for all fourmethods, to solve the FC debt in Minas Gerais isestimated at approximately US$ 1.6 0.3 billion. Ourestimates of public costs for implementing the ECP areUS$ 90 million, making the sum of private and publiccost approximately US$ 1.7 0.3 billion. It is possible,

0

State capital

40 W

42 W46 W

50 W18 S

Biomes22 S

44 W

48 W

Other land uses

High

Low

Favorability

100 200 km

Figure 2. Favorability for natural regeneration on pasturelands of Minas Gerais.

40 W

41 W

44 W

48 W

State capital

Biomes

Remaining FC debt

PASRE

PASRE plus ANR

Areas without FC debt

49 W

46 W

21 S

22 S

19 S

17 S

0 100 200 km

Figure 3. Solving the FC debt by employing PASRE and ANR. The remaining FC debt would require PARPLAN and TOTPLANmethods.

Environ. Res. Lett. 12 (2017) 044022

however, that in the absence of law enforcement land-use opportunity costs present a potentially greaterbarrier to compliance. Our results suggest that whenthe opportunity costs of compliance are included thetotal costs of compliance shoot up to US$ 4.8 1.5billion.

6

Fully solving the FC debt in Minas Gerais wouldsequester 345 86MtCO2e, but the cost per ton variesgreatly. A price of US$ 1.1 per tCO2e would cover theprivate costs where only PASRE is needed over a 20 yrperiodat this price, the mean carbon sequestrationper hectare (220 85 tCO2e ha

1) would suffice to pay

0,5 1 1,5

Potential restored area (Mha)

PASRE

ANR

PARPLAN

TOTPLAN

US$

/ ha

200

1000

2000

3000

Figure 4. Marginal abatement cost curve for restoration of native vegetation.

Table 3. Private costs of restoration, public costs, and opportunity costs of compliance in NPV.

Restoration method Potentially restored area

(thousand ha)

Private costs

(US$ Million)

Public costs

(US$ Million)

Opportunity costs

(US$ thousand/ha)

Passive restoration 715 175 47 30 1.0 1.4 0.4Assisted natural regeneration 763 540 88 31 1.0 1.6 0.6Partial planting 268 398 75 11 0.3 1.8 0.7Total planting 230 508 126 9 0.3 2.0 0.9

00

2,5

5

7,5

10

50 100 150 200 250 300 350 400

Carbon (MtCO2e)

US$

/tCO

2e)

Figure 5. Marginal abatement cost curve for carbon sequestration.

Environ. Res. Lett. 12 (2017) 044022

the marginal costs of fencing (240 US$ ha1). Incontrast, prices would need to increase to between US$8 or 10 per tCO2e to cover the costs of PARPLAN andTOTPLAN investments (figure 5).

Finally, in the terms of ecosystem services, themost relevant areas for targeting large-scale restorationare located in the south of the state along theMantiqueira ridge as well as along the Espinhao ridge

7

in central and north of the state (figure 6). Indeed, awider restoration program to meet the more ambi-tious targets of The Atlantic Forest Restoration Pact(Rodrigues et al 2011, Pinto et al 2014) could bepromoted through payments for ecosystem services(PES), such as the States Program Bolsa Verde (IEF2014). These payments should cover the land-useinvestments needed for fostering passive restoration as

48 W

49 W

22 S

19 S

17 S

46 W

40 W

41 W

44 W

21 S

State capital

Biomes

0 100 200 km

Other land uses

High

Priority areas for restoration

Low

Figure 6. Priority areas of the FC debt in Minas Gerais for large-scale restoration projects aimed to enhance ecosystem services,including carbon sequestration, water resources protection, and biodiversity conservation. Ellipses depict major areas.

Environ. Res. Lett. 12 (2017) 044022

well as land-use opportunity costs of properties abovecompliance. Such an initiative would need US$ 416 116 million to target 250 000 hectares over a 20 yrperiod. Our estimates indicate that a carbon price ofUS$ 7.5 per tCO2e would suffice to cover this budgetresulting in a potential sequestration of 55 MtCO2e.

4. Discussion and conclusion

The model developed in this study employed acombination of methods for mapping the naturalregeneration potential in Minas Gerais, whichrepresents a key issue for the implementation ofBrazils FC. While forest ecosystem models involvecomplex processes to simulate the vegetation structureand dynamics (Hurtt et al 2016), our fine spatialresolution approach enables to model the effect ofpolicy actions on the recovery of native vegetation. Asa result, our study confirms the findings of Martinset al (2014a) that areas with high to medium potentialfor passive restoration can be found at the landscapelevel. The vast area to be restored and its associatedcost variation will require different degrees ofintervention that combine passive, intermediate andactive restoration methods. Planting seedlings, themost widely, and often costly, restoration approach,may not be feasible to achieve the restoration needs inMinas Gerais. Our results reinforce the role of naturalregeneration in significantly reducing the cost of large-scale restoration (Chazdon and Guariguata 2016).Policies aimed at FC successa total of 2Mha restored

8

in Minas Geraisunder the NDC/PLANAVEGshould therefore prioritize areas with high naturalregeneration potential, which cover 1.5 Mha, acrossthe State.

There is, therefore, a need to develop anappropriate legal framework within the ECP thatrecognizes the possibility of application of a wide rangeof restoration methods according to the site suitability,thereby avoiding one size fits all solutions (Duriganet al 2010, Aronson et al 2011).

Although there are opportunities for large-scaleforest restoration via low-cost approaches, it isessential to acknowledge the many obstacles ahead.The first barriers include challenges related to large-scale governance and the lack of long-term studies forassessing costs and ecological benefits of restoration(Metzger and Brancalion 2013, Wheeler et al 2016).Furthermore, understanding how much landownersare willing to internalize the substantial opportunitycost related to forest restoration is key. Theory suggeststhat individual farmers would restore their forest if thecost of remaining non-compliant is greater than theland-use opportunity cost. However, practicalapproaches by non-profit groups, such as Alianada Terra (www.aliancadaterra.org), have demonstratedsignificant conservation investments by landownerswithout direct compensation.

As the choice of the most appropriate restorationmethod depends on a local diagnosis (Reis et al 2003,Rodrigues et al 2009, Rodrigues et al 2011), the fourrestoration methods proposed in this study should notbe seen as packages ready for restoration projects but

http://www.aliancadaterra.org

Environ. Res. Lett. 12 (2017) 044022

rather a set of restoration approaches to be customizedand even combined according to local conditions andlandscape contexts. It is also important to recognizethe caveats of the modelling approach. By defining andspatializing the influence of variables related to naturalregeneration potential, our results might underesti-mate the local impact of the historical land-use and theecosystem resilience in some areas. Therefore, localdiagnosis is still needed to accurately estimate the sitepotential for local regeneration.

In sum, our results provide policy makers with thegeographic opportunities and the magnitude of theprivate and public efforts required to foster large-scaleforest restoration inMinas Gerais. Still, enabling large-scale forest restoration in Minas Gerais also relies onadvancing the science and practice of ecologicalrestoration together with effective regional policiesaimed at the FC implementation, especially, theEnvironmental Compliance Program. And if we wantto promote restoration beyond the FC compliance,these policies should contemplate PES programs, suchas the States program Bolsa Verde. Regarding thelatter, the extendedmarket of forest certificates, namedXCRA (Soares-Filho et al 2016), potentially offers aunique opportunity to disseminate PES programsacross Brazil.

Acknowledgments

This work was supported by the Minas Gerais StateResearch Foundation (FAPEMIG), the BrazilianNational Council for Scientific and TechnologicalDevelopment (CNPq), and Climate and Land UseAlliance. Felipe Nunes is supported by FAPEMIG.Raoni Rajo receives support from NORAD/IPAM,FAPEMIG and CNPq. Britaldo Soares-Filho issupported by the Humboldt Foundation and CNPQ.

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Enabling large-scale forest restoration in Minas Gerais state, Brazil1. Introduction1.1. Forest restoration methods1.2. Opportunities for large-scale restoration in Minas Gerais

2. Methods and material2.1. General approach2.2. Data2.3. Quantifying the natural regeneration potential2.4. Analyzing forest restoration under the FC implementation2.5. Calculating costs and benefits2.6. Prioritizing areas to enhance ecosystem services

3. Results4. Discussion and conclusionAcknowledgementsReferences