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Estimating the Value of Near-real-time Satellite Information for

Monitoring Deforestation in the Brazilian Amazon

Katrina Mullan1, Thales A. P. West2, Jill Caviglia-Harris3, Erin Sills4, Thaís Ottoni Santiago1,

Jime Rodrigues Ribeiro4, Trent Biggs5

Affiliations:

1 Department of Economics, University of Montana

2 Environmental Geography Group, Institute for Environmental Studies (IVM), Vrije

University Amsterdam

3 Economics and Finance Department, Environmental Studies Department, Salisbury

University

4 Department of Forestry and Environmental Resources, North Carolina State University

5 Department of Geography, San Diego State University

Abstract

We estimate the amount of avoided deforestation due to the use of near-real-time satellite

imagery (DETER) to support the Action Plan for the Prevention and Control of Deforestation

in the Legal Amazon (PPCDAm), the conservation of indigenous and other protected areas,

and compliance with the Brazilian Forest Code (FC). We develop a Directed Acyclical Graph

(DAG) that outlines some of the econometric challenges that arise from the role of policy in

the estimation of satellite data on deforestation and consider that policy could be a mediator

and/or a moderator along this causal chain. We control for policies that were introduced

simultaneously with DETER, and allow for changes in the influences of prices, agricultural

settlement, and forest conservation policies on deforestation after near-real-time monitoring

was introduced. We find both direct impacts of DETER on deforestation, and indirect impacts

via changes in the influences of commodity prices on deforestation. In total we estimate the

amount of avoided deforestation is approximately 467-471 thousand km2 between 2001-2015,

an area that is larger than the state of California, more than twice the amount of deforestation

recorded in that region in the same time period, and translates to approximately 12 billion

tons of avoided CO2. The net benefits of satellite monitoring range from US$1-5.4 billion per

year when estimated using the WTP to preserve Amazon rainforest and between US$54

US$197 billion per year when estimating using the social cost of carbon.

Keywords

value of information; satellite monitoring; mediators; moderators; directed acyclical graph

(DAG); deforestation; Brazilian Amazon

1. Introduction

Remotely sensed data provides enormous societal benefits when mobilized to address global

issues such as climate change, natural disasters, and disease outbreak; national challenges

related to land cover and land use change; and regional emergencies such as dangerously

impaired air quality (Kansakar and Hossain 2016). However, the value of satellite data is

largely invisible because the benefits are not communicated to the general public, the data are

used by many private and public agencies and governments in unknown ways, and because

the quantification of these non-market benefits is challenging. Remote sensing data from

satellites have been used to monitor deforestation in the Brazilian Amazon beginning in 1988

with the launch of the PRODES (Portuguese acronym) monitoring system, which publishes

annual deforestation rates for use by policy makers, government agencies, and the broader

public. These data have been crucial to the enforcement of environmental policy because the

Earth's largest contiguous tropical rainforest is too vast to otherwise monitor. The Brazilian

Amazon is home to a third of the world’s rainforests (FAO 2011), contains one of the most

biologically diverse biomes, (Dirzo and Raven 2003; Mittermeier et al. 2003) and significantly

influences global climate (Cao and Woodward 1998; Foley et al. 2007; D. C. Nepstad et al.

2008). Approximately 47% of the existing native forests in this region are protected within

conservation units and indigenous territories. The remainder of the Brazilian Amazon (with

the exception of a few contested public land areas) includes private properties that fall under

the protection of the Forest Code (FC), the central piece of legislation designed to protect the

public good aspects of forests (Sparovek et al. 2010).

The DETER satellite system, launched in 2004 as part of the Action Plan for the Prevention

and Control of Deforestation in the Legal Amazon (PPCDAm), changed the deforestation

policy landscape in Brazil. The DETER system enables near real-time detection of

deforestation and has served as an effective way to monitor ongoing land cover change

(Hargrave and Kis-Katos 2013). The system is used to send daily DETER alerts to the

enforcement agency Brazilian Institute for the Environment and Renewable Natural

Resources (IBAMA; Portuguese acronym) and state environmental agencies for planning

inspection actions to make sure that private properties are in compliance with the FC and

public lands are not encroached upon. These data are also used to implement policies like the

Priority List (a public listing of municipalities with high levels of deforestation defined using

PRODES data) and to target ground inspections that can result in environmental fines.

Large scale deforestation of the Brazilian Amazon began in the mid-1960s with government

settlement programs, large infrastructure projects, and investment in industrial agriculture

(Andersen 2002; Barreto, Pereira, and Arima 2008). This time period saw one of the most

extensive frontier colonization programs to occur in the past century, settling over one million

individuals in the Amazon with oversight from the National Institute of Colonization and

Agrarian Reform (INCRA; Portuguese acronym). By the 1980s, these programs resulted in a

substantial change to land cover dynamics in the region. Deforestation became closely linked

to market forces, increasing at historically high rates in the 1980s and 1990s, specifically with

the expansion of cattle ranching and soybean operations. Reductions in deforestation rates

after 2004 have been explained by a series of policy responses (including the use of satellite

imagery for monitoring and enforcement) that are believed to have effectively decoupled

potential agricultural revenue from deforestation (Caviglia-Harris et al. 2016; Nepstad et al.

2014). These policies at least partially contributed to the more than 70% reduction in

deforestation between 2004-2012 (T. A. P. West and Fearnside 2021; INPE 2021); one estimate

is that they reduced deforestation by approximately 47% below what would have otherwise

occurred (Busch and Engelmann 2017). Existing evidence suggests that the satellite-based

system alone has had an important impact. Assunção et al. (2019) estimate that deforestation

would have been four times greater between 2006-2016 in the absence of the system.

This study estimates the amount of deforestation that would have occurred (i.e. counterfactual

deforestation) if the near real-time satellite monitoring system (DETER) was not launched in

2004 and the amount of carbon that would have been released into the atmosphere had the

deforestation occurred. We begin with the development of a Directed Acyclical Graph (DAG)

tracing the causal pathway from satellite data (the treatment) to deforestation (the outcome)

as is informed by interviews with public officials and desk review of policy documents. We

consider that policy could be a mediator and/or a moderator along this causal chain. We then

outline our methods, specifically addressing econometric challenges including a lack of

spatial and temporal variation in the use of real-time satellites at our unit of analysis (i.e.,

municipalities), selection bias, and the implementation of multiple policies that rely on the use

of satellite data, in different periods and with strong selection bias. Our estimations include

the impacts of macroeconomic influences (such as commodity prices), pro-development

policies and programs (such as INCRA settlements), and pro-conservation policies (such as

environmental fines and supply chain initiatives) and quantify the value of satellite imagery

as the value of carbon stored due to avoided deforestation.

2. Policy context

The Brazilian government began monitoring deforestation in the Amazon forest largely using

Landsat satellite data through the PRODES program (monitoring of Brazilian Amazon

deforestation project) in 1988 (INPE 2021), in response to international pressure to reduce

deforestation resulting from the government incentivized settlement of the region (Achard

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EstimatingtheValueofNear-real-timeSatelliteInformationforMonitoringDeforestationintheBrazilianAmazonKatrinaMullan1,ThalesA.P.West2,JillCaviglia-Harris3,ErinSills4,ThaísOttoniSantiago1,JimeRodriguesRibeiro4,TrentBiggs5Affiliations:1DepartmentofEconomics,UniversityofMontana2EnvironmentalGeographyGroup,InstituteforEnvironmentalStudies(IVM),VrijeUniversityAmsterdam3EconomicsandFinanceDepartment,EnvironmentalStudiesDepartment,SalisburyUniversity4DepartmentofForestryandEnvironmentalResources,NorthCarolinaStateUniversity5DepartmentofGeography,SanDiegoStateUniversityAbstractWeestimatetheamountofavoideddeforestationduetotheuseofnear-real-timesatelliteimagery(DETER)tosupporttheActionPlanforthePreventionandControlofDeforestationintheLegalAmazon(PPCDAm),theconservationofindigenousandotherprotectedareas,andcompliancewiththeBrazilianForestCode(FC).WedevelopaDirectedAcyclicalGraph(DAG)thatoutlinessomeoftheeconometricchallengesthatarisefromtheroleofpolicyintheestimationofsatellitedataondeforestationandconsiderthatpolicycouldbeamediatorand/oramoderatoralongthiscausalchain.WecontrolforpoliciesthatwereintroducedsimultaneouslywithDETER,andallowforchangesintheinfluencesofprices,agriculturalsettlement,andforestconservationpoliciesondeforestationafternear-real-timemonitoringwasintroduced.WefindbothdirectimpactsofDETERondeforestation,andindirectimpactsviachangesintheinfluencesofcommoditypricesondeforestation.Intotalweestimatetheamountofavoideddeforestationisapproximately467-471thousandkm2between2001-2015,anareathatislargerthanthestateofCalifornia,morethantwicetheamountofdeforestationrecordedinthatregioninthesametimeperiod,andtranslatestoapproximately12billiontonsofavoidedCO2.ThenetbenefitsofsatellitemonitoringrangefromUS$1-5.4billionperyearwhenestimatedusingtheWTPtopreserveAmazonrainforestandbetweenUS$54US$197billionperyearwhenestimatingusingthesocialcostofcarbon.Keywordsvalueofinformation;satellitemonitoring;mediators;moderators;directedacyclicalgraph(DAG);deforestation;BrazilianAmazon1.IntroductionRemotelysenseddataprovidesenormoussocietalbenefitswhenmobilizedtoaddressglobalissuessuchasclimatechange,naturaldisasters,anddiseaseoutbreak;nationalchallengesrelatedtolandcoverandlandusechange;andregionalemergenciessuchasdangerouslyimpairedairquality(KansakarandHossain2016).However,thevalueofsatellitedataislargelyinvisiblebecausethebenefitsarenotcommunicatedtothegeneralpublic,thedataareusedbymanyprivateandpublicagenciesandgovernmentsinunknownways,andbecausethequantificationofthesenon-marketbenefitsischallenging.RemotesensingdatafromsatelliteshavebeenusedtomonitordeforestationintheBrazilianAmazonbeginningin1988withthelaunchofthePRODES(Portugueseacronym)monitoringsystem,whichpublishesannualdeforestationratesforusebypolicymakers,governmentagencies,andthebroaderpublic.ThesedatahavebeencrucialtotheenforcementofenvironmentalpolicybecausetheEarth'slargestcontiguoustropicalrainforestistoovasttootherwisemonitor.TheBrazilianAmazonishometoathirdoftheworld’srainforests(FAO2011),containsoneofthemostbiologicallydiversebiomes,(DirzoandRaven2003;Mittermeieretal.2003)andsignificantlyinfluencesglobalclimate(CaoandWoodward1998;Foleyetal.2007;D.C.Nepstadetal.2008).Approximately47%oftheexistingnativeforestsinthisregionareprotectedwithinconservationunitsandindigenousterritories.TheremainderoftheBrazilianAmazon(withtheexceptionofafewcontestedpubliclandareas)includesprivatepropertiesthatfallundertheprotectionoftheForestCode(FC),thecentralpieceoflegislationdesignedtoprotectthepublicgoodaspectsofforests(Sparoveketal.2010).TheDETERsatellitesystem,launchedin2004aspartoftheActionPlanforthePreventionandControlofDeforestationintheLegalAmazon(PPCDAm),changedthedeforestationpolicylandscapeinBrazil.TheDETERsystemenablesnearreal-timedetectionofdeforestationandhasservedasaneffectivewaytomonitorongoinglandcoverchange(HargraveandKis-Katos2013).ThesystemisusedtosenddailyDETERalertstotheenforcementagencyBrazilianInstitutefortheEnvironmentandRenewableNaturalResources(IBAMA;Portugueseacronym)andstateenvironmentalagenciesforplanninginspectionactionstomakesurethatprivatepropertiesareincompliancewiththeFCandpubliclandsarenotencroachedupon.ThesedataarealsousedtoimplementpolicieslikethePriorityList(apubliclistingofmunicipalitieswithhighlevelsofdeforestationdefinedusingPRODESdata)andtotargetgroundinspectionsthatcanresultinenvironmentalfines.LargescaledeforestationoftheBrazilianAmazonbeganinthemid-1960swithgovernmentsettlementprograms,largeinfrastructureprojects,andinvestmentinindustrialagriculture(Andersen2002;Barreto,Pereira,andArima2008).Thistimeperiodsawoneofthemostextensivefrontiercolonizationprogramstooccurinthepastcentury,settlingoveronemillionindividualsintheAmazonwithoversightfromtheNationalInstituteofColonizationandAgrarianReform(INCRA;Portugueseacronym).Bythe1980s,theseprogramsresultedinasubstantialchangetolandcoverdynamicsintheregion.Deforestationbecamecloselylinkedtomarketforces,increasingathistoricallyhighratesinthe1980sand1990s,specificallywiththeexpansionofcattleranchingandsoybeanoperations.Reductionsindeforestationratesafter2004havebeenexplainedbyaseriesofpolicyresponses(includingtheuseofsatelliteimageryformonitoringandenforcement)thatarebelievedtohaveeffectivelydecoupledpotentialagriculturalrevenuefromdeforestation(Caviglia-Harrisetal.2016;Nepstadetal.2014).Thesepoliciesatleastpartiallycontributedtothemorethan70%reductionindeforestationbetween2004-2012(T.A.P.WestandFearnside2021;INPE2021);oneestimateisthattheyreduceddeforestationbyapproximately47%belowwhatwouldhaveotherwiseoccurred(BuschandEngelmann2017).Existingevidencesuggeststhatthesatellite-basedsystemalonehashadanimportantimpact.Assunçãoetal.(2019)estimatethatdeforestationwouldhavebeenfourtimesgreaterbetween2006-2016intheabsenceofthesystem.Thisstudyestimatestheamountofdeforestationthatwouldhaveoccurred(i.e.counterfactualdeforestation)ifthenearreal-timesatellitemonitoringsystem(DETER)wasnotlaunchedin2004andtheamountofcarbonthatwouldhavebeenreleasedintotheatmospherehadthedeforestationoccurred.WebeginwiththedevelopmentofaDirectedAcyclicalGraph(DAG)tracingthecausalpathwayfromsatellitedata(thetreatment)todeforestation(theoutcome)asisinformedbyinterviewswithpublicofficialsanddeskreviewofpolicydocuments.Weconsiderthatpolicycouldbeamediatorand/oramoderatoralongthiscausalchain.Wethenoutlineourmethods,specificallyaddressingeconometricchallengesincludingalackofspatialandtemporalvariationintheuseofreal-timesatellitesatourunitofanalysis(i.e.,municipalities),selectionbias,andtheimplementationofmultiplepoliciesthatrelyontheuseofsatellitedata,indifferentperiodsandwithstrongselectionbias.Ourestimationsincludetheimpactsofmacroeconomicinfluences(suchascommodityprices),pro-developmentpoliciesandprograms(suchasINCRAsettlements),andpro-conservationpolicies(suchasenvironmentalfinesandsupplychaininitiatives)andquantifythevalueofsatelliteimageryasthevalueofcarbonstoredduetoavoideddeforestation.2.PolicycontextTheBraziliangovernmentbeganmonitoringdeforestationintheAmazonforestlargelyusingLandsatsatellitedatathroughthePRODESprogram(monitoringofBrazilianAmazondeforestationproject)in1988(INPE2021),inresponsetointernationalpressuretoreducedeforestationresultingfromthegovernmentincentivizedsettlementoftheregion(AchardandHansen2012b;2012a).Thismonitoringsystemrecordedmorethan500,000km2ofdeforestationthrough2020(INPE2021),revealinghistoricalcorrelationsbetweenannualdeforestationratesandpoliticalandeconomicchanges(Margulis2003;HargraveandKis-Katos2013;Assunçãoetal.2013).Despitetheuseofsatellitestomonitordeforestationbeginningin1988,annualdeforestationremainedrelativelyhighthroughthefollowingdecade(AchardandHansen2012a)inpartduetotherelativelylonglagbetweenthetimetheimagesweretakenandanalyzed,andinformationwasmadeavailabletothegovernmentandotherenvironmentalenforcementagencies.Inresponse,anewmonitoringsystembasedonMODISimages,DETER,wasdevelopedin2004forrapiddetectionofdeforestationpatchesgreaterthan25hectares(Shimabukuroetal.2006).DETER’smainobjectivewastoprovidedeforestationalertstoenforcementagenciesonpotentiallyillegalforest-clearingactivitiesintheAmazon,whichcouldthenbeusedtosupporton-the-groundactions(AchardandHansen2012b;INPE2021).DETERissuedmorethan70,000alertsfrom2004to2017,coveringanareaof~88,000km2ofdeforestation(INPE2021).TheDETERsystemhassincesupportedtheFederalGovernment’sPPCDAm;AchardandHansen,2012)andhasbeenattributedwithhelpingtoslowillegaldeforestationintheAmazon(HargraveandKis-Katos2013).Thepost-2004slowdownofdeforestationintheBrazilianAmazonthatwearemodelingcanbeexplainedbyaseriesofresponsestoeffortsdesignedtomeetdifferent,andoftenconflicting,policyobjectives,manyofwhichhavebeensupportedbysatellitedata(Figure1).Brazil’sinterventionstoslowAmazoniandeforestationhavebeencreditedwithsuccessfullydecouplingpotentialagriculturalmarketsfromdeforestation(Caviglia-Harrisetal.2016;Assunção,Gandour,andRocha2015;Nepstadetal.2014),thuscontributingtotheapproximately75%reductioninthedeforestationratebetween2004and2012(WestandFearnside,2021;INPE,2021).ThroughoutthefourphasesofPPCDAmsupportedbyDETERtherewasasignificantexpansionoftheprotectedareasnetwork(Jusys2018;Pfaffetal.2015;Barberetal.2014;Nolteetal.2013),thecreationofamonitoringprioritylistofmunicipalitieswithillegaldeforestation(Cisnerosetal.2013;Arimaetal.2014a;AssunçãoandRocha2014),restrictionsonpubliccreditaccessforillegaldeforesters(Assunçãoetal.2013),andthe2012revisionstotheForestCode(Soares-Filhoetal.2014).Thisseriesofpolicieswasaccompaniedbyprivatesupplychainactionsincludingthe“SoyMoratorium”of2006,apledgefromtheBrazilianAssociationofVegetableOilsIndustriesandtheNationalAssociationofCerealExporterstobansoybeansproducedindeforestedareasinAmazoniaafter2008(Fearnside2017;Gibbs,Munger,etal.2015),andthe“CattleMoratorium”of2009,apledgefromthelargestmeatpackingcompaniesinthecountrynottopurchasebeefproductsfromfarmslinkedtoillegaldeforestation(WestandFearnside2021;Gibbs,Munger,etal.2015).Figure2.1:OutlineofMajorDeforestationandDevelopmentPoliciesImpactingtheBrazilianAmazon2.1PrioritylistofmunicipalitiesInJanuary2008,Brazil’sfederalgovernmentannouncedaprioritylistofAmazonianmunicipalitieswithrisingdeforestationrates(WestandFearnside2021).Theoriginallist,fromDecree6321of2007,included36municipalities(43afterMarch2008)thattogetherrepresented46%ofallAmazoniandeforestation.“Blacklisted”municipalitiesweresubjectedtomoreintenseenvironmentalsurveillance,restrictionsontheissuanceof(legal)deforestationpermits,embargoesofillegallyclearedareas,andlimitedaccesstoruralcredit(Fearnside2017).StudiesassociatethePriorityListwithsignificantreductionsindeforestation(Arimaetal.2014a);inparticular,Cisnerosetal.(2015)estimatedthepolicytohavereducedtheexpected2008–2012forestlossby13%–36%.2.2RevisiontotheBrazilianForestCodeTheBrazilianForestCode(FC)wasrevisedin2012toincludemoreflexiblerulesfortherestorationofconservationareasonprivatelots(i.e.,PermanentPreservationAreasandLegalReserves)andgrantedamnestytoareasillegallyclearedpriorto2008.Aresultisthatthe2012FCreducedBrazil’s“environmentaldebt”(i.e.,areasillegallyclearedbefore2008andthatwouldhavetoberestoredaccordingtothepreviousversionofthecode)by58%(WestandFearnside2021;Soares-Filhoetal.2014).Atthesametime,thenewcodeenabledmechanismstomoreefficientlymonitortheenvironmentalcomplianceandforestrestorationrequiredforallruralpropertiesinthecountry(Azevedoetal.2017),andauthorizedthecreationofanenvironmentalmarkettotrade“forestcertificates”thatcanbeusedtooffsetlandowners’restorationrequirements(Soares-Filhoetal.2016).However,afullyfunctioningmarkethasnotyetbeenestablishedforthesecredits.CompliancewiththenewcodecannowbecheckedagainstthenationalRuralEnvironmentalRegistry(CAR;Brazilianacronym),aself-reported,spatially-explicitregistrationsystem.2.3Supply-chaininterventionsInitiativestoreducedeforestationfromagriculturalsupplychainswereledbyprivatesectoractorsandNGOsbeginningintheearly2000s(Lambinetal.2018;Nepstadetal.2014).In2006,soybeantradingcompaniespledgedtolimitgrainpurchasesfromfarmswithpost-2008deforestation.Thisagreementisknownasthe“SoyMoratorium”(Gibbs,Rausch,etal.2015).Then,in2009,GreenpeacenegotiationswiththesupportoftheFederalPublicMinistry(MPF;Brazilianacronym)resultedinagreementswithmeatpackingcompaniestopurchasecattleexclusivelyfromdeforestation-freeranchers(Arimaetal.2014a;Klingler,Richards,andOssner2018),inwhatwerefertoasthe“CattleMoratorium.”Still,despitehighexpectationsfortheeffectivenessofthesesupply-chaincommitments,studiesfoundthattheBrazilianmoratoriahadsmall(Heilmayretal.2020)topotentiallynoimpactsondeforestation(Klingler,Richards,andOssner2018;Alix-GarciaandGibbs2017;SvahnandBrunner2018;Macedoetal.2012).Inparticular,thesoymoratoriumhasbeenlinkedtoaprocessof“indirectland-usechange,”assoybeanfieldsstartedreplacingpasturelandmoreintensively,leadingtoanincreaseintheratesofcattle-drivendeforestationinAmazonianagriculturalfrontiers(Arimaetal.2011;Baronaetal.2010;Richards,Walker,andArima2014).Similarly,otherstudieshaveidentifiedloopholesinthemonitoringofcattlesupplychainsallowingfarmswithillegaldeforestationtosellcattleindirectlytoslaughterhousessignatorytozero-deforestationcommitments(Klingleretal2018,Westetal2022).2.4LandtenurepoliciesLandtenurepolicies,suchastheexpansionoftheprotectedarea(PA)networkandtheofficialrecognitionofindigenouslands(ILs)areakeycomponentofthePPCDAm(WestandFearnside2021).Amongthetargetssetbythesepolicieswasthecreationof50millionhaofPAsaspartoftheProtectedAreasProgram(ARPA;Portugueseacronym;establishedbyDecree4326of2002).Pfaffetal.(2015)foundPAsintheBrazilianAmazontohavereduceddeforestationby~2%incomparisontocounterfactualsduring2000–2008,whereasasimilaranalysisbyJusys(2018)founddecliningavoideddeforestationinPAs,withthegreatestconservationgainsoccurringbetween2001–2004andgradualreductionsinavoideddeforestationthrough2005–2014.WhilePAsaregenerallyassociatedwithpositive,butmoderate,conservationoutcomes(Miteva,Pattanayak,andFerraro2012),manyAmazonianPAscontinuetoexperienceforestlossandfragmentation,oftenduetopressurefromcattleranchingactivitiesandillegalforestfires(Cabraletal.2018).LandtenurepolicieshavealsobeeninfluencedbytheestablishmentofruralsettlementsestablishedbyINCRA(NationalAgencyforLandReform)thatbeganinthe1970sand1980swithasupportingroadandhighwaynetworkbuilttoconnectthenortheasternandsoutheasternedgesoftheAmazonwiththeportsandmarketsinSãoPaulo,Brasília,andBelém(Barberetal.2014;NelsonandHellerstein1997).INCRAsettlementsarestronglyassociatedwithdeforestation(BrandãoJr.,Barreto,andSouzaJr.2012)includinginnearbyprotectedareas(Jusys2018;Oldekopetal.2016;Nolteetal.2013).Yanaietal.(2017)estimatedalossof41%oftheoriginalvegetationintheseINCRAsettlementsestablishedthroughouttheBrazilianAmazon.2.5MacroeconomicInfluencesDeforestationisalsoinfluencedbymacroeconomictrendsincludingagriculturalcommoditypricesandcurrencyexchangerates(Arcand,Guillaumont,andJeanneney2008;HargraveandKis-Katos2013).Assunçãoetal.(2015)estimatedthatnearlyhalfoftheavoidedAmazoniandeforestationduring2005–2009wasduetoless-favorableeconomicconditionsforagriculturalexpansioninsteadoftheconservationpoliciesinBrazil.Arimaetal.(2014b)finddeforestationtobepositivelycorrelatedwithsoybeanpricesduring2008–2011,buttobenegativelycorrelatedtoagriculturalGDPanduncorrelatedwithcattleprices.Inaddition,FariaandAlmeida(2016)arguethatincreasedopennesstotradealsoincreaseddeforestationintheAmazonbetween2000and2010.Ontheotherhand,therelationshipbetweenper-capitagrossdomesticproduct(GDP)andforestlossdependsonnationalandregionalcontexts.Severalstudiessuggestthemarginalimpactofper-capitaGDPondeforestationispositiveforlowlevelsofincome,buttobecomenegativepastagivenincomethresholdlevel(Arcand,Guillaumont,andJeanneney2008;BarbierandBurgess2001;Köthke,Leischner,andElsasser2013).3.TheCausalChainfromSatellitestoForestCoverSatellitedatadonotimpactdeforestationratesdirectly.Policymustbeinplacetoincentivizetheuseofthedatatoaspecificend,buthowpolicyentersintothecausalrelationshipfromsatellitestoforestcoverisunclear.Tounderstandthisroleofpolicy,weinterviewedpublicagents(governmentofficialsandrepresentativesofcivilsocietyorganizations)andconductedadeskreview(oflaws,regulations,andgovernmentwebsites).Wereviewedaround60specificenforcementenvironmentallaws,accountingover500pagesofpublicdocumentation.Inaddition,weconductedinterviewswith8policymakers(majorycareercivilservants)fromdifferentNorthernStates(ParaandAmazonas)andtheFederalDistrict(Brasilia),includingFederalEnvironmentalAgencies(IBAMAandMMA),StateEnvironemtalAgency(SEMAS),PublicCompanythatleadswithCAR(EMATER),plusconsultantsandNGOrepresentant.Theseinterviewswereinitiallyguidedbyopenendedquestionswith5numberoffollowups.Asecondroundofinterviewswereconductedwithsemi-structuredquestionstosupporttheconstructionofourcausalchainpicture(Figure3.1).Causalchains,orboxandarrowdiagramsthatdisplayalogicalandordersequenceofeffects,areusedacrossdisciplinesandpolicydomainstovisualizesuchcausalrelationships(Qiuetal.2018).DirectedAcyclicGraphs(DAG)areatypeofcausalchainparticularlyhelpfulforvisualizingtheassumptionsthatunderliemodelspecificationandidentificationofeffects(SillsandJones2018).Thesediagramsincludethreeelements:(1)representationsofdependentandindependentvariables(wordsorabbreviationsinboxes),(2)arrowsindicatingthedirectionofcausaleffects,and(3)paths,(thecombinationofvariablesandarrowsthatlinkthetreatment,ortheindependentvariabletotheoutcome,thedependentvariable)(ElwertandWinship2014).Thesediagramsareparsimonious,focusingonthemostimportantelementsofthecausalpath(Huntington-Klein2022).WeuseaDAGtorepresentinsightsobtainedfromopen-endedinterviewswithpublicagents(representativesofBraziliangovernmentagenciesandcivilsocietyorganizations)anddistilltheirimplicationsforourempiricalanalysis.OurreducedformDAGcanbeexpressedasfollows,wheresatellitedataisthetreatmentanddeforestationistheoutcome:Figure3.1.ReducedDAGWeexpanduponthisreducedmodelbyconsideringtheroleofmediators(mechanismsthatlieonthecausalchainbetweentreatmentandoutcome)andmoderators(factorsthatlieoffthecausalchainbutinfluencethelevel,direction,orpresenceofarelationshipbetweentreatmentandoutcome)inthisrelationship(VanderWeele2009).Mediators1lieonthecausalpathway,thatis,theyarecausedbythetreatmentandtheyhaveacausaleffectontheoutcome.Moderators,ontheotherhand,arecontextualfactorsnotinfluencedbysatellitedata.Unlikeconfounders(variablesthatdirectlyinfluencebothassignmentoftreatmentandtheoutcome),moderatorscanbeomittedfromempiricalmodelswithoutbiasingtheestimate.However,theyareusefultoincludebecausetheyshowforwhom,when,orunderwhatcircumstancesarelationshipwillhold.Moderatorsarealsorelevanttotheexternalvalidityofastudybecausetheyidentifytheconditionsunderwhichacausalrelationshipholds.Whenthereisnovariationinamoderatorinthestudysite,itisstillimportanttorecognizeitasapotentialnecessarycondition.Ontheotherhand,mediatorsshouldonlybeincludedin“causalmediationanalysis”(Keele,Tingley,andYamamoto2015)andnotwhenthegoalistoestimatethetotaleffectofatreatmentonanoutcome.2Thus,theassumptionsvisualizedintheDAGhavedirectimplicationsfortheempiricalanalysis,andinturn,theestimationresultsreflectthoseunderlyingassumptionsaboutcausalityandourunderstandingofhowsatellitedataisrelatedtodeforestation.Empiricalevidencesuggeststhatmonitoringandenforcementaresignificantdeterminantsofenvironmentalcomplianceingeneral(GrayandShimshack2011),andthisisparticularlyimportantintheBrazilianAmazon,wherestrictlawshavelongbeenpoorlyenforced(Bauchetal.2009),recentyearshaveseendismantlingofenforcementcapacity(Escobar2020),andsomehaveevenarguedthatthepublicnatureofsatellitedatacanbeusedtocompensateforlawenforcementshortcomingsresultingfromweakinstitutionalenvironments(Assunção,Gandour,andRocha2019).Further,thevastsizeoftheAmazonmeansthateffectiveenforcementdependsoneffectivemonitoring,whichcanbothmediatetherelationshipbetweensatellitesanddeforestation.Ourinterviewsalsosuggestthatpoliciesthemselvesmayalsofunctionasbothmediatorsandmoderators,andthusourcausalassumptionsinthisrealmalsoarekeytodevelopingspecificationsandinterpretingresultsofempiricalmodels.1Theterm“mediator”isuseddifferentlyindifferentfieldsanddisciplines.Inthispaper,weusethetermmediatortoreferonlytomechanismsonthecausalchainbetweentreatmentandoutcome.Weusethetermmoderatortorefertoconditionsthatareoffthecausalchainbutinfluenceeffectsizes,acknowledgingthatinsomeliterature,thetermmediatorisalsousedinthiscase.Weusetheterm“externalvaliditymoderator”torefertoconditionsonthecausalchainthatvaryacrosscountriesandarethereforeimportanttoconsiderwhenthinkingaboutapplyingthisDAGtodifferentcountries.2Anothertypeofvariablethatcanbiasestimationresultswhenincludedinanempiricalmodelisa“collider”,e.g.somethingcausedbyboththetreatmentandtheoutcome.3.1PolicyasaModerator(offthecausalpath)BrazilianpoliciessuchastheForestCodehavelonglaidoutdefinitionsofwhatconstituteslegalvs.illegaldeforestationandthusdeterminehow,whereandforwhomsatellitedataareusedformonitoringandenforcement.Inthissense,forestpoliciescanbetreatedasmoderators:intheirpresence,satellitedatahasalargercausaleffectondeforestation.Becausetheyarelocatedoffthecausalpathway,theycanbeincludedascontrols(possiblyinteractedwiththetreatment)intheestimationoftheeffect.Figure3.1a:SuggestedDAGhavingPolicyasaModerator.3.2PolicyasaMediator(onthecausalpath)BrazilianpoliciessuchasthePriorityList,colloquiallyknownastheBlackList,wereconceivedatleastinpartduetotheexistenceofsatellitedatashowingthespatialconcentrationofdeforestationinasmallfractionofthejurisdictionsintheAmazon.Inthissense,forestpoliciescanbetreatedasmediators,locatedonthecausalpathway,andthereforenotincludedascontrols(althoughpossiblyconsideredincausalmediationanalysis)intheestimationoftheeffect.Figure3.2:SuggestedDAGhavingPolicyasaMediator.Forpoliciesimplementedafterourtreatment,weobtainupperandlowerboundsofthetreatmenteffectbyassumingthatthepoliciesareeithermediatorsormoderators,respectively,generatingtwodifferentcounterfactualestimates.4.DataWecollectedandmergeddatafromeightdifferentsourcesandreclassifiedandcorrectedtheland-use/coverdatafromMapBiomastocreatea2000-2015deforestationtime-seriesthatcontrolsfortheimplementationofconservationanddevelopmentpolicies,aswellasalargenumberofcontrolsandbiophysicalcharacteristicsatthemunicipalitylevel(Table1).Ourstudyregionincludesthe783municipalitiesintheLegalAmazon.4.1DeforestationandCarbonStockDataFollowingWestetal.(2020),wereclassify,filter,andsummarizepixel-wisedeforestationatthemunicipalitylevelforthe2000–2015periodbaseonannualland-use/cover(LUC)rastermaps(30mresolution)fromtheMapBiomas(v.6)project(Souzaetal,2020)3.Spatiotemporalfilterswereappliedtoensureconsistencyinannualdeforestationacrossthetimeseriesby:(i)replacingeachcloudpixelwiththepixel’sLUCclassinthenextobservableyearorwhenthepixel’slandusecoverclasswasunobservedthroughoutthestudyperiodmaskthemfromtheanalysis;(ii)maskingpixelsthattransitionedfromforesttowater(andvice-versa);(iii)maskingforestpixelsthattransitionedtoanotherclassinoneyear,buttransitionedbackinthenext;and(iv)maskingnaturalnon-forestpixelsthattransitionedtoforest,whichincludednaturalsavannasthatwereclassifiedasnon-forestinsomeyearsbutforestinothers.4Table1:VariableDefinitionsandSourcesdefinitionSourceAnnualdeforestationthousandskm2(MapBiomas)https://mapbiomas.orgForestAreathousandskm2(MapBiomas)https://mapbiomas.orgSoyPriceworldsoyprice,US$/mt,realhttps://databank.worldbank.org/databases/commodity-price-dataSoySuitabilitySuitabilityofsoilsforsoyauthorcalculationsBeefPriceworldbeefprice,US$/kg,realhttps://databank.worldbank.org/databases/commodity-price-dataPastureSuitabilitySuitabilityofsoilsforpastureauthorcalculationsINCRAAmountoflandformallysettledINCRAsettlementsize,thousandkm2http://painel.incra.gov.br/sistemas/index.phpProtectedareasprotectedareas(sumofstate,federalandindigenous),tenthousandkm2http://www.icmbio.gov.br/EnvironmentalFinestotalenvironmentalfinespaid,tenthousandreaishttps://servicos.ibama.gov.br/ctf/publico/3Thefulldescriptionoftheprojectcanbefoundathttp://mapbiomas.org.4ThealgorithmwasimplementedinGoogleEarthEngineandispubliclyavailable(https://github.com/KA-Jones/Voluntary_REDD_Analysis_GEE)andcomparedwithothersourcesofdeforestationintheAmazonPrioritylistofmunicipalitiesdummyvariableindicatingmembershiponlisthttp://www.mma.gov.br/informmaSoymoratoriumdummyfor2006+authorcalculationsCattlemoratoriumdummyfor2009+authorcalculationsGrossDomesticProduct(GDP)Valueofallfinalgoodsandservices,realreaishttps://sidra.ibge.gov.br/Tabela/5938ElectionDummyforelectionyearsauthorcalculationsExchangerateRealeffectiveexchangeratewithInternational$https://data.worldbank.org/indicator/PX.REX.REER4.2PolicyDataThedatasetincludespoliciesthatexistedpriortotheintroductionofDETERandpoliciesthatwereintroducedafter.ProtectedAreasincludethetotalareapermunicipalityofallcategoriesofstateandfederalprotectedlandandallindigenousterritories,basedonshapefilesfromICMBIO,theBrazilianfederalconservationagency.Insomecases,landclassifiedunderdifferentdesignationsoverlapped,forexampleagivenareaoflandfellwithinanindigenousreserveandastateprotectedarea.Theseareasweresubtractedtoensurethetotalareaprotectedunderanydesignationwascorrect.DataonenvironmentalfinesarepublishedbyIBAMA,thefederalForestPolice.Weusereportedinformationonfinesthatwereactuallypaidasmanyoftheissuedfinesarelaterwaivedonappeal.TheareaofINCRAsettlementisprovidedbyINCRAintheformofshapefiles,whichweprocessedforeachmunicipality.ThePriorityListvariableisabinaryvariable,equalto1ifthemunicipalitywasincludedonthePriorityListinanyprioryear.35municipalitieswereplacedonthelistin2008,andotherswereaddedinlateryears,totaling52by2012.Somemunicipalitiesweretakenoffthelist,butweconsiderthem‘treated’forthefullperiodbecausetheeffectsofpolicymaypersistevenaftertheirremoval.Thesoymoratoriumwasintroducedin2006andthecattlemoratoriumin2009forallmunicipalitiesintheAmazonbiome.Otherstudieshaveusedmeasuresofsoyorbeefproductiontodistinguishbetweenmunicipalitiesaffectedbythesupplychainmeasuresandmunicipalitiesthatwerenotaffected.However,productionislikelytobeendogenoustothepoliciesthemselves.Inaddition,thepoliciesaretargetedatnewdeforestation,whichismorelikelyinplaceswithremainingforestedland.Weusebinaryvariablesforbeforeandaftertheintroductionofeachofthemoratoria,andinteractthesewithlaggedforestareaandtheaveragesuitabilityofremainingforestareaforsoyorbeefproductionrespectively.4.3BiophysicalandMacroeconomicControlsWeconstructedsuitabilitymapsforsoyandpasturealsobasedontheMapBiomasdata(v.6).First,wemappedtheareasofbothlandusesin2000.Theseareasweresampledandusedtoparameterizeamachine-learningalgorithm,SimWeight(Sangermano,Eastman,andZhu2010),availablefromtheTerrSetsoftware(v.18.2),whichwasusedtoconstructthesuitabilitymapsforbothlandusesintheforestareaintheBrazilianLegalAmazon.Suitabilitieswerecomputedbasedon16biophysicalmapsobtainedfromBrazil’sNationalSpaceResearchInstitute(INPE;Portugueseacronym):(1)averageannualtemperature;(2)averagediurnaltemperaturevariation;(3)isothermality;(4)temperatureseasonality;(5)maximumtemperatureofthehottestmonth;(6)minimumtemperatureofthecoldestmonth;(7)annualthermalrange;(8)annualprecipitation;(9)precipitationinofthewettestmonth;(10)precipitationinofthedriestmonth;(11)seasonalityofprecipitation;(12)drainagepotential;(13)elevation;(14)slope;(15)soiltype,and;(16)heightabovethenearestdrainage.Lastly,wecomputedannualaveragesuitabilityindicesatthemunicipalitylevel,fortheBrazilianLegalAmazonregion,basedontheforestcoverleftwithineachmunicipalityineachyearfrom2000to2020.Soyandbeefprices,expressedinrealBrazilianreais,arefromtheWorldBankGlobalCommoditiesdatabase.Thesedonotvaryspatially,soweincludethemintheregressionmodelinteractedwith1-yearlaggedforestareaandwithaveragesuitabilityforsoyandpasturerespectively.Thisisonthebasisthatthegreatestresponsetochangesinpricesisexpectedinlocationswithlandavailabletodeforestandwithlandthatissuitedtothecommodityinquestion.MunicipalGDPinrealBrazilianreaisispublishedbyIBGE,thefederalstatisticsagency.Weconstructedabinaryvariableequalto1inyearsthatafederalelectionwasheldandincludedannualvaluesoftherealeffectiveexchangeratecomparedwithinternationalvaluesfromtheWorldBankdatabase.ThesevariablesdonotvaryspatiallywithinBrazilandareusedtocaptureannualvariationacrossthesample.4.4DescriptiveStatisticsAnnualdeforestationratesintheBrazilianAmazondeclinedpost-2004.Ourdatasuggestthatthe5-6yearaveragesbetween2000and2015fellfromapproximately38,000km2in2000-2004to14,500between2010-2015(Table2).Atthesametime,protectedareashavebeenexpandedandpoliciestocounterdeforestationhaveincreased,whichshouldacttoconstraindeforestation.Table2:DescriptiveStatistics(meanwithstandarddeviationinparentheses)2000-20042005-20092010-2015Annualdeforestation,thousandskm238.4823.9414.50(81.88)(57.20)(27.89)ForestArea,thousandskm24922.434777.324691.29(12647.69)(12538.23)(12468.39)SoyPrice3.073.924.71(0.41)(0.78)(0.45)SoySuitability4.644.624.61(7.71)(7.68)(7.67)BeefPrice2.842.974.00(0.18)(0.14)(0.49)PastureSuitability17.0316.9916.96(7.25)(7.24)(7.23)INCRA0.260.550.68(0.81)(2.06)(2.44)Protectedareas0.150.230.28(0.57)(0.85)(0.99)EnvironmentalFines8.399.014.22(38.33)(64.18)(62.24)Prioritylistofmunicipalities0.000.020.06(0.00)(0.14)(0.24)Soymoratorium0.001.001.00(0.00)(0.00)(0.00)Cattlemoratorium0.000.201.00(0.00)(0.40)(0.00)GrossDomesticProduct(GDP)1.5e+052.7e+055.3e+05(8.8e+05)(1.5e+06)(2.7e+06)Election0.250.200.33(0.43)(0.40)(0.47)Exchangerate60.7384.4389.31(2.25)(6.23)(11.05)5.EstimationofcounterfactualoutcomesWeusethefollowingmodeltoestimateannualareaofdeforestationformunicipalityi(𝑌𝑌𝑖𝑖𝑖𝑖)asafunctionoftheimplementationofnearreal-timesatellitemonitoring(𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷),municipalityfixedeffects(𝜇𝜇𝑖𝑖),covariateswithtime-spacevariationincludingagriculturalcommodityprices,laggedforestarea,laggedGDP,andpoliciesthatencourageanddeterdeforestation(𝑋𝑋𝑗𝑗𝑗𝑗𝑗𝑗),andmacroeconomiccontrols(includingtherealeffectiveexchangerateandrealsoyandbeefprices)thatvaryovertimebutnotbetweenmunicipalities(𝑍𝑍𝑘𝑘𝑘𝑘):𝑌𝑌𝑖𝑖𝑖𝑖=𝛽𝛽0+𝛽𝛽1𝐷𝐷𝐷𝐷𝐷𝐷𝐸𝐸𝑅𝑅𝑡𝑡+𝛾𝛾𝑗𝑗𝑋𝑋𝑗𝑗𝑗𝑗𝑗𝑗+�𝛿𝛿𝑗𝑗𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷∗𝑋𝑋𝑗𝑗𝑗𝑗𝑗𝑗+�𝛿𝛿𝑖𝑖𝑍𝑍𝑘𝑘𝑘𝑘+𝜇𝜇𝑖𝑖+𝑢𝑢𝑖𝑖𝑖𝑖Allexplanatoryvariables(Table2)areinteractedwithDETERtocapturechangesintheirimpactsondeforestationbeforeandafter2004.Theexceptiontothisisconservationpoliciesthatwereimplementedsubsequentto2004.Theeffectsofthenearreal-timesatellitemonitoring(DETER)areestimatedusingabinaryvariableforpre-vs.post-2004,theyearoftheintroductionofthePPCDAm.WhiletheinitiationoftheDETERprogramwasamajorfeatureofthispackageofpolicies,othermeasureswereimplementedatthesametimewiththesameobjectiveofreducingdeforestation.Inparticular,ProtectedAreasandapplicationofenvironmentalfineswereexpandedafter2004.WeincludethesevariablesinthemodeltocontrolforindependenteffectsoftheirexpansionaspartofPPCDAminordertoisolatethesatellitemonitoringcomponentofthepackage.AlthoughtheotherPPCDAminitiativesdidnotrelyonDETER,theuseofnearreal-timemonitoringislikelytohaveincreasedtheireffectiveness.WethereforealsointeracttheProtectedAreasandenvironmentalfineswithDETERtoaccountforanychangesintheireffectivenesswhencombinedwithreal-timemonitoring.Thismodelisusedtopredictasimulatedbaselinedeforestation,inwhichDETERisequalto1from2004onwards,andtwoestimatesofcounterfactualdeforestationinwhichDETERisequalto0inallyears(i.e.undertheassumptionthatitwasneverintroduced).Thesimulateddeforestationdiffersfromactualsatellite-derivedmeasurements(ourReferenceCase,Figure2)becauseourmodelcapturestheimpactsofcommodityprices,forestconservationpolicies,andfederaleconomicdevelopmentprograms,butdoesnotaccountforotheridiosyncraticinfluencesondeforestation.WeestimatethedifferenceindeforestationthatcanbeattributedtouseoftheDETERmonitoringsystembycomparingthesimulatedbaselinedeforestationundertheassumptionthatDETERwasimplementedin2004andusedforpolicyenforcementinallsubsequentyearswithcounterfactualestimatesofdeforestationsimulatedbythesamemodelundertheassumptionthatDETERwasnotimplemented,butotherexplanatoryvariablesremainedunchanged.Weestimatetwodifferentcounterfactualstoaccountforthefactthatimportantpolicieswereintroducedtocontroldeforestationafter2004,namelythePriorityListandthesoyandcattlemoratoria.WecannotestimatetheimpactsofDETERontheeffectivenessofthesepoliciessincethereisnospatialvariationinDETERthatcouldbeusedtoidentifythesemodels.However,itislikelythattheavailabilityofnearreal-timesatellitemonitoringcontributedtotheirimpactsondeforestation.Itisalsopossiblethattheavailabilityofsatelliteinformationmayhaveincreasedpublicpressureforforestconservationpolicies.Inthiscase,itcouldbearguedthatintheabsenceofDETERthepolicieswouldnothaveexistedatall.Thetwoversionsofourwithout-satellitescounterfactualscenariosarethereforebasedondifferentassumptionsaboutwherethePriorityListandthesoyandcattlemoratoria(policies)lieonthecausalpathfromsatellitestodeforestation.InCounterfactual1,whichrepresentsalowerboundontheimpactofsatellitemonitoring,weassumethatthepoliciesintroducedafter2004wouldhavebeenfullyeffectivewithoutnearreal-timesatellitemonitoring,andthereforewerenotinfluencedorimpactedbytheintroductionofDETER.Inthiscase,thepolicieslieoffthecausalpathfromsatellitestodeforestation,andactasmoderatorsontheeffectofDETERinourregressionmodel.Toestimatedeforestationinthisscenario,weincludethepoliciesintheestimationascontrolsandholdthepoliciesattheirbaselinelevels(i.e.,either0or1)whenpredictingdeforestationareawithDETERassumedtobezero.Counterfactual2representsanupperboundontheimpactofsatellites.Forthisscenario,weassumethatthePriorityListandsoyandcattlemoratoriawouldhavehadzeroeffectivenessifnearreal-timesatellitedataondeforestationwerenotavailable.Thislackofeffectivenesscouldbebecausethepoliciesexisted,butcouldnotbesuccessfullyenforced,orbecausetheywerenotimplementedatall.Ineithercase,thepoliciesareassumedtobeafunctionofDETERandtolieonthecausalpathbetweensatellitemonitoringanddeforestation,andrepresentoneofthemechanismsthroughwhichsatelliteinformationreducesdeforestation.Toestimatedeforestationunderthisscenario,wefollowtheliteratureoncausalmediationanalysis(Keele,Tingley,andYamamoto2015).ThisinvolvessettingthePriorityListandsoyandcattlemoratoriatothevaluestheywouldhavetakenintheabsenceofDETER(i.e.0inallcases)tosimulatetheno-satellitesestimateofdeforestation,andtotheiractualvaluesforthewith-satellitesestimate.6.ResultsWeestimatethedirectimpactofDETERondeforestationandtheextenttowhichDETERalterstheimpactsofotherdriversofdeforestation,includingprices;biophysicalcharacteristicssuchasmunicipalforestareaandsuitabilityforsoyandpasture;andpre-existingpolicyinstrumentssuchasprotectedareas,environmentalfinesandINCRAsettlements.Figure1:AverageMarginalEffectsofDETER.ThebluepointestimatesaretheimpactswithDETER,with95%confidenceintervalsrepresentedwiththebluebarsaroundthesepoints.ThegraypointestimatesaretheimpactswithoutDETER,andthe95%confidenceintervalsarerepresentedbythegraybarsaroundthesepoints.DETERisshowntohavesignificantimpactswhenthebarsdonotoverlap.Table3andFig.1showthemarginaleffectofeachvariableondeforestationbeforeandaftertheintroductionofDETER.WeobservealargedecreaseindeforestationasadirectresponsetotheintroductionofDETERaswellasindirecteffectthroughchangesintheeffectsofothervariables.Inparticular,therearelargereductionsintheresponsivenessofdeforestationtopricesofkeyagriculturalcommodities,soyandbeef.Priortotheintroductionofnearreal-timemonitoring,deforestationincreasedinyearswithhighcommodityprices.AftertheintroductionofDETER,beefpricesstillpositivelyaffectdeforestationbuttoalesserextent,andsoypricesarenotsignificantlyrelatedtodeforestation.AmongthepoliciesthatwereexpandedaspartofthePPCDAmpackageofmeasures,wefindthatProtectedAreasarenoteffectiveinreducingdeforestationpriortoDETER,butdohaveanegativeandsignificanteffectafterwards.Wedonotobserveanyeffectofenvironmentalfines,withorwithoutsatellitemonitoring.Thismaybebecausethefineswerefrequentlynotenforced,butcouldalsobebecausewedonotaccountfortheinherentselectionbiasinenvironmentalfines,whicharetypicallyimposedwherethereareenvironmentalinfractions(suchashighdeforestation).Figure2comparesestimatesoftotalannualdeforestationunderabaselinescenarioinwhichnearreal-timesatellitemonitoring(DETER)wasadoptedin2004andsubsequentpoliciessuchasthePriorityListandsoyandcattlemoratoriawerealsoimplementedwithtwocounterfactualscenariosinwhichDETERwasnotimplemented.Counterfactuals1and2arerepresentationsofdeforestationwithoutreal-timesatellites,bothallowingfordifferentialimpactsofcommoditypricesandpre-existingpoliciesondeforestationbeforeandafterDETER.Deforestationissubstantiallyhigherpost-2004inboththeCounterfactual1and2simulations,whereDETERisnotintroduced,thaninthebaselinesimulationwhereDETERisinitiatedin2004.ThesedifferencescanbeattributedtoboththedirecteffectoftheintroductionofDETERondeforestation,andtheeffectofDETERontherelationshipbetweencommoditypricesandpoliciesondeforestation.Figure2:EstimatedTotalAnnualDeforestationwithandwithoutSatellites.Counterfactual1assumesthatdeforestationpoliciesactasmoderatorsofDETER,arenotonthecausalpathandshouldbecontrolledforintheestimationsofcausalimpact.Counterfactual2assumesthatdeforestationpoliciesactasmediatorsofDETER,areonthecausalpath,andshouldnotbecontrolledforintheestimationsofcausalimpact.TheCounterfactual1LowerBoundscenariointhisfigureassumesthatthePriorityListandthesoyandcattlemoratoriawouldhavebeenaseffectiveintheabsenceofDETERastheywereintheBaselinescenariowhereDETERwasintroducedin2004.TheCounterfactual2UpperBoundscenarioassumesthatthePriorityListandthesoyandcattlemoratoriawouldnothavehadanyimpactondeforestationintheabsenceofDETER.Inotherwords,itassumesthatifnearreal-timesatellitedatahadnotbeenavailablethepolicieseitherwouldnothaveexistedatallorwouldhavehadzeroeffectiveness.Inthiscase,theiradditionalimpactsshouldalsobeattributedtothesatellitemonitoring.ThePriorityListandthesoyandcattlemoratoriasignificantlyreducedeforestationinourmodel(Table3).Becauseofthis,estimateddeforestationishigherinCounterfactual2whereweassumethatifDETERhadnotbeeninplace,thesepolicieswouldnothavehadtheeffectsthattheydidinpractice.However,theireffectsarerelativelysmallinaggregatebecausetheyonlyinfluenceddeforestationinarelativelysmallsubsetofmunicipalities.ThePriorityListmeasureswereonlyeverimplementedwithinourstudyperiodin52outof783municipalities,andtheeffectsofthesoyandcattlemoratoriawereonlyobservedinmunicipalitieswithsuitablebiophysicalconditionsforsoyproductionorpasture.Weestimateavoideddeforestationunderourtwocounterfactualscenariosbasedonthedifferencebetweenbaselineandeachcounterfactual,foreverymunicipality-year.Thetotalavoideddeforestationfortheregionasawholeisestimatedatapproximately467millionsquarekm.Assuggestedbytheannualdeforestationestimates,wedonotseealargedifferenceinaggregatedeforestationwhetherweassumethattheeffectivenessoflaterpolicieswasduetoDETERorthattheywouldhavebeeneffectiveregardless.Wealsodisaggregatetheestimatesofavoideddeforestationbystate.WeseethelargestabsoluteareasofavoideddeforestationinMatoGrosso,Pará,andAmazonas,whichisunsurprisingasthesestatesalsohavethelargesttotalarea.Wethereforealsoestimatethesedifferencesasapercentofthestatesize(Figure3).TheoldfrontierstatesofMatoGrosso,Pará,andRondôniahavesomewhathigherpercentagesofavoideddeforestationonaveragethanotherstates;followedbytheinteriorstatesofthenewfrontier:Acre,AmazonasandRoraima.AvoideddeforestationishighestasapercentoftotalareainMaranhão,TocantinsRondôniaandMatoGrosso.ThesestatesliewithintheoldfrontierregionsoftheAmazon.DuetotheeffectofDETERinmitigatingtheresponsivenessofdeforestationtosoyandbeefprices,ourresultssuggestthatdeforestationwouldhavebeenevenhigherinthisactivefrontierregionwithoutnearreal-timesatellitemonitoring.Inthemajorityofstates,wedonotseelargedifferencesbetweenthetwocounterfactuals,butthereisageneralpatternthatCounterfactual2ishigherinsomeoftheoldfrontierstatesandnoneofthenewfrontierstates.Thissuggeststhatthepoliciesimplementedafter2004hadalargereffectinthestatesintheoldfrontier,wheredeforestationrateshavebeenhistoricallyhigher.WhiletheeffectsofthePriorityListandthecattlemoratoriumweredistributedfairlybroadlyacrosstheregion,theeffectsofthesoymoratoriumweregreatestinlocationsthatweresuitableforsoyproduction.TheseareconcentratedinMatoGrosso,soassumptionsaboutwhetherthemoratoriumwouldhavebeeneffectiveintheabsenceofDETERhavegreatereffectsontheseestimatesofavoideddeforestation.Figure3:AmountofAvoidedDeforestation,PercentoftheStateArea.ThepercentofthestatethatavoideddeforestationaccordingtoCounterfactuals1and2for2000-2015isnomorethan2%ofthetotalareaofanystate.TheoldfrontierarestatesthatwerelargelysettledduringtheinitialdevelopmentphaseoverseenbyINCRAandbeginninginthelate1960s-early1970s,whilethenewfrontierincludesstatesthathavehistoricallybeenprotectedfromlargescaledeforestationduetotheirdistancefromBrasiliaandthemorepopulatedsouth(SchieleinandBörner2018).TheimpactofCounterfactual2(whichassumesthatpoliciesareonthecausalpathandarenotcontrolledforintheestimations)islargerinsomeoftheoldfrontierwheremoreofthedeforestationpolicieshavebeenfocusedandsmallerinthenewfrontier.6.1AvoidedcarbonemissionestimatesWeestimatedthevolumeofavoidedcarbonemissionsduetothenon-useofsatellitestoinformconservationactionsatthemunicipalitylevelfor2000-2015usingfourcarbon/biomass-densitymapsavailablefortheBrazilianAmazon(Avitabileetal.2016;Baccinietal.2012;2017;Englundetal.2017;Figure4).FollowingWestetal.(2019),carbon/biomassvalueswereconvertedtotCO2ha−1andpixel-wiseaverageswerecomputedfortheremainingforestareasintheLegalAmazonregionby2015permunicipality(Figure5).StandardizedcarbonemissionreductionswereestimatedbasedonourCounterfactual1deforestation(km2;lowerboundary)timestheaverageCO2stockperkm2oftheremainingforestareasby2015withineachmunicipality,dividedbythesizeofthemunicipality(km2).Figure4.Carbon-densitymapsforthe2015forestareasintheLegalBrazilianAmazon.AverageandstandarddeviationmapscomputedbasedonAvitabileetal.(2016),Baccinietal.(2012;2017),andEnglundetal.(2017).BasedontheCounterfactual1estimates,466,904km2offorestweresavedfrom2000to2015intheLegalAmazonregionduetothepresenceofsatellites.Thisamountstoanaverageavoidedemissionof12.4billiontCO2totheatmosphereduringourstudyperiod.Atthemunicipalitylevel,standardizedcarbonemissionreductionsrangedfrom-9to288,611tCO2permunicipalkm2(Figure5).MostreductionswerelinkedtomunicipalitiesinNorthernAmazon,butsubstantialwithin-statedifferencescanbeobservedacrossthewholeregion.Figure5.EstimatesofAvoidedCarbonEmissions2000-2015.CarbonandbiomassvaluesareconvertedtotCO2andpixel-wiseaverageswerecomputedfortheremainingforestareasintheLegalAmazonregionby2015permunicipality.Thesevaluesareappliedtoestimatesofavoideddeforestationpermunicipalitytodeterminetheamountofavoidedcarbon.6.2Benefitsandcostsofsatellites:avoideddeforestationandavoidedCO2emissionsWeestimatethebenefitsofsatellitesintwoways.First,weestimatethebenefitsofeachhectareofavoideddeforestationinregardstothevalueofecosystemservicespreserved(theprovisionofhabitatforspecies,carbonsequestration,waterregulation,recreationandecotourism).TheseestimatesofthewillingnesstopaytoavoiddeforestationrangefromUS$410-3,168/ha/year(Brouweretal.2022;Abdeta2022;SiikamäkiandNewbold2012;Strandetal.2014).Ourlowerboundisfromameta-analysisoftheBrazilianvaluationliterature(Brouweretal.2022).OurupperboundisderivedfromU.S.andCanadianhouseholdWTPtofinancetheprotectionoftheAmazon(SiikamäkiandNewbold2012)5.Second,weestimatetheaverageamountofCO2avoidedinanaverageyear(inthisdecliningdeforestationtimeperiod)andestimatethesocialcostsofcarbon(SSC)avoided.EstimatesoftheSSCrangefrom$24-$185(Rennertetal.2022;vanderPloeg,Emmerling,andGroom2022;Lemoine2021;5Thisvalueof$3,168/ha/yeartranslatesto$92perhouseholdperyear,whichfitswithintheupperboundsofthevaluesreportedinAbdeta(2022)andStrandetal.(2014).Reguant2021;Pindyck2019;CaiandLontzek2019;Tol2019).Weusethe$51/tCO2(IWG2021)aslowerbound,$185/tCO2(Rennertetal.2022)asanupperboundand$85/tCO2(vanderPloeg,Emmerling,andGroom2022)asamid-rangeestimate(Table4).Table4:CostandBenefitRangesforAvoidedDeforestationandCO2EmissionsLowerboundMiddleboundUpperBoundBenefitsAvoideddeforestation$410/ha/year$1,368/ha/yearAvoidedCO2$51/tCO2$85/tCO2$185/tCO2CostsPRODESandINPEbudgets$3.5million/year$227million/year$543million/yearToestimatetheaveragebenefitsofsatellitesforanaverageyearinthisdecliningdeforestationtimeperiod,wefirstsumthehectaresofavoideddeforestationandcarbonemissionsbyyearandestimatetheyearlyaverageforthistimeperiod:2004-2015(weassumenodeforestationisavoidedpriortotheimplementationofDETERin2004).Figure6.EstimatesofAvoidedDeforestationandCO2Emissions2001-2015.EstimationsareinmillionsofhectaresperyearandthemillionsoftonsofCO2.TheannualaverageofthesevaluesareusedinthecalculationsinTable4.TheseannualaveragescalculatedoverthistimeperiodaremultipliedbythevaluesaboveinTable4andpresentedinTable5.Table5:EstimationoftheCostsandBenefitsofDETER(millionsofUS$)LowerBoundMiddleBoundUpperBoundCounter-factual1Counter-factual2Counter-factual1Counter-factual2Counter-factual1Counter-factual2BenefitsAvoideddeforestation1,5951,6215,3235,408AvoidedCO252,74854,29187,91390,486191,341196,940LowerboundMiddleBoundUpperBoundCosts3.5227543Accordingtoourcalculations,thebenefitsrangefromUS$1.6-5.4billion/yearwhentheestimateismadeusingawillingnesstopaytopreservetheAmazonianforest(whichincludesthebenefitsofasuiteofecosystemservices).ThisrepresentsthebenefitstotheaveragehouseholdinBrazil(thelowerbound)andtheGlobalNorth(theupperbound).Thetotalbenefitsareordersofmagnitudehigher,rangingfromUS$53-197billion/year,whenincludingtheglobalcostsofavoidedCO2emissions.ThecostsofsatellitescanbeinferredfromtheprogramcostsofPPCDAM(TableB1)andcomparedtotheINPE(InstitutoNacionaldePesquisasEspaciais)annualbudget(TableB2)fromtheMinistryoftheEnvironment’sbudget.ThePPCDAMmeanannualexpenseswereapproximatelyUS$543millionperyearbetween2007-2014(Casteloetal.2018).Thisupperboundincludesthebudgetforlandplanning,monitoring,andsustainabledevelopment(TableB1).Weusetheaverageannualbudgetforthelatertimeperiod(20011-2014)US$227millionasthemiddlebound,assumingtheprogrambecamemorecosteffectiveovertime.AlowerboundonthecostsofsatellitesistheannualbudgetoftheBrazilianspaceagency.ThisisapproximatelyUS$4millionperyearforourtimeperiodaccordingtodatadownloadedfromSistemaIntegradodePlanejamentoeOrçamentodoGovernoFederal(TableB2).AsecondsourceofinformationontheINPEbudgetestimatestheannualbudgetofINPE(specificallytoaddressPPCDAMandDETER)atapproximatelyUS$3millionperyearforthetimeperiodof2010-2020(Monteiroaetal.2020).Weusetheaverageofthesetwovaluesasourlowerbound.Finally,ourlastsourceofdataoncostsistheMinistryoftheEnvironmentbudget.Thisincludesexpensesforthemanagement(US$11.4billion/year)andcontrol(US$1.4billion)ofenvironmentallaws,andusedtoconfirmthatthePPCDAMisbelowandaportionofthisbudget(TableB4).Accordingtoourestimates,thenetbenefitsofsatellitemonitoringinthistimeperiodofdecliningdeforestation(2004-2015)arepositiveatthelocalscale,thatiswhenconsideringthesuiteofecosystemservicesandtheWTPtopreserveAmazonrainforestfortheaveragehousehold.Dependingonthebenefitandcostassumptions,thisvalueisaslowasUS$1billionperyear6andashighasUS$5.4billionperyear7.Thenetbenefitsarealsopositiveattheglobalscale,thatiswhenestimatingthebenefitsoftheavoidedCO2emissionsusingthesocialcostofcarbon.Dependingonthebenefitandcostassumptions,thisvalueisaslowasUS$54billionperyear8andashighasUS$197billionperyear9.7.DiscussionandconclusionsIn2004,Brazilbeganusingnear-real-timesatelliteinformationthroughtheDETERprogramtodetectandreportillegaldeforestation.ThiswasthestartofasubstantialdropinratesofdeforestationintheBrazilianAmazonthatcoincidedwithaperiodofoverlappingpublicandprivatedeforestationanddevelopmentpolicies.Multiplechangesinthemacroeconomicandpolicyenvironmentsoccurredsimultaneously,includinganexpansioninprotectedareas,increasedapplicationandenforcementofenvironmentalfines,andchangesincommodityprices.Toaccountforandcontrolforthesefactors,weestimatethechangeindeforestationafter2004whileconditioningontheotherchangesthatoccurredsimultaneouslytoseparatetheeffectsofDETERfromotherinfluencesondeforestation.Itisalsopossiblethatreal-timesatellitemonitoringchangedtheeffectivenessoffinesandprotectedareaswhilealsoalteringtheincentivesfordeforestationprovidedbyhighcommoditypricesorregionaldevelopmentpolicy.WeuseinteractionswithinourempiricalmodeltoaccountforwaysthatDETERchangedtheeffectsofpre-existingdriversofdeforestation,andincorporatethisinourestimatesofcounterfactualdeforestationintheabsenceofDETER.OurDAGhighlightstwopossibleassumptionsaboutpoliciesthatwereimplementedafterDETER:weobtainalowerboundoncounterfactualdeforestationbyassumingthatthosepoliciesarecompletelyindependentandoffthecausalpath,andweobtainanupperboundoncounterfactualdeforestationbyassumingthatthosepoliciesaremediatorsonthecausalpathwayandthuspartoftheeffectofDETER..WefindthattheinitiallypositiverelationshipbetweencommoditypricesanddeforestationandthepositiverelationshipbetweenareasofINCRAsettlementandratesofdeforestationnolongerexistafter2004.OurresultsindicatethatDETERhasalargedirect,andimmediate,effectondeforestationin2004andthatDETERalterstheestimatedfutureratesthroughindirecteffectsonotherfactorsthatinfluencedeforestation.Intotal,annualdeforestationissubstantiallyhigherinthecounterfactualscenarioswhereDETERwasnotintroduced,with6TheCounterfactual1lowerboundminustheuppercostbound.7TheCounterfactual2upperboundminusthelowercostbound.8TheCounterfactual1lowerboundminustheuppercostbound.9TheCounterfactual2upperboundminusthelowercostbound.thedifferenceincreasingtowardstheendofthestudyperiod.Intotalweestimatetheamountofavoideddeforestationisapproximately467-471thousandkm2between2001-2015accordingtoourlowerandupperboundsidentifiedinCounterfactuals1and2.ThisisanareathatislargerthanthestateofCaliforniaandmorethantwicetheamountofdeforestationrecordedinthatregioninthesametimeperiod(282thousandkm2).WealsoestimatetheamountofCO2emissionsavoidedandfindthesetotalstobeabout12billiontons.Ourbenefit-costsanalysissuggeststhatthenetbenefitsofsatellitesaresubstantial(US$1-5.4billion/year)whenestimatingbenefitsusingtheWTPtopreserveAmazonrainforest,andbetweenUS$54billion/yearandUS$197billion/yearwhenestimatingthebenefitsofavoidedCO2emissions.Brazilwasanearlyadopterofnear-real-timesatellitedeforestationalerts,buthigh-frequencysatellitedatahasbecomemorewidelyavailabletotropicalcountriesinrecentyears.Forexample,GlobalForestWatchhasworkedwithvariedpartnersinthetropicstoprovideGLADrapiddeforestationalertssince2014,andin2020,Norway’sInternationalClimateandForestInitiativeofferedfreelyavailablehigh-resolutionmonthlydeforestationdatatoanyuser.Ourfindingssuggestthatthistypeofdatacanmakeimportantcontributionstoreducingdeforestation,butthesecontributionswilldependonhowthesatellitedataareused,madepublic,andthecorrespondingsupport(orlackofsupport)bypublicandprivateagencies.WefoundthatpublicandprivateforestconservationpoliciesthatwereimplementedaftertheintroductionofDETERsignificantlyreduceddeforestation.However,wecannotdeterminehoweffectivetheywouldhavebeenintheabsenceofDETER.Certainly,thesepoliciesreliedonsatelliteinformation,buttheyusedbothlow-andhigh-frequencydata.WethereforeestimatelowerandupperboundsbasedontheassumptionsthatthepolicieswouldhavebeenequallyeffectivewithoutDETERortotallyineffectivewithoutDETER.Therealityislikelytoliebetweenthesebounds,whichwouldindicatethatthecombinationofaccesstonear-real-timesatellitedataandthemotivationtousethedatainnovelwayscanbeevenmoreeffectivethanusingthedataforpre-existingpoliciesandenforcement.Finally,weestimatethebenefitsofsatellitesusingatimeperiodofdecliningdeforestation.DeforestationstartedtoincreaseintheBrazilianAmazonafter2012,andhasbeenatdecadehighsinrecentyears.Actually,ratesin2021werethehighestsince2006(INPE2021)andareontargettobehigherin2022.Thisreversalhighlightsthattheeffectivenessofdeforestationalertsandsatellitedatadependsonthewillingnesstoenforceexistingforestlawsandpenalizeviolations.Intheabsenceofpoliticalwilltoconstraindeforestation,theincreasingpublicavailabilityofhigh-frequencydataraisesglobalawarenessofincreasesindeforestation.Thiscangenerateexternalpressuresforconservation,forexamplethroughsupplychaininitiativesdevelopedthroughglobalcommoditymarkets,andexternalpressurefromNGOs,internationalorganizationsandindividualforeigngovernmentstoenforceandstrengthenpolicy.Table3:Marginaleffectsoftime-varyingcovariatesbeforeandafterDETERonannualareaofdeforestation1:BeforeDETER;2:AfterDETER(1)(2)Areaofdeforestation,nomunicFE,noyearFEAreaofdeforestation,withmunicFE,noyearFE1.DETER1._at-48.81-66.33(38.99)(15.68)2._at-48.81-66.33(38.99)(15.68)soysuitabilityindexforAmazonbiome1._at4.8426.769(1.469)(17.78)2._at1.8104.320(0.220)(17.68)worldsoyprice,$/mt,real1._at27.5839.17(7.765)(4.416)2._at-1.306-0.160(2.524)(0.954)LagForestArea(1000km-sq)1._at1.3179.838(1.567)(1.228)2._at0.7809.721(0.0535)(1.108)pasturesuitabilityindexforAmazonbiome1._at-2.145-0.434(4.069)(9.785)2._at2.0535.448(0.140)(9.367)worldbeefprice,$/kg,real1._at55.4972.86(53.63)(20.05)2._at9.8997.469(2.872)(1.411)incrasettlementsize,numberofhectares1._at1.6260.109(3.199)(1.645)2._at-0.7330.126(0.297)(0.285)protectedareas(sumofstate,federalandindigenous),thousandskm21._at-1.9677.432(11.99)(5.179)2._at-3.599-7.145(3.507)(2.728)environmentalfinespaid,reais1._at0.1690.00233(0.109)(0.0226)2._at0.03130.0114(0.0283)(0.00929)1.PL_dum1._at29.04-16.37(3.464)(7.870)2._at29.04-16.37(3.464)(7.870)1.soy_mor_year1._at-5.5911.861(6.046)(2.026)2._at-5.5911.861(6.046)(2.026)1.cattle_mor_year1._at-9.639-3.083(1.986)(0.953)2._at-9.639-3.083(1.986)(0.953)Observations75557555AdjustedR2Standarderrorsinparenthesesp<.10,p<.05,p<.01AcknowledgementsThisresearchwassupportedinpartthroughtheNationalAeronauticsandSpaceAdministrationcooperativeagreementnumberNNX17AD26AwithResourcesfortheFuturetoestimatethevalueofinformationobtainedfromsatellite-basedremotesensing.AdditionalsupportisfromtheNationalScienceFoundation,grantnumberCNH-L1825046.ReferencesAbdeta,Diriba.2022.“WillingnesstoPayforForestConservationinDevelopingCountries:ASystematicLiteratureReview.”EnvironmentalandSustainabilityIndicators16(December):100201.https://doi.org/10.1016/j.indic.2022.100201.Achard,Frédéric,andMatthewC.Hansen,eds.2012a.GlobalForestMonitoringfromEarthObservation.1stEdition.JointResearchCentre,InstituteforEnvironmentandSustainability.BocaRaton:CRCPressTaylorandFrancisGroup.https://www.routledge.com/Global-Forest-Monitoring-from-Earth-Observation/Achard-Hansen/p/book/9781138074477.———.2012b.“UseofEarthObservationTechnologytoMonitorForestsacrosstheGlobe.”InGlobalForestMonitoringfromEarthObservation-1stEdition-Freder,39–54.BocaRaton:CRCPressTaylorandFrancisGroup.https://www.routledge.com/Global-Forest-Monitoring-from-Earth-Observation/Achard-Hansen/p/book/9781138074477.Alix-Garcia,Jennifer,andHollyK.Gibbs.2017.“ForestConservationEffectsofBrazil’sZeroDeforestationCattleAgreementsUnderminedbyLeakage.”GlobalEnvironmentalChange47(November):201–17.https://doi.org/10.1016/j.gloenvcha.2017.08.009.Andersen,LykkeE.,ed.2002.TheDynamicsofDeforestationandEconomicGrowthintheBrazilianAmazon.Cambridge:NewYork:PressSyndicateoftheUniversityofCambridge;CambridgeUniversityPress.Arcand,Jean-Louis,PatrickGuillaumont,andSy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tionallycutdowntheforestintherainiestmonthsoftheyeartoescapesatellites.Butitisnotenoughtohaveamodernmonitoringsystemifthereisnoinspection.MediatorMonitoring:SAD-EEimprovements-atnightandunderclouddetection2017othermonitoringsystemssuchasGLAD(allbiomes)carriedoutbytheUniversityofMaryland.Thissystemisglobal,butithasanumberofproblems,forexample,itisnotsoaccurateatthemunicipallevel.MediatorMonitoring:GLAD(independentfromgovernment)2017ProjetoAmazôniaProtege-MPFisaprojectconceivedbytheFederalPublicMinistry(MPF).TheMPFreceivessatelliteimagesproducedbyINPErecordingareasofillegaldeforestationintheAmazon.Ibamaanalyzestheimages,cross-referencestheinformationonthelandwithpublicdatabasesandissuesreportsconfirmingillegaldeforestation.Then,toidentifythoseModerator/Mediator:IbelievethattheBrazilianPublicMinistry(MPF)canassumearoleasamediatorandalsoasamoderator(onceitpersiisapowerfulinstitutionasanessentialfunctionofJustice).Inthisparticularresponsiblefortheenvironmentaldamage,theMPFteamconductsresearchinpublicdatabases.context,itisassumingaroleofEnforcementMediatorintheprojectAmazoniaProtege-MPF.2018MapBiomasAlertawascreatedtoenhancetheusabilityandeffectivenessofthealertsalreadygeneratedbythesethreesystems(SAD,DETERandGLAD).Giventheresolutionusedinthesesystems(20to60m),thealertsneedtogothroughadetailedvalidationprocess,oftenfollowedbyfieldverificationsbeforetheycanbeusefultodirectmeasuressuchastheembargoandassessmentofillegaldeforestationareas.MapBiomasAlertis,therefore,asystemforvalidatingandrefiningalertsondeforestation,degradationandregenerationofnativevegetationwithhighresolutionimages.Itistheresultofconsultationswithgovernmentagenciesthatusealertsystems(egMMA,IBAMA,SFB,ICMBio,MPF,SEMAs,MP-State,EnvironmentalMilitaryPoliceandTCU)andalertproviders(egINPE,IMAZON,UniversityofMaryland,CENSIPAM,ISA,JICA+JAXA)thatrevealedthebestcontributionthattheMapBiomasnetworkcouldsupporttosupportenvironmentalmonitoring.FederalPublicMinistry(MPF).ProsecutorsusethereportsgeneratedinMapbiomasAlertasothattheycanbeusedindecisionmakingandinestablishinganembargo.TheMPFusuallysendsdocumentstoCongresscontainingSADreportstoavoidthereductionofprotectedareas.MediatorMonitoring:MapBiomasAlertaEnforcement:onthegroundverification2018IBAMAunofficiallyusesdatafromSADmonitoring,buteverymonthImazonsendsthehistoricalseries.“IknowthatMatoGrossostateinspectorsuseourdeforestationalerts”(Imazoninterview).MediatorMonitoring:IBAMA2018Thereferencebaseusedtoleavethelistofillegaldeforestation(LDI)isinformationfromPRODES,however,throughouttheyear,theyusedatafromSADandDETERtofindoutifthemeasurestocombatdeforestationarehavinganeffect.Inaddition,withinthescopeofthegreenmunicipalitiesprogram,forexample,ithasalreadyprovidedtrainingforlocaltechnicianswhoworkinmonitoringandsupportinglocalpactsagainstdeforestation.LDIwasnotbeingupdated,buttheywillupdateit.MediatorEnforcement:combineduseofsettle,greenmunicipalities,illegaldeforestation2021ImazonplannedtolaunchtheDeforestationRiskinthecomingweeks,aprojectinpartnershipwithMicrosoftandFundoValethatusesartificialintelligencetopredictintheshorttermwheredeforestationwilloccur.SADpreventsdeforestationfromcontinuing,thissystemwantstopreventitfromhappening.BasedonSADalerts,thissystemwillpredictdeforestationwithintwomonthstothreemonthsbeforeithappens.See:https://previsia.org/MediatorMonitoring:deforestationpredictionsbyIMAZON(collaborationwithMicrosoftandFundoVale)CAR-RuralEnvironmentalRegistry-Statesusesatelliteimagestoconductthestatemanagementandmonitoringpolicy.MediatorMonitoring:CARPRA-EnvironmentalRegularizationProgram-StatesusesatelliteimagesinconductingthestatemanagementandmonitoringpolicyMediatorMonitoring:PRA2006Publicforestmanagementlawinstitutes,withintheframeworkoftheMinistryoftheEnvironment(MMA),theBrazilianForestService(SFB)MediatorEnforcement:creationofframeworkforMMAandSFBEnvironmentalProtectionDirectorate(DIPRO).IBAMAalsohasanintelligenceareathatislittlepublicizedfornationalsecurityreasons.DIPROisrelatedtoAgênciaBrasileiradeInteligência(ABIN).Thisone,likethegeneralagencies,usessatelliteimages.Satelliteimagesareusedasanindicationofdeforestation,buttheremustbefieldinspectionstoidentifyoffenders.MediatorMonitoring:deforestationmonitoringEnforcement:creationofframeworkforMMAandSFB2002Inordertomakeeffectiveuseofsatelliteimages,IBAMAhadtohireandtraintechnicalstaff,andacquirecomputingtechnology.Startingin2002,IBAMAhiredtechnicalstaffthroughaperiodiccompetitivehiringprocess(concursopublico).Inrecentyears,IBAMAhasnothadbudgettohirenewstaff.Mediator:TheavailabilityofsatelliteimageryledIBAMAtorunconcursosforstaffwithgeotechnologicalskills.Thesestaffarepartofthemechanismfortranslatingsatelliteimagesintousefulinformationforenforcement.Thismeansthatsatelliteimageryisunlikelytoreduceoverallcosts(becauseofthepersonnelneeded).2014IBAMA'sCenterforRemoteSensing(CSR)competedwithINPE(inmonitoring).AfterthelaunchofDETER-Bin2014,theCSRclosedandmonitoringwithinIBAMAbecameremote.In2016startedOperationRemoteControlofIBAMA:DETER-Bdata,crossedwithCARdata,madethehistoryofdeforestationinthatarea,madetheinfractionnotice,andembargoedthearea.TheytooktheCARfromIBAMA.AlotofpoliticalpressureonIBAMA.MediatorMonitoring:CSR(viaDETER-B)In2014,IBAMAledanarmedoperationattherequestofindigenouspeopletoidentifydeforestersinBaúandMenkragnotivillages(BR163).Theyfoundthatthepeopleinvolvedindeforestationwereusingsatelliteimagerytoplantheirillegalactions,havingcontractedageoprocessingteamthatunderstoodsatellites,analyzedINPEdataandhelpedplandeforestation.Fieldcrewswereinstructedtocleartheunderstoryandmaintaintheforestcanopytoavoiddetection.ThegeoprocessingteammonitoredwhereDETERfoundactivedeforestation(whichpolygonswereflagged)andwarnedthefieldteamssotheycouldfleebeforeIBAMAarrived.Moderator:Organizedindigenousgroupsengagedinreal-timeonthegroundmonitoringofdeforestationandcommunicatewithIBAMAMediatorAgentsofdeforestationlearnhowtousesatelliteimagerySatelliteimagesaresharedamongagenciesinvolvedinmonitoringandenforcementthroughinstitutionalpartnerships.Stateagenciesmostlyusefreeimagery,suchasLandsat(withrevisittimeof16days)andrecentlySentinel(revisittimeofjust5days).Moderator:InstitutionalpartnershipsexpanduseofsatellitedataParáevenboughtPlanetimages,butthosewerenowellused,becauseofthewaythattheywerearchived.Mediator:StorageandsharingofsatellitedatadetermineuseAfterfinesareissued,thereisalongprocessofreviewbeforetheyarepaid.In2019,theBolsonaroadministrationcreatedtheso-called“conciliation”chamberthateffectivelydismantledtheprocessofcollectingenvironmentalfines.FromOctober2019toMay2021,thischamberheldfewerthan15meetingsandnofineswereupheld.Moderator:PoliticalcontextExtensionagencies,suchasEmater-PA,workwithsmallproperties,whichrequireshighresolutionimagery.Insomeregions(e.g.inAltamiraandSãoFélixdoXingu)theyhavebeenusingdronesforthepastcoupleofyears(since2019).Moderator:ExistenceandcapacityofextensionprogramsMediator:AdoptionofhighresolutionimagerytosupportextensionAgencieslikeIBAMA/MMAnowhaveremotesensingtechnologywithsufficientprecisiontoallowthemtoidentifyinfractionsandapplyfineswithoutvisitingpropertiesontheground.Thisisthefutureofenforcement,justnothappeningyetbecausethereisnosupportingcaselaw.FutureMediator:Caselawandenforcementsystemsareexpectedtoevolveinresponsetonewtechnologyforhigherprecisionremotesensing.Interviews:Carvalho,JoséCarlos.InterviewwithThaisSantiago,JillCaviglia-Harris,RicardoVale.Openquestions.Onlinemeeting.December,22,2020.Evaristo,Luciano.2021.InterviewbyThaisSantiago.Openquestions.Onlinemeeting.May,272021.Fonseca,AntônioVictor.InterviewbyThaisSantiago.Openquestions.Phonecall.June,2,2021.Gontijo,Gustavo.InterviewbyThaisSantiago.Openquestion.Phonecall.May,29,2021.Pellicciotti,André.InterviewbyThaisSantiago.Semi-structuredquestions.GoogleForms.June,1,2021.Pereira,DiegoHenrique.InterviewbyJimeRodriguesRibeiro.Semi-structuredquestions.Googlemeet.December,02,2021.Silva,BeneditoEvandro.InterviewbyJimeRodriguesRibeiro.Semi-structuredquestions.Googlemeet.February,15,2022.Viana,Jamerson.InterviewbyJimeRodriguesRibeiro.Semi-structuredquestions.Marituba,PA,Brazil.November,25,2021.TableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)DECRETONo23.793,DE23DEJANEIRODE19341934GuardasMultasN/AN/AN/AGuardasouvigias,encarregadosdavigilânciadiretadasflorestas,serãonomeadoshabitantesnoprópriolocal.FederalCodigoflorestalRevogadopelaleinº4.771,de15desetembrode1965.LEINº4.771,DE15DESETEMBRODE19651965PoliciaAPPConselhoFlorestalFederalConselhoMonetárioNacionalCriacaodeareasprotegidasFederalInstituionovoCódigoFlorestaÁreadepreservaçãopermanente(APP)LEINo5.868,DE12DEDEZEMBRODE19721972SNCRCNIRINCRAInstitutoBrasileirodeDesenvolvimentoFlorestal(IBDF);MinistériodaAgriculturaArrendatárioseParceirosRurais,ImpostosobreaPropriedadeTerritorialRuralFederalCriaoSistemaNacionaldeCadastroRural(SNCR),edáoutrasprovidências;CadastroNacionaldeImóveisRurais(CNIR);RegulamentadopeloDecretono72.106,de18deabrilde1973.LEINo5.870,DE26DE1973CNIRINCRAInstitutoBrasileiroArrendatártransformarmadeirasdeFederalAcrescentaalíneaaoartigo26daLeinº4.771,de15desetembro1965,queinstituiTableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)MARÇODE1973deDesenvolvimentoFlorestal(IBDF);MinistériodaAgriculturaioseParceirosRurais,leiemcarvão,inclusiveparaqualquerefeitoindustrialsemlicençadaautoridadecompetenteonovoCódigoFlorestal;SistemaNacionaldeCadastroRural(SNCR);CadastroNacionaldeImóveisRurais(CNIR);InstitutoNacionaldeColonizaçãoeReformaAgrária(INCRA).LEINº6.938,DE31DEAGOSTODE19811981CONAMAPNMAIBAMASISNAMAResponsáveispelocontroleefiscalizaçãodessasatividades,nassuasrespectivasjurisdiçõesFederalDispõesobreaPolíticaNacionaldoMeioAmbiente(PNMA),seusfinsemecanismosdeformulaçãoeaplicação,edáoutrasprovidências.Art.225,daConstituiçãoFederalde19881988FederalConsagraomeioambientecomo"bemdeusocomumdopovoeessencialàsadiaqualidadedevida,impondo-seaopoderpúblicoeàcoletividadeodeverdedefendê-loepreservá-loparaaspresentesefuturasgerações".LEINº7.797,DE10DEJULHODE1989.1989ProjetosIBAMACONAMASecretariadePlanejamentoeFederalCriaoFundoNacionaldeMeioAmbienteedáoutrasprovidências.TableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)CoordenaçãodaPresidênciadaRepública(SEPLAN/PR)LEINº7.875,DE13DENOVEMBRODE19891989FiscalizacaoModificadispositivodoCódigoFlorestalvigente(Leinº4.771,de15desetembrode1965)paradardestinaçãoespecíficaapartedareceitaobtidacomacobrançadeingressosaosvisitantesdeparquesnacionais.LEINº7.803,DE18DEJULHODE19891989FiscalizacaoIBAMAConvêniocomosEstadoseMunicípiosConvêniocomosMunicípiosparaaçõesdefiscalizaçãoFederalAlteraaredaçãodaLeinº4.771,de15desetembrode1965,erevogaasLeisnºs6.535,de15dejunhode1978,e7.511,de7dejulhode1986.DECRETONº1.298,DE27DEOUTUBRODE19941994FLONASIBAMAAprovaoRegulamentodasFlorestasNacionais(FLONAS),edáoutrasprovidências.DECRETONº1.282,DE19DEOUTUBRODE19941994TableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)RESOLUÇÃOCONAMANº237,DE19DEDEZEMBRODE19971997LicenciamentoSISNAMACONAMAConveniosCompeteaoórgãoambientalmunicipal,olicenciamentoambientaldeempreendimentoseatividadesdeimpactoambientallocalFederalEstabeleceacompetênciadoórgãoambientalmunicipalparaolicenciamentoambientaldeempreendimentoseatividadesdeimpactoambientallocal,edeoutrosquelheforemdelegadaspeloEstadoporinstrumentolegalouconvênio.LEINº9.605DE12DEFEVEREIRODE19981998FederalDispõesobreassançõespenaiseadministrativasderivadasdecondutaseatividadeslesivasaomeioambiente,edáoutrasprovidências.DECRETONº2.661,DE8DEJULHODE1998.1998PREVFOGOPREVFOGOIBAMASISNAMAOrganismospúblicosouprivadoshabilitaçãodetécnicosparaatuarjuntoaprefeiturasmunicipaisedemaisentidadesouFederalRegulamentaoparágrafoúnicodoart.27daLeinº4.771,de15desetembrode1965(códigoflorestal),medianteoestabelecimentodenormasdeprecauçãorelativasaoempregodofogoempráticasagropastoriseflorestais,edáoutrasprovidências.SistemaNacionaldePrevençãoeCombateaIncêndiosFlorestais(PREVFOGO)DECRETOS/NDE3DEJULHODE20032003PPCDAMFederalCriadooProgramadePrevençãoaodesmatamentodaAmazônia(PPCDAM),lançadoem2004,foielaboradopeloGrupoPermanentedeTrabalhoInterministerialTableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)(GPTI),constituídoem2003pormeiodoDecretos/nde3dejulho,comointuitodeconteroaumentododesmatamentonaAmazônia.“Art.1Reduçãodosíndicesdedesmatamentonosbiomasbrasileiros,pormeiodaelaboraçãodeplanosdeaçãoparaaprevençãoeocontroledosdesmatamentos”RevogadopeloDecretonº10.142,de2019LEINº11.284,DE2DEMARÇODE20062006PMFSPAOFSFB(ServiçoFlorestalBrasileiro)SISNAMAConvênios,termosdeparceria,contratosouinstrumentossimilarescomterceiros.RepassesderecursosfinanceirosoriundosdasconcessõesflorestaisFederalPlanodeManejoFlorestalSustentável(PMFS);Dispõesobreagestãodeflorestaspúblicasparaaproduçãosustentável;institui,naestruturadoMinistériodoMeioAmbiente,oServiçoFlorestalBrasileiro-SFB;criaoFundoNacionaldeDesenvolvimentoFlorestal-FNDF;alteraasLeisnos10.683,de28demaiode2003,5.868,de12dedezembrode1972,9.605,de12defevereirode1998,4.771,de15desetembrode1965,6.938,de31deagostode1981,e6.015,de31dedezembrode1973;edáoutrasprovidências.DECRETONº5.975DE30DENOVEMBRO2006PMFSPMFSIBAMASISNAMA;SINIMACONAFLORFederalRegulamentaosarts.12,partefinal,15,16,19,20e21daLeino4.771,de15desetembrode1965,oart.4o,incisoIII,daLeino6.938,TableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)DE2006.de31deagostode1981,oart.2odaLeino10.650,de16deabrilde2003,alteraeacrescentadispositivosaosDecretosnos3.179,de21desetembrode1999,e3.420,de20deabrilde2000,edáoutrasprovidências.SistemaNacionaldeInformaçõesAmbientais(SINIMA).DECRETONº6.321,DE21DEDEZEMBRODE2007.2007INPE(Re)cadastroINCRAIBAMAICMBIOMunicipiosListademunicípiosprioritáriossituadosnoBiomaAmazôniaparamonitorardeformapreventivaecontrolededesmatamentoilegal.FederalDispõesobreaçõesrelativasàprevenção,monitoramentoecontrolededesmatamentonobiomaAmazônia.Definição,ações,correlação,prevenção,controle,desmatamento,fauna,flora,regiãoamazônica.“§1oOobjetivoprecípuodaatualizaçãocadastraléreunirdadoseinformaçõesparamonitorar,deformapreventiva,aocorrênciadenovosdesmatamentosilegais,bemcomopromoveraintegraçãodeelementosdecontroleegestãocompartilhadaentreaspolíticasagrária,agrícolaeambiental”.DECRETONº6.514,DE22DEJULHODE2008GeorreferenciamentoFiscalizacaoSISNAMAMPN/AAsinfraçõesadministrativassãopunidasFederalMinisterioPublico(MP);Dispõesobreasinfraçõesesançõesadministrativasaomeioambiente,TableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)2008comoembargodeobraouatividadeesuasrespectivasáreas,entreoutrassanções.estabeleceoprocessoadministrativofederalparaapuraçãodestasinfrações,edáoutrasprovidências.DECRETONº6.527,DE1ºDEAGOSTODE20082008CTFAPPCDAMBNDSComitêOrientador(COFA)SociedadecivilFederalCriaoFundoAmazônia;ComitêTécnico(CTFA)comaatribuiçãodeatestarasEmissõesdeCarbonoOriundasdeDesmatamento(ED)calculadas.RESOLUÇÃONº3545/2008BANCOCENTRAL2008CCIRMCRBancoCentralINCRAFederalCriarestriçõesparaacessoacréditosbancáriosparaaquelesquenãocomprovemaregularizaçãoambiental;AlteraoMCR2-1paraestabelecerexigênciadedocumentaçãocomprobatóriaderegularidadeambientaleoutrascondicionantes,parafinsdefinanciamentoagropecuárionoBiomaAmazônia;ManualdeCréditoRural(MCR).CertificadodeCadastrodeImóvelRural(CCIR)PORTARIA28,DE24DE2008INPEListademunicipiosListademunicipiosprioritariosTableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)JANEIRODE2008prioritariosPORTARIAMMANº103,DE24-03-2009-SOGI2009CARCondicionouaexclusãodalistademunicípiosembargados,aexecuçãodoCadastroAmbientalRural(CAR)em80%deseuterritório.LEINº12.651,DE25DEMAIODE20122012CARPRAsSecretariasEstaduaiseMunicipaisdeMeioAmbienteEMATERTerceirosetorRegularizaçãoambientaldaspropriedadesFederalCadastroAmbientalRural(CAR);ProgramasdeRegularizaçãoAmbiental(PRAs);“Art.1o-A.EstaLeiestabelecenormasgeraissobreaproteçãodavegetação,áreasdePreservaçãoPermanenteeasáreasdeReservaLegal;aexploraçãoflorestal,osuprimentodematéria-primaflorestal,ocontroledaorigemdosprodutosflorestaiseocontroleeprevençãodosincêndiosflorestais,eprevêinstrumentoseconômicosefinanceirosparaoalcancedeseusobjetivos”DECRETONº7.830,DE17DEOUTUBRODE20122012CARPRAFederalDispõesobreoSistemadeCadastroAmbientalRural,oCadastroAmbientalRural,estabelecenormasdecarátergeralaosProgramasdeRegularizaçãoAmbiental,dequetrataaLeino12.651,de25demaiode2012,edáoutrasTableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)providências.INSTRUÇÃONORMATIVANo3,DE18DEDEZEMBRODE20142014SICARPISISFB/MMAÓrgãoseentidadesdaadministraçãopúblicaAcordosdeCooperaçãoTécnicaSistemadeCadastroAmbientalRural(SICAR)FederalInstituiaPolíticadeIntegraçãoeSegurançadaInformação(PISI)doSistemadeCadastroAmbientalRural(SICAR)edáoutrasprovidências;TermodeCompromissodeManutençãodeSigilo(TCMS)INSTRUÇÃONORMATIVANo2,DE5DEMAIODE20142014CARSISCARÓrgãoestadual,distritaloumunicipalcompetenteÓrgãosdoSistemaNacionaldeMeioAmbiente(SISNAMA)Órgãocompetenteeainstituiçãoouentidaderepresentativadospovosoucomunidadestradicionais;FUNAIAanálisedosdadosdeclaradosnoCARseráderesponsabilidadedoórgãoestadual,distritaloumunicipalcompetente.FederalDispõesobreosprocedimentosparaaintegração,execuçãoecompatibilizaçãodoSistemadeCadastroAmbientalRural(SICAR)edefineosprocedimentosgeraisdoCadastroAmbientalRural(CAR).DECRETONº8.235,DE5DEMAIODE20142014CARPRAMMAEstadoseMunicípiosN/AAlocalizaçãodaÁreadePreservaçãoPermanenteouReservaLegalouáreadeusoFederalEstabelecenormasgeraiscomplementaresaosProgramasdeRegularizaçãoAmbientaldosEstadosedoDistritoFederal,dequetrataoDecretono7.830,de17deoutubrode2012,instituioProgramaMaisAmbienteBrasil,edáoutrasprovidências.TableA2:DeforestationandSatelliteLawsbyCategory(Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiomefrom1934to2021)LawYearMonitoring(oneworddescription)Enforcement(oneworddescription)Institutions(who/what)PublicPartnerships(who)OtherPartnership(who)MunicipalityImpacts(how)Level(municipality,state,federal)Notes(descriptionofpolicies)restritoaserrecomposta,recuperada,regeneradaoucompensada;PORTARIANº360,DE8DESETEMBRODE20172017SecretariasEstaduaiseMunicipaisdeMeioAmbienteListademunicípiosprioritáriosFederalEdiçãoanualdalistademunicípiosprioritáriosparaaçõesdeprevenção,monitoramentoecontroledodesmatamentoedaediçãoanualdalistademunicípioscomdesmatamentomonitoradoesobcontrolePORTARIANº161,DE15DEABRILDE20202020SecretariasEstaduaiseMunicipaisdeMeioAmbienteListademunicípiosprioritáriosFederalDispõesobreosrequisitosparaainclusãonalistademunicípiosprioritáriosparaaçõesdeprevençãoecontroledodesmatamentoenalistademunicípioscomdesmatamentomonitoradoesobcontrole.PORTARIAMMANº475,DE21DEOUTUBRODE20212021SecretariasEstaduaiseMunicipaisdeMeioAmbienteListademunicípiosprioritáriosFederalDispõesobreaatualizaçãodaslistasdemunicípiosprioritáriosparaaçõesdeprevençãoecontroledodesmatamentoedemunicípioscomdesmatamentomonitoradoesobcontrole,aqueserefereoDecretonº6.321,de21dedezembrode2007.Definitions(forTableA2)●Law:Policiesfortheprevention,monitoringand/orcontrolofdeforestationintheAmazonbiome●Monitoring:theremotesensing,classificationandpublicationofsatellitedata(actionsthatarelinkedtowhat’shappeninginspace)●Enforcement:theeffortsneededonthegroundtoenforcelaws●Institutions:evidencethatriles,lawsandgovernmentareneededforenforcementormonitoring●PublicPartnerships:partnershipsbetweendifferentgovernmentagencies●OtherPartnerships:partnershipsbetweengovernmentagencies,privateorganizationsand/orNGOs●MunicipalityImpacts:evidencethatmunicipalitiesimpaconeanother-orthatalaw/enforcementofalawimpactanothermunicipalityAppendixBThisappendixoutlinesthedifferentsourcesusedtoestimatethecostsofsatellites.TableB1outlinestheprogramcostsofPPCDAM.Thisbudgetincludesseparatelineitemsforlandplanning,monitoringandsustainabledevelopment.ThePPCDAMmeanannualexpensesaretheaverageoftheannualmeanforthetwobudgettimeperiods.Thiseight-yearannualmeanisapproximatelyUS$543millionperyear.TableB1:PPCDAMExpenses(millions)2007-2010AnnualAverage(R$)AnnualAverage(US$)2011-2014Annualaverage(R$)AnnualAverage(US$)LandPlanning82020511143610956Monitoring95924012970317690SustainableDevelopment4584114661963815981Total636315918591777444227Source:(Casteloetal.2018).Averageannualexchangeratein2008:1BR$=US$0.54.In20121BR$=US$0.51,OECDhttps://data.oecd.org/conversion/exchange-rates.htmTableB2outlinestheannualbudgetoftheBrazilianspaceagency,INPEbetween2011and2021.Annualexpansesdeclineeachyearinthistimeperiod.Theyarehighestin2011atoverUS$4millionandlowestin2021at$492,000,representingan88%budgetreduction.TheannualaverageisapproximatelyUS$1.9millionperyear,butgiventhatthedeclineinbudgetlargerhappenedafterourtimeperiodofconsideration,US$4millioniswhatweuseasourlowerboundforsatellitecosts.TableB2:BrazilianSpaceAgency(INPE)AnnualBudgetR$US$20116,726,0004,020,32320126,726,0003,443,93220136,389,7002,963,68320146,389,7002,715,55520156,389,7001,920,55920165,916,2001,694,70120175,379,0001,685,67820183,220,000881,22620193,220,000816,43020203,220,000624,63620212,655,812492,364AnnualAverage5,112,0101,932,644Source:SistemaIntegradodePlanejamentoeOrçamentodoGovernoFederal(https://www.siop.planejamento.gov.br/modulo/login/index.html#/)Infographichere:https://infogram.com/evolucao-orcamento-i-1h7k230q8qggv2xExchangeratesfromhttps://data.oecd.org/conversion/exchange-rates.htmTableB3outlinestheaverageannualbudgetoftheBrazilianspaceagency,INPEbetween2010and2020astheirbudgetspecificallyrelatestoPPCDAMandDETER.Thesebudgetitemsaredividedbetweensatellitemonitoring,supervision(salariesofheadsofagency),theprovisionofDETERandPRODESalerts,andDETER.ThelargestbudgetitemistheDETERandPRODESalerts.ThesumofalltheseitemsisapproximatelyUS$3millionperyear.TableB3:INPEPRODESandDETERAnnualBudgetR$US$Monitoring3,000,000901,713Supervision500,000150,286DETER&PRODESalerts5,000,0001,502,855DETER2,000,000601,142Total10,500,0003,155,996Source:(Monteiroaetal.2020).Exchangeratefor2015:https://data.oecd.org/conversion/exchange-rates.htmTableB4includesourlastsourceofdataoncostsistheMinistryoftheEnvironmentbudget.Thisincludesexpensesforthemanagement(US$11.4trillion/year)andcontrol(US$1.4trillionofenvironmentallaws,andusedtoconfirmthatthePPCDAMisbelowandaportionofthisbudget.TableB4:MinistryoftheEnvironmentalBudgetforCombinedMunicipalityandStateExpensesforEnvironmentalManagementandControlintheAmazonManagement(stateandmunicipality)Control(stateandmunicipality)R$millionUS$millionR$millionUS$million20047,4202,5371,00334320058,4763,482922379200612,6595,8202,2241,022200714,4597,4262,0831,070200822,74212,4003,6822,008200923,99512,0044,6562,329201027,73215,7666,9013,923201123,93114,3043,8152,281201229,47115,0904,8502,483201339,01418,0964,5732,121201447,82620,3263,5121,493201535,14310,5633,6781,106AverageAnnual24,40611,4843,4921,713Source:SistemadeInformaçõesContábeiseFiscaisdoSetorPublicoBrasileiro,MinistériodoEconomiahttps://siconfi.tesouro.gov.br/siconfi/pages/public/consulta_finbra/finbra_list.jsfReferencesCastelo,ThiagoBandeira,MarcosAdami,CrislayneAzevedoAlmeida,andOrianaTrindadedeAlmeida.2018.“GovernoseMudançasNasPolíticasdeCombateAoDesmatamentoNaAmazônia.”Revibec:RevistaIberoamericanadeEconomíaEcológica28(December):125–48.Monteiroa,AntonioMiguelVieira,MariaIsabelSobralEscadaa,MariaAntôniaF.deOliveira,AndreaCoelho,LuisE.Maurano,ClaudioAlmeida,CamiloRennó,andLubiaVinhas.2020.“AnálisedeEfetividadeeCusto-EfetividadeparadoisSistemasdeMonitoramentoeAlertadeDesmatamentos:DETER-INPEeDFLORASCCONavaliadosparaoperíododeJaneiroaDezembrode2018noPará.”NOTATÉCNICA2.LiSS–LaboratóriodeinvestigaçãoemSistemasSocioambientais.OBT,INPE.https://www.lissinpe.com.br/nt-deter-dflora.

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