Regionalinteractionsofdeforestation

insidetheGuianaShield

This study was led with the financial and technical support of the REDD+ for the Guiana Shieldproject.TheREDD+fortheGuianaShieldisacooperationprojectbetweenSuriname,Guyana,FrenchGuiana and Amapá State of Brasil. It is funded by the European ERDF/FEDER Interreg Caraibes IVfunds,FFEM,ONFandRégionGuyane.

Tableofcontent

I| EXECUTIVESUMMARY.........................................................................................................................5

II| BACKGROUND........................................................................................................................................6

III| POTENTIALREGIONALSPILLOVEREFFECTS............................................................................7III.1 RATIONALCONTEXTFORSPILLOVEREFFECT..................................................................................................7III.2 REGIONALINTERACTIONSINTHEGUIANASHIELD.....................................................................................10III.2.1 Economicinteractionsandmarketleakageeffect......................................................................10III.2.2 ActivityshiftingleakageandSmall-ScaleGoldMining.............................................................14

IV| THEORETICALANDEMPIRICALMODEL...................................................................................15IV.1 THEORETICALMODELOFDEFORESTATIONANDLEAKAGEEFFECTS........................................................16

IV.1.1 Modelofdeforestation.............................................................................................................................16IV.1.2 Modelingregionalinteractions............................................................................................................18

IV.2 EMPIRICALESTIMATIONS.................................................................................................................................19IV.2.1 Dataandvariables.....................................................................................................................................19IV.2.2 Econometricstrategy................................................................................................................................21

V| RESULTSANDIMPLICATIONS........................................................................................................22V.1 ECONOMETRICRESULTS.....................................................................................................................................22V.1.1 Resultsofestimateddeforestationmodel.........................................................................................22V.1.2 Resultsofestimatedregionalinteractionseffects.........................................................................25

V.2 IMPLICATIONSANDPERSPECTIVES...................................................................................................................29

VI| REFERENCES......................................................................................................................................32

ListofFiguresFigure1:Trendofdeforestationfrom2002to2010_______________________________________________________________8Figure2:GrowthinriceexportsofGuyanaandSuriname ______________________________________________________12Figure3:GrowthinbilateralexportsoffoodproductsbetweenGuyanaandSuriname_______________________13Figure4:Summarydataandsources_____________________________________________________________________________20Figure5:Observeddeforestationvspredictedvaluesbythemodel_____________________________________________23Figure6:Econometricresultsandstatisticaltests_______________________________________________________________24Figure7:Endogenousregionalinteractionsofdeforestation_______________________Erreur!Signetnondéfini.

I| ExecutiveSummary

The state of Amapá in Brazil, Guyana, Suriname and French Guiana have many

environmental,economicandsocioculturalsimilaritiesthatfacilitatethemigrationofpeople

within theGuianaShieldandeconomic interactionsbetweenthesecountries.Thiscontext

suggests that theremay be a shift of deforestation, through shifting activities, from one

countrytoanother.

The main objective of this study is to know if leakage effects of deforestation can exist

betweenthesecountriesandcanbegeneralized.Inotherwordswearelookingatwhether

an increase (or decrease) of deforestation in a country always causes a reduction (or

increase)ofdeforestationinothercountries.Therefore,thestudyfocusesontwodifferent

typesof regional interactions, theeffectsofmarket leakageand leakageeffectsrelatedto

thedisplacementofgoldminingactivities.Theeconometricmodelingexercisestrengthens

the hypothesis of regional interactions that lead to leakage effects of deforestation.

Nevertheless,theresultsshowthattheseregional interactionscouldnotbehomogeneous

withintheGuianaShield.Accordingtoestimatesamajorityoftheleakageeffectrelatedto

the displacement of gold mining activities come from Amapá and most of them moves

towardFrenchGuianaandSuriname.Moreover, theestimatesalsoreveal that theAmapá

andGuyanaarethemostsensitive toexogenousshocksofdeforestationof theSuriname,

dueprobablytothemarketeffects.

Insum,theresultsencouragemoreconsultationandcooperationwithintheGuianaShield.

AtatimewheneachcountryenteredtheREDD+process,thisstudyspecificallyhighlights

the risk of implementationof REDD+uncoordinated and at different speeds. Indeed, the

successfulavoideddeforestationeffortsrealizedbysomecountriescouldbeannihilatedby

thesesleakageeffectsinsidetheGuianaShield.

It should be noted that results and implications that can be drawn are bounded by the

availability,qualityandconsistencyofdataofeachstudiedcountryoftheGuianaShieldand

the technical difficulties tomodel and to generalize such complexphenomena, as are the

regionalinteractions.However,thisstudyprovidesbasicelementsforfurtherreflectionand

understandingofregionalinteractionswithintheGuianaShield.

II| Background

TheGuiana Shield is northeastern contiguous eco-region housing one of the richestworld

spotofbiodiversitywithlotofendemicspecies(Berryetal.,2007).Thisistheresultofanextensive

forestareaonwholeGuianaShieldcombinedtoleastpopulatedareasoftheworld.Indeed,French

Guiana,SurinameandGuyana,includedintheGuianashield,arerankedinthetopthreeofhighest

forestareapercapita(Hammond,2005).Beyondthebiodiversity issue,thetropical forestofthese

countries and Brazilian state of Amapá (that form the whole REDD+ Guiana Shield project

intervention area), contribute to stock more than 10% of global forest carbon stock and thus

significantlyimpactinregulatingtheglobalclimate(Saatchietal.,2011).

The High Forest covers (HF) of the Brazilian state of Amapá, the Guyana, the Suriname and the

FrenchGuiana(higherthan0,85%)aresubjectuntilnowtolowthreatasillustratedbylowratesof

Deforestation(LD)(lowerthan0.1%)1.ThusaccordingtoDaFonsecaetal.(2007)theseregionscan

beclassifiedinHFLDgroup.However,regardingpositionofHFLDgroupinforesttransitioncurve,the

threats are coming (Mather, 1992; Rudel, 2002). Indeed, in recent decades, the Brazilian state of

Amapá,theGuyana,theSurinameandtheFrenchGuianaaresubjecttoanincreasingofunderlying

threatsresultingfromeconomicanddemographicdynamics.Thissuggeststhatdeforestationinthe

Guianashieldmayrapidlyincreasesinthecomingyears(Williams,2011).

Furthermore, similar cultural groupsare spreadacross all of theGuianaShield.Cultural and social

proximityisstillmorepronouncedinborderareaswhicharelimitedbyarivertothepointthatsome

familiesmaybedistributedeithersideoftheborder.Moreover,thelanduseandlandusechanges

areclosebetweenthesecountriesbecauseofsocio-culturalsimilaritiesanduniqueecosystem,and

miningandagriculturearethemaindirectcausesofdeforestationinallcountries.

However,theseregionsareadministrativelyseparateandautonomousterritories.Thereforeeachis

implementing its own economic, social and environmental policies. In an environment where

mobilityofagent iseasybetweenregions, theestablishmentofasymmetricpolicies,particularly in

termsoflanduse(e.gcommandcontrolpolicy,...),cancausethedisplacementofpopulationfroma

country toanother.Also,highereconomicdevelopmentgrowth ina country (infrastructure, social

security, etc ..) can also encourage people from other countries tomove their business and take

advantageofmorefavorableeconomicconditionsinaneighboringcountry.

These effects may be more pronounced in the presence of political instability in a country (e.g.

populationdisplacementduringtheCivilWarinSuriname).Beyondthedisplacementofpopulations,

thecompetitioninthemarketsinconnectionwithlanduse(particularlyagriculturalandmining)can

explain the implicit moving of a commodity production from one country to another activity.

Ultimatelytheseregionalinteractionscanleadtoatransferofdeforestationbetweencountries.

Theseregionalinteractionsresultingfromleakageeffectsofdeforestationmaycompromiseoverall,

atthescaleoftheGuianaShield,theefficiencyofthefightagainstdeforestationpoliciesconducted

unilaterally.

Indeed,eachcountryisnowactivelyengagedinthereductionofdeforestationpolicy(WWF,2012),including throughREDD+mechanism inGuyana, SurinameandBrazilian stateofAmapá. REDD+ is

1Source:http://www.globalforestwatch.org2Ineconomics«spillovereffect»isanexternalitythataffectacountryresultingfromaneconomiceventofanothercountry.3Notehoweverthatsince1january2010,SurinameandGuyanabenefitsfromduty-freeandquot-freeaccesstothe

design under the UNFCCC as an incentivizing reductions emissions from deforestation and forest

degradation,conservingandenhancing forestcarbonstocksandsustainablymanaging forests that

haveemergedas international instrument fordevelopingcountriesand to involve them inclimate

changemitigation efforts (Angelsen, 2009). French Guiana is a French territory and therefore not

eligibletotheREDD+mechanismasanAnnex1country.Thought,theterritoryisactivelyinvolvedin

thevoluntaryimplementationofactivitiestolimititsdeforestation.

However,uncoordinatedimplementationofREDD+inacontextwheretheregionalinteractionslead

to leakage effects of deforestation between countries, could limit the effectiveness of individual

effortsregardingforestprotectionofGuianaShield.

Thus, theanalysisof theexistenceornotofhistoricalspillovereffectsofdeforestation is themain

rationale of this study. Of course, there is no means for generalized and estimated interactions

within the Guiana Shield over long period with certainty. Since then, we used in this study an

inductive approach that consist to test hypothesize on the studied effect and its causes (here

spillover effect) using past observations. Therefore, we describe in first part, the economic

characteristics and dynamics of deforestation of each studied country that help us identify the

potentialregionalinteractionsthatmayoccurbetweencountriesofGuianaShield.Evenifliterature

isincreasingonlandrelatedleakageanddistantdeforestationdrivers,theestimationmethodsstay

poorlydeveloped(Henders,2014).Therefore,inanotherpart,wedevelopedatheoreticalmodelof

deforestationincludingregional interactionsandthenproposeaninnovativeeconometricmodelof

deforestationtotestspillovereffectsontheregionoverthe2000-2010periods.

III| Potentialregionalspillovereffects

III.1 Rationalcontextforspillovereffect

Spillover effects2of deforestation are most of time explained by economics interactions

(Wunder, 2008). Economic, social and environmental similaritiesmay thus justify the existence of

such effects. Therefore before to identify potential drivers of regional spillover of deforestation

between the countries, we describe briefly forest change trend and economic and social

characteristicsofeach.

Suriname, as the smallest country in South America, is an upper middle-income country that

recorded strong performing economy on the period 2004-2014 with an average growth of 4.5

percent that results tomore than10,000$ incomepercapita in2014 (source:Worldbank,2014).

Forest cover in Suriname represents about95%of entire territory including95%ofprimary forest

cover. Average annual deforestation is close to 5,000hawith an increasing trendover the period

2001-2013(i.e.0.03%ofannualrateofdeforestation)(cf.figure1).Maincausesofdeforestationin

Suriname are mining sector including gold, bauxite and oil extraction activity and agricultural

expansionlocatedoncoastalareaforpermanentagricultureandhinterlandforshiftingagriculture.

Indeed, the country has largemineral reserve and an economy dominated by the production and

exportofgold,bauxiteandfuel.Thegoldsector isdividedbetweenRosebelGoldMine(located in

themineralrichBrokopondodistrict innortheastern)andverylargenumberofunregulatedsmall-

2Ineconomics«spillovereffect»isanexternalitythataffectacountryresultingfromaneconomiceventofanothercountry.

scalegoldproducers.AgriculturalsectorinSuriname,representingmorethan10%ofGDP,ismainly

focused on rice, bananas, oranges, vegetables, plantains and coconuts while rice and bananas

representthemajorityofexportsvalue(Latawiec,2014).Fromthelast15years,agriculturalexports(particularly rice andbananas exports) had increasedwith the global growth in trade. Suriname is

commonly assumed as a country with high potential to increase agricultural production however

constraint by relatively poor infrastructure, outdated land tenure system and restricts producers’

accesstocredit.However,housingdevelopment,infrastructureofcommunicationandhydroelectric

are increasing with economic and population growth and represents an increasing drivers of

deforestationinthelastdecade.Toalesserextenttimberharvestingdrivesalsoforestdegradation

anddeforestationinthecountry.Notehoweversince1995,theSurinamesegovernmentestablished

severalforestryactionstomonitorandcontrolloggingandsettingasidenewprotectedareas.Today

morethan12percentofisunderprotectedareastatus.

Figure1:Deforestation(ha)from2001to2011

Guyana, as the third smallest country in South America, is a low income country and the third

poorest intheWesternHemispherewithonly lessthan4,000USdollarspercapita incomein2014

(source:Worldbank,2014).GuyanahoweverrecordssimilareconomicgrowththanSurinameonthe

last 10 years. Overall, even if Guyana’s economy shows a development delay compared to its

neighbor,bothhavegloballyacomparableeconomicpattern.ForestcoverinGuyanarepresents75%

ofentire territoryandcharacterizedby roughly60percentofwhich is classifiedasprimary forest.

ForestlossisclosetoSurinametrendandrepresentsannuallyapproximately5,000ha(i.e.0.03%of

annualrate)withnoevidenceofan increasingtrendovertheperiod2001-2013(cf. figure1).Four

anthropogenicchangedriversthatleadtodeforestationareusuallyhighlightedinGuyana.Firstone

istheexpansionofminingactivitiesoccurringmostoftimeinclustersalongstreamsornearwater

bodies and in remote areas with limited road infrastructure suggesting small to medium scale

activity.Indeed,Guyanaeconomyismainlybasedonextractiveindustryandlargelydependsonthe

exports of mineral especially gold and bauxite. Permanent and shifting cultivation areas are

increasing leading to large contribution forest change. Indeed, agricultural sector, contributing as

well as GDP (about 20%) is mainly dominated by rice and sugar production. Whereas sugar

production is dominated by 100% State-owned Guyana Sugar Corporation, small-scale private

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Guyana" Suriname" Amapa" Fr."Guiana"

producers mainly carry out the rice production. Since last 10 years, Guyana multiplies the policy

efforts to liberalize and facilitate trade in order to improve competitiveness. Government

increasingly supportagriculturaldevelopmentbyanextensionof servicesprovided to farmersand

various tax exemptions. Then forestry activity within the State Forest Area is recognized most

noticeablybytheappearanceofroadandthedegradationcausedbysurroundingselectiveharvest

areas. However pressure from forest logging decreased from1995 since the government issued a

three-yearmoratoriumonnew logging concessions and shortly thereafter, enactedenvironmental

legislation over the timber industry. As a consequence the level of harvested today is very low.

Development of infrastructure as roads and settlements is also an increasing driver forest change

withtheincreasingofsuchprojectsinacontextofeconomicdevelopment.Indeed,highGDPgrowth

over theperiod2000-2014was accompaniedbyhigh inflation,which is nevertheless slowed since

2012duemainlytoanactivemonetarypolicytoattainpricestability.Fromaneconomicperspective,

highesteconomicgrowthisexpectedforGuyanauntil2020.

Amapá isaBrazilianState,anemergingcountrywithoneof theworld’s fastestgrowingeconomy,

representingonly0.2%ofBrazil’seconomyandaccountsfornearly4,000$incomepercapita.Forest

coverinAmapástaterepresentsmorethan75%ofentireterritory.Eveniftheeconomyofstateof

Amapá is largely dominated by the tertiary sector representing close to 90% of total GDP

contribution, Amapá recorded the highest annual deforested area (about 12,500 ha) and highest

annual rate of deforestation (i.e. 0.1%) among the studied countries. As Guyana and Suriname,

Amapá economy trade is dominated by same extractive industry i.e. the gold exportations that

account for half of the total exported product of Amapá and thus contribute to forest losses.

Moreover,evenifagriculturalsectorcontributestoonly2.3%ofgrossdomesticproduct(Vianaand

al.,2014)geographicalexpansionofthissectorresultsinlargedeforestationarea.Inaddition,asthe

rest of the Brazilian Amazonia, the extensive cattle ranching activities explain a significant part of

deforestationinAmapá.Agriculturalproduction,dominatedbycassava,maizeandrice,andlivestock

productsaregearedmostly for internal consumption.Notehowever that, according toVianaetal

(2014), it exist an emerging agribusiness sector in Amapá that should result in an expansion of

soybeanproduction areas in the following years.With the economic development andpopulation

growth several infrastructures as roads, hydroelectric dam, and others settlements were

implementedduring theperiodand leddirectlyand/or indirectly todeforestation.Note thanover

the studied period, no increasing trend appears on time series of deforestation but rather high

volatilityintheannualforestlosses(cf.figure1).

FrenchGuianahastheparticularitytobeadevelopedcountrydepartment.Howeveronthecontrary

of the French Mainland, French Guyana was characterized the last decades by a high economic

growth (4%)withmoderate inflation rateabout2%, combined topopulationgrowth (>3%)among

the higher of the world (INSEE, 2008). French Guyana is thus characterized by a small but high

growingeconomyonalargeterritorymakingitcomparableintheeconomicdynamicspointofview

to its neighboring countries.Moreover, similarly to the others studied regions, FrenchGuiana has

highforestcover(>90%)thatencompassnearof95%ofprimaryforest.FrenchGuianahasarelative

stableannualdeforestation(cf.figure1).ExceptforestrysectorwhichispoorlydevelopedinFrench

Guiana, same main causes of deforestation occurred in French Guiana: gold mining activities,

agricultural expansion and infrastructural and settlement development. Gold mining is mostly

located inhinterlandwhilemostofagriculturalactivitiesare implementedonthecostalzonesand

along the river close to the residential areas. Agricultural sector is dominated by rice andmanioc

production around the concentrated population areas. However, if dynamic is similar, economic

pattern of French Guiana are quite different to its neighboring countries especially considering

Guyana and Suriname. Indeed, the gold mining sector is suffering from difficulties to conciliate

modernization and environmental protection as amajor preoccupation of France and the primary

sectorisdecreasingandrepresentsonly4%ofGDP.Todaymostoffoodcommoditiesareimported.

AsaconsequencethetradebalanceislargelyindeficitinFrenchGuiana.FrenchGuianarecordedthe

lower annual deforested area (about 2,700 ha) even if the annual rate of forest loss is similar to

othersstudiedregions(i.e.0.03%)(Cf.figure1).

Evenifitexistsdifferencebetweenthecountries,wenotegloballyhomogeneityintermsoflanduse

sectorsthatmayfacilitateregionalinteractionsandfinallyspillovereffectsofdeforestation.Notean

economicspillovereffectcanbepositiveornegative.Incaseofpositivespillovereffect,adecreasing

(or increasing) of deforestation of a country should lead to a decreasing (or increasing) of

deforestationinneighboringcountry.Wesupposehoweverthatglobalspillovereffectintheregion

shouldbenegativeconsideringsimilarcharacteristicsofstudiedcountriesandforallreasonsthatwe

attempttoexplaininthenextsections.Moreparticularly,inviewofcharacteristicsofthecountries

encounteredintheregion,weidentifiedtwopossiblecausesthatmaydumpdeforestationbetween

countries.Indeed,asthelandusesaresimilarinthecountrieswesupposedthatexistacompetition

for that resulting fromboth, amarketeffect andadisplacementof activities. Thus,wedeveloped

andillustraterationaleregardingeconomicregionalinteractionsforthesepossiblespillovereffectsin

thenextsection.

III.2 RegionalinteractionsintheGuianaShield

According to most of authors, neighboring countries with similar economic and natural

characteristicsmay interact inacompetitivewayandbesubject toenvironmental spillovereffects

(Lambin,2011).Eachstudiedcountry,beingingrowingprocessespecially,seekforcompetitiveness,

particularlyinextractiveindustryandagriculturalsector,throughscaleupactivitiesthatmayresults

implicitly in a competition for cleared land between them. In this case, regional interactions are

competitive meaning that what is not produce by one, should be produced by others, at least

partially. As a consequence, substitute strategy may take place and an increase (or decrease) of

deforestation in a country results, or it the result of, a decrease (or increase) of deforestation in

neighboringcountries.Atthemicroeconomicscale,theagentsarealsoseekforcompetitivenessand

may displace their activity on the territory with more economic incentives, which conduct to a

spillovereffecttoo.Thiseffectseemsparticularlytrueforsmall-scalegoldminingactivities.

This feeling is reinforced by observations made on dynamics of deforestation in the Guianas

(includingStateofAmapá).Indeed,accordingtothedeforestationtimeseriesovertheperiod2000-

2010,thesumoftheannualaveragevariationofdeforestationofeachcountry is20%higherthan

theannualaveragevariationoftotaldeforestationonthewholeofthestudiedarea.Inotherwords

the decreasing (or increasing) of deforestation in a countrymay have beenpartially compensated

and/orexplainedbyanincreasing(ordecreasing)ofdeforestationintheotherscountries.Therefore,

beforetomodelingandestimatesthepossiblespillovereffects,weattemptinthissectiontoprovide

concretedriversfortheseregionaleffects.

III.2.1 Economicinteractionsandmarketleakageeffect

Thefirstdevelopedeffect is linkedtomarket leakageresultingfromboth internationaland

regionalcompetitioninextractiveandlandusesectors.Thiskindofeffectassumesthataleakageof

deforestationmayresultfromachangeofsupplyanddemandequilibrium.Therefore,assumingthat

uncoordinated economic and environmental policies in the Guianas (including State of Amapá)

changetherelativecompetitivenessofcountries,demandforcleared land inonecountrycouldbe

correlatedtothedemandofclearedlandofothersneighboringcountries.Competitionforlandcan

beatransboundaryissuebetweenGuianaShieldcountriesandbecauseofcross-countrycompetition

within the Guiana Shield on international and/or regional trades, leakages “or indirect land use

changes”canarise(LambinandMeyfroidt,2011;Strassburgetal.,2013). Forexample,atregional

scale this spillovereffectcan results inwasteofcultivatedareasand thusdeforestation. Indeed,a

country should increase its cultivated area to respond to an annual growing demand (regional or

international) in a landuse sector andbe able to satiate this demand the following yearswithout

increasecultivatedarea.Howeverifneighboringcountrycompetesinthesamesectors,itundertakes

competitiveness efforts and in turn satisfies this same demand the next year and increase its

cultivatedarea.

Competitionon the internationalmarket isgenerally reinforcedwithgeographicalproximityof the

supplier countries as well as their economic similarities. Indeed while proximity of countries

annihilates thedifference in the transportation cost to supply theworldmarket, similar economic

structures(includingnaturalresources)limitdisparitiesinproductioncosts(Aucklandetal.,2003).As

aconsequence,Suriname,GuyanaandStateofAmapá,involvedintheprovisionoftheglobalmarket

foragriculturalcommoditiesandmineralproducts,maybeindirectlyconfrontedeachotherinsuch

tradecompetitioninducingleakageeffects.

Indeed, Guyana and Suriname are considered among the largest supplier of rice among the ACP

countriesandtheysharedanexportationquotatotheEuropeanEconomicCommunityuntil20103.

EvenifSurinameseemslesscompetitivethanGuyanaregardingtotalexportedvolumes,asymmetric

trend in time series suggests, over the studied period, a possible direct competitiveness in rice

exportation(cf.figure2).Consideringthatricesectoristhesinglelargestuserofagriculturallandin

Guyana and Suriname (Poerschke, 2005), this competitiveness in rice industry between both

countriescanimpacttheirrespectivedemandforclearedlandandthusresultindirectlyinaspillover

effect of deforestation. More recently, Suriname received increasing capital–rich country

investmentsforoilpalmandsugarcanelanddevelopmentwhileGuyanaisalreadyactivelyinvolved

in these productions (Latawiec, 2014). Suriname could challenge in the next years Guyana bygrabbingaportionofthis internationaldemand.NotethatFrenchGuiana’sagriculturalexportsare

structurallyweakduetolowercompetitivenessthanitsneighborsparticularlybecausehighercosts

ofproductionandmorebindinglegislationthantheregionalaverage(FrenchGuianaandSuriname:

better…). As consequence, in french Guiana the rice sector is losing momentum since middle of

2000’s,even if it is still themainsourceofexportationswhereas thericeexportsofSurinameand

Guyana is instead increasing. State of Amapá is not involved in the exportations of agricultural

commodities as agricultural sector is poorly developed and intended mainly to supply the local

markets.

Mineralexportsareanotherpossiblesourcesofcompetitionthatmayresultsinspilloverbetweenall

Guianascountries(includingStateofAmapá).Indeed,mineralresourcesasgold,bauxiteandoilare

present in importantquantity in theGuiana shieldand sincemineral industry represent important

sourceofGDPforeachandespeciallyfromgold.Forallcountries(includingStateofAmapá)these

mineral exports are the main source of total exportations. Thus direct competition come from

mineral exportsbetweenall theGuianas countries (including Stateof Amapá). Competition in this

sector is not only production-driven considering scarcity of gold but comes from more to the

attractivenessofforeigndirectinvestmenttoconcededextractiveactivitytotheforeigncompanies.

Indeed competitiveness on the international goldmarket is cost-driven and thus depends on the

technologicalcapacitytolimitextractioncosts.

3Notehoweverthatsince1january2010,SurinameandGuyanabenefitsfromduty-freeandquot-freeaccesstotheEUmarket.

Figure2:GrowthinriceexportsofGuyanaandSuriname

Talking about large-scale gold mining activity, possible spillover effect comes from international

trade policy put in place by government (requested royalties, environmental regulation, …). For

example,mosteffortaremadesinceearly’s2000byGuyanaaswellasSurinametoattractForeign

Direct Investment (FDI) inmineralsector.Fromtimeto time, respectivegovernmentshadreduced

their royalty to motivate development of major mineral project and particularly attract foreign

investmentingoldsector.CompetitiontoattractFDIcanbegeneralizedatotherslandusesectors.

Indeed,Guyana,forexample,hadatotalinflowofFDIof165millionsdollarsandaninwardstockof

FDIof1billions.Amongtheseamounts,agriculture,forestryandminingarethetop3sectorsofthe

FDIsourcing.SurinameandAmapáStatesharethesamecharacteristicsintermsofFDIsourcingthat

mayconductincompetitionbetweenthem.

Same competitive effect seems to occur on the Guiana shield regional market. Indeed, regionaldemand over the Guiana Shield is increasing rapidly with the important economic growth of

countries. At the same time, regional economy is increasingly liberalized and transboundary

infrastructure are planned and developed (IMF, 1997; Gafar, 2003; Pavcnik, 2004). As Guianas

countries(includingAmapá)haveclimateandnaturalconditionssimilarities,eachmaybeinterested

inrespondingtoregionaldemandforgoodsandproducts.Moreover,noclearregionalcomparative

advantage in agricultural sector is yet drawn by one or another country. Therefore it exist a

competition on the provision on some agricultural products between countries that may lead to

short-term volatility in agricultural production of each country. As a consequence, as for the

international market, regional interactions seem to exist on regional markets that may result in

spilloverofdeforestation.

Forexample,as for the internationalmarket,competitionforriceproductionexistsat theregional

market. Indeed, rice is the most important crop in Suriname and represents thus the higher

cultivated area of the country. As a consequence regional rice demand is mostly satiated by

Surinamethatisconsideredasa“breadbasket”oftheCaribbean(Latawiec,2014,CIS,2010).Butasalready underlined, specialization is not reach and neighboring countries and particularly Guyana

challengesthusthecompetitivenessofSuriname.Therefore,evenifGuyanaappearsin2012asthe4

main export partners of Suriname, Guyana has also an important rice economy. Consequently,

conjecturalcompetitionbetweenbothisleadingtosignificantfluctuationinriceproductionofeach

and thus in the growing of cultivated area. Beyond the rice product, this competitiveness can be

generalized to others food products as suggest by the opposite trends of bi-lateral import/export

foodproductsbetweenGuyanaandSuriname(cf.figure3).

Figure3:GrowthinbilateralexportsoffoodproductsbetweenGuyanaandSuriname

Furthermore, if Guyana and Suriname compete to satisfy their nationalmarket, competition is to

satisfy othersGuiana Shield countries. For example, both Suriname andGuyana have each partial

preferential agreement with Brazil particularly to access to the agricultural and food market.

Commercial trade between Suriname and Brazil (including State of Amapá) have been rapidly

increasingduringthelast15yearssurgingfromnear11$millionsin2002tomorethan60$millions

in2012(Abdenhur,2013).Inthesametime,agriculturalandfoodproductsrepresents65%oftotal

Guyana’s export to Brazil. Otherwise competition seems to be aswell importantwith Brazil since

agricultural products represents 50% of Guyana’s importations from Brazil. Note that difficulty to

accessbyroadstotheBrazilfromGuyanaandSurinameconductalargepartoftradetobeexchange

byboat.IntheseconditionAmapáStateisoneoftheclosestpathforeconomictradebetweenBrazil

and Guyana or Suriname suggesting reinforced competition between Amapá and other Guianas

regioncomparedtotherestofBrazil.NotethattheongoingredevelopmentoftheSantanaport in

Amapáshouldintensifythesetraderelationsinthefollowingyears(Viana,2014).

Amapá also receives products from French Guiana and promote greater trade relation for

agriculturaland livestockproductsprofitingparticularlyof theBR-156 roadsconnection.Note that

trade opportunities should be increases in the next years considering that BR-156 road is being

paved and the bi-national bridge completed (Viana et al. 2014). Even if French Guiana hasparticularitytobeaDepartmentofdevelopedcountrycomparedtotheothers,itparticipatesinthe

regionalmarketessentiallyon thedemand-side foragriculturalproducts.However importations to

French Guiana from its neighboring countries stay limited and hindered by a harder regulation

dependent of French and European legislations with respect to imported products (Boudoux

d’hautefeuille,2010).Thus,inordertosatisfyitsdomesticdemandFrenchgovernmentputinplace

differentmeasurestoincentivelocalproductiondevelopment.Forexample,agriculturalsubsidiesin

French Guiana increased from 2.5 euros millions in 2002 to more than 10 millions in 2010

contributingtothedevelopmentofdomesticagriculturalproductsreducingagriculturalimportation4

and dependency probably at the expense of neighboring products. Governmental authorizations

given to migrants to develop agricultural activity is another significant measure promoting local

production and limiting this French Guiana dependency to its neighbors. For example, lots of

Surinamese has migrated in the North-West of French Guiana to implement slash agriculture

practicessinceFrenchadministrativeauthoritygrantan impliedright.Notethatthismigrationwas

particularly severeduring theSurinamesecivilwar (1986-1992)and since lotof Surinamese family

have remainedon thewest of FrenchGuiana territory. Thus beyond food self-sufficiency function

agriculturalactivitiespracticesinFrenchGuiana,eveninformal,wasawayofintegrationformigrant

lookingforbetterlifeconditionandgivethemananchorintothedomesticmarket(Demaze,2008.).

Inthiscontext,morethanmarketleakageeffectofdeforestation,itisanactivityleakageeffectthat

may takeplaceespeciallyas regionalmigration is importantandas thepoliciesareuncoordinated

(mainlyinagriculturalsector).

In a genera perspective,while commercial flows do exist and are rapidly increasing in theGuiana

Shield,theyarestillhamperedbythelackoftransportinfrastructure,thenon-harmonizedregulation

andthedifferentlegislation(Bourdouxd’hautefeuille,2010).Howeveraccordingtotheliterature,a

significantpartofexchangesbetweenGuianaShieldcountriesareconductedbyinformaleconomy,

especially in the primary sector that may hide other potential spillover effects not cought when

analysing formal exchanges. For example, Surinamese Office of Statistics estimates informal

economy to be 14% of GDP, while a very significant part comes from agricultural and extractive

resources. Here transboundary exchanges exist and are volatile with regularly changing direction

flow resulting mainly of the reciprocal value of currencies because of an environment of the

controlledinflationininformaleconomy.Theseinformalexchangesarebothpresentinthetradeof

agriculturalcommoditiesthan intheexchangeofgold.OverallGuianas(includingStateofAmapá)

thegoldisincreasinglysmuggled.Thusonepartofproductionofsmall-scalegoldmininginacountry

issoldinotherneighboringcountryofGuyanaShieldofferingmorebenefitsforresale.Thesemarket

leakageeffectsshouldbeall themore importantthat landusesectorialpoliciesareuncoordinated

amongGuianascountries(e.g.differenttaxes,subsidies,etc.).

III.2.2 ActivityshiftingleakageandSmall-ScaleGoldMining

Beyond the possible market leakage effects due to international and regional market

competition, informal gold mining activities seem to represent another major leakage of

deforestation taking the formthis timeofactivity shifting5. Indeed,according to the literatureand

the regionalexperts, the small-scalegoldmining (SSGM)activity isa transboundary issueover the

GuianaShield.Indeed,duetothesimplicityofoperationsandabsenceofmeaningfulinvestment,the

activity can easily be started, stopped and moved (Roopnarine, 2006). Thus SSGM can beimplemented rapidly throughout the Guiana shield especially by migrant miners that encounter

difficultiestoimplementtheiractivitiesintheircountryoforigin.

Indeed, the displacement of SSGM activities by migrants over the Guiana shield is an important

phenomenonthatstartedsincethanBraziliangovernmenthasimplementedstrictermonitoringand

regulationonSSGMin1970s.SincethenlotofGarimpeiros(i.e.Brazilianminers)areexpelledfrom

naturalreserveareasandindigenousterritoriesinAmapá.Asaconsequence,Garimpeirosstartedto

displacetheiractivitybeyondborderonthewholeofGuianaShield (Weigand,2009).TheBrazilianmigrantsrepresentnowaboutthree-quarterofsmall-scalegoldminersinSurinameandlargepartof

4Importationsofagriculturalproductshavebeenreducedfromnearto10millionseurosin1993to7.5millionseurosin2005(source:http://www.insee.fr/fr/regions/guyane/)5AccordingtothedefinitionofWunder,«activityshifting»isaphisycaldisplacementofactivity(Wunder,2005)

the 10,000 illegal gold miners in French Guiana (Heemskerk, 2011). In addition this shifting ofBrazilianminerswasaccompaniedtosmallernumberofBraziliansmall-farmersworkingtemporarily

inthemines. In lowerproportion,Garimpeirosarealsopresent inGuyanaaswellastheGuyanese

arepresentininformalgoldminingofSurinamandFrenchGuiana.Guyanaalsoreceivesinformally

Surinamesegoldminers.

Inthiscontext,weassumethatmayexistadisplacementofdeforestationacrosstheregiondueto

the displacement of SSGM activities by migrants. In other words some migrants may shift their

activityfromtheircountriesoforiginstoanothercountry.Ifthebehavioroftheformalsectorfrom

goldmining companies is essentially demand-led, this informal SSGM activity tends to be supply-

driven (MacMillan, 1995). As a consequence,while the implementation of large-scale goldmining

activity mainly depends on the international price of gold and national arrangements with host

country(tax,royalties,etc.),thedecisiontoimplementthesmall-scaleactivitiestakesintoaccountin

additionthelocaleconomicconditions.

Assuming that migrant miners seek to optimize their revenue from the gold selling on informal

domestic market mainly two factors may encourage gold miners to displace their activity: the

geographicalproximityofwelcomedlandandthedomesticgoldprice.Thelastisdirectlyrelatedto

theinternationalgoldpriceandtothenationalrealeffectivechangerate.Iftheinternationalpriceof

gold is common over the Guiana shield and should homogeneously stimulate the national gold

productions,therealeffectivechangerateshouldplayasan importantfactor inthe localizationof

activity.Therealeffectivechangerateistheweightedaverageofacountry’scurrencyrelativetoan

index of other currencies of major trade partners adjusted for the effect of inflation. Thus

theoretically thisallowsestimating if it ismoreprofitabletoproduceandsell thegold inaspecific

country and consequently the opportunity to shift SSGM activities from a country to another.

AccordingtoHeemskerk(2001)thisarbitrationseemsveryimportantinSSMGsectorassumingthat

the monetary devaluation and rising consumer prices may have been more important than for

example the absolute lack of jobs in making the decisions about participation in gold mining. Of

course national restrictions on land use accompanied by repressive policies may encouraged

populationtodisplacetheiractivityoutsidetheboundaryandthusreinforcedeffectduetoeconomic

incentives.

Finally, all previously described transmission channels for regional spillover effect of deforestation

are assumptionsmade regarding regional context. No scientificway exists to estimate precisely if

theseeffectsoccurredastheyaredescribedhere.Howeverstatisticaltestscanbeconductandcan

allowtorejecttheseassumptionsornot.Thusarobustscientificapproachisdevelopedandusedin

thenextpartinordertotestinageneralwaytheseeffects.

IV| Theoreticalandempiricalmodel

Themainquestionofthisstudyistoknowwhetherornotregional interactionsareexisted

betweenGuianaShieldcountrieswhentalkingaboutdeforestation,i.e.whetherornotdeforestation

fluctuationinacountrycanbeexplainedbydeforestationfluctuationinothercountries.Thusafter

developedandillustratedinthepreviouspart,thepresupposedcausesforregionalspillovereffects,

thispartintroducesthescientificapproachusedtotestregionalspillovereffectofdeforestation.For

thatwedevelopfirstlytheoreticalmodelofdeforestationinsidetheGuianaShieldandaddthetwo

spillover effects as previously defined. Then, the theoreticalmodel is empirically tested (i.e. using

pastobservations) inorder to rejectornot theexistenceof thesespillovereffects into theGuiana

Shield.

IV.1 Theoreticalmodelofdeforestationandleakageeffects

IV.1.1 Modelofdeforestation

Before conducting the empirical analysis, a theoretical model was initially developed as a

demandfunctionforclearedland.Asusualineconomic,thedemandfunctionsaremostoftimenon

linear.ThusthedemandfunctionforclearedlandintheGuianasisformalizedasfollows:

D = !!!!

WhereDisthedemandforclearedland,Ktheunderlyingcausesandβtheirelasticity.Accordingto

thedescribedeconomiccontextsinthepreviouspartandtothemostoftheliteratureondriversof

tropicaldeforestation,weidentifiedfourmainsunderlyingcausesofdeforestation(Kaimowitzetal.,

1998)thatmayimpactcommonlythefourregions.

Firstly and according to Rudel (1989) and Rudel et al. (1996, 1997) large compact forest are less

accessible andmore difficult to clear compared to areas characterized by high fragmented forest

landscape. Indeedclearing largescaleof compact forestneed large investment incapital-intensive

techniquethatareonlywithinthereachoflargeeconomiescountries.ConsideringHFLDcountriesof

Guiana Shield that are starting their forest transition and are subject to low income (except for

FrenchGuiana),weassumethatdeforestationisslowedbylowaccessibilityonlargecompactforest

area. Deforestation is expected to accelerate in the following years with the decline of forest

landscape.

Secondly and according to the literacy on modeling tropical deforestation, the economic

developmentisassumedtohaveanambiguousindirecteffectondeforestation.Ononehand,atthe

firststageofdevelopment,morenationalincomemayresultinmoreinfrastructuralinvestments,as

roadsandsettlements,whichresulttoanincreaseofdeforestation.Further,morenationalincome,

particularly for developing countries, conduct generally to more demand for agricultural

commoditiesandasaconsequencetoanincreasingdemandforlandasneededfortheagricultural

expansion.Ontheotherhand,national income increasingresultsstepbysteptoaspilloverof the

economyfromintensivelandsectorasagriculturaltoothercapital-intensivesectorasmanufacturing

industry and service. Consequently growth of national income is not supply by an increasing of

agriculturalproductionthatstabilizesandreducesthedemandforland.Deforestationisdecreasing

with growth of national income. Further, some authors have advanced that environmental

considerationare increasingwith thenational incomeand thusdecreasingpressureon forest. For

example, FrenchGuyana, as department of high-income country, is submitted to an increasing of

law,regulationandcontrolaboutenvironmental issuefromnationalgovernment,whatconstraints

tomoreattentionandcertainlylessdeforestationthanitsneighboringcountries.Finally,weassume

generally that economic development increases technological investment in agricultural sector.

ConsideringBorlaughypothesis, if agricultural productiondemand is fixed, a higher average yields

allowsbytechnologicalinvestmentreducesagriculturalareasandthuslimitdeforestation.

Thirdlyandaccordingtothelanduse/coverchangeliterature,populationdynamicsisconsideredas

the one of major force driving global deforestation (Houghton, 1991; Myers 1991). At macro-

economicscalepopulation increasealsocausesdirectdeforestationby increasing landdemandfor

humanimplementation(assettlement)thanindirecteffectbyincreasingdemandonlocaleconomic

sector in competition with forest (e.g. increasing demand for forest products and agricultural

commoditiesaswellasdemandforenergymainlysatisfiedbyhydroelectricdamimplementationin

theregion)(Carr,etal.2005).

Fourth,asdescribedintheprevioussectioneachcountryhasanagriculturalsectorthatmayhavea

directnegativeeffectonforestcover,evenifgrowingdynamicsseemsmoderateorevendecreasing,

Indeedforestareaisdirectlycompetingwithagriculturalexpansion.Asaconsequenceagricultureis

commonly considered, as the main drivers of deforestation in developing countries. This is

particularlytrueinlow-incometropicalcountrieswherefarmerspracticeextensivefarmingmethods,

while rapid loss of fertility force farmers tomove on and clear forestland (i.e. shifting cultivation)

(Angelsen et al. 1999). Thus even in a case of stabilized demand for agricultural commodities,

deforestation isneededtomaintainagriculturalproduction.Summarizing forestcanbeconsidered

as an input in agricultural production (Benhin, 2006) and consequently agricultural production is

usually a good index for measuring pressure of agriculture on forest in developing countries.

However, itcouldbenotethatwhenagriculturalproductionismarginally increased,thiscanresult

fromanincreasingagriculturalproductivitywithoutnecessarilyincreasingdeforestation.

Fifth,theGuianaShieldhasfiguredprominentlyintheglobalproductionofseveralpreciousmetals

includinggold that ismainlyexplainsby largegreenstone formationandcontributed to forest loss

(Mainardi,1996).Drivenbytheboomofgoldpriceandliberalizationofgoldtradesince1970’s(i.e.

post-Bretton-Woods),goldminingactivitiesintheGuianaShieldexpandedrapidly(Hammond,2007).

Sincegoldminingactivitiesarebecomean importantsourceof thenational incomeandespecially

for whole the Guiana Shield region it is assumed that deforestation from gold mining activities

represents the fastestgrowingdriverofdeforestation in theGuianas (WWF,2012).Goldmining in

the region ranges at different operational scales with a tendency towards larger operating for

exportspurpose(Rahmetal.,2015).Indeed,smallnumberoflargeroperationsisundertakenbutis

rapidly growing with the increase of production investment capacities, that started frommarket-

capitalizedand theopeningofoperations to the internationalmining company.Howeverartisanal

operations still themostpredominantoperating scale in theGuiana shieldand is characterizedby

labor-intensive process extracting relatively small volumes. Whereas large-scale activities are

growingwiththeincreasingmid-longtrendofinternationalgoldprice,smallscaleartisanalactivities

remainsverypresentandseemstobemoresensitivetothevolatilityof“domesticgoldprice”that

dependinadditiontothecountry'seconomicsituation(Heemskerk,2001).

Finally, as extractive industry and agricultural sector dominate globally the exports of Guianas

countries (including stateofAmapá), the termsof trade (that canbemeasuredbyexchange rate)

mayappearasanunderlyingcauseofdeforestation in the region. Indeed,assuming thathigh real

exchangeratepromotescompetitiveness,mostoftheauthoradvancedthatitmakemoreprofitable

to convert forest to others uses (Capistrano, 1990; Gullison and Lossos, 1993; Kant and Redantz,

1997,KaimowitzandAngelsen,1998).

Theseunderlyingcausesofdeforestationmay impactcommonlythestudiedregionsandtherefore

areimportanttotakeintoaccountinthemodeltoavoidthattheestimatedregionalsinteractionsbe

mechanically distorted in favor of a positive spillover (i.e. an increasing (or decreasing) of

deforestation in a country conduct to an increasing (or decreasing) of deforestation in the

neighboringcountries).

IV.1.2 Modelingregionalinteractions

Thereafter,thetwopresupposedregionaleffectswere introducedinthisdemandfunction.

The first is the general spillover effect of deforestation.Here is assumed that demand for cleared

land of a country (i) depends on the demand for cleared land of its neighboring countries (j) and

inversely. InotherwordweassumethatexistspillovereffectofdeforestationacrossGuianaShield

that presumably comes from competition for international and regional trade between countries.

Thesecondexpectedeffectisthatdemandforclearedlandduetosmall-scalegoldminingactivities

of country (i) is explained by the relative competitiveness of domestic gold price between

neighboring countries. To illustrate, migration from a country (i) to a country (j) should be

encouraged for relativedepreciationof the local currencyof country (j). This canbe the caseof a

relative increase of the money supply and/or higher relative inflation rate of the country (j)

comparedtothecountry(i).Inthiscase,minersofcountry(i)shouldproduceandsellgoldincountry

(j)tooptimizehiseconomicrevenue.ThisrationalbehaviorcouldbemoreimportantasfarasLowe

et al. (2005, Situation analysis report: small scale goldmining inGuyana) explains that “miners inGuyanahavesolidbasiceducationoratradeskill”andthat“mostparticipantsinminingexercisesa

choicebasedontheirassessmentofcomparativeeconomicadvantage“.Inotherwordifcountry(i)

hashigherdomesticgoldpricecomparedtoitsneighborsitshouldbeattractmoregoldminersfrom

neighboringcountries.Formallythedeforestation(asclearedlanddemand)forcountry(i)become:

!! = !!!!!!!!!!!

WhereDjandGjarerespectivelythedemandforclearedlandanddomesticgoldpriceofneighborj.

Togeneralizebeyondtwocountrieswecharacterizetheseleakageeffectsbydefiningtwoweighted

spatialmatrix toaccount forpurespilloverofdeforestationononehandand leakages fromSSGM

activitiesontheotherhand.

Thefirstistheweightmatrixsupposedtotakeintoaccountpotentialspilloverofdeforestationdue

to market leakages. According to the section IV.1, we assume that this spillover effect and the

intensity of cross-country interaction may be different following the economic similarities or

dissimilaritiesbetween countries (Hammadou,2014). Thereforeweassume that inside theGuiana

Shieldtheregionalinteractionsshouldbemoreintensebetweeneconomicallyproximatecountries.

To approximate this economic proximity and thus the degree of interdependence between two

countries we used economic size difference between countries. Therefore, to capture regional

interactionswedefine aweightingmatrix such that higherweights are assigned to countrieswith

moresimilareconomiccharacteristics(thatisexpressedbyGDPpercapita6),asfollows:

W!!" =1

(!"#! − !"#!)

W! =W!!! ⋯ W!!"⋮ ⋱ ⋮

W!!" ⋯ W!!!

6Wehad testedothersspatialweightmatrixespeciallybycombiningGDPgapmatrixwithgeographicalproximityand contiguity cross bordermatrix. All results obtainwith these others spatiallyweightmatrix are summarize infigure6.

Thesecondweightmatrixwasdevelopedtotakeintoaccountthepotentialleakageofdeforestation

duetoshiftofSSGMactivities.Whilemarket leakageeffectsbetweencountriesshoulddependon

economic similarities between countries, the activity shifting effect of goldmining activity should

respond to geographical criteria (as explained in section IV.2) and should be different following

geographicalproximityoftwocountries(d)7andthelengthofthecommonborder(l).Thereforeweassumethat,insidetheGuianaShield,regionalinteractionsshouldbemoreintenseifcountriesare

close and should increased with length of the common border. Therefore we define the weight

matrixfortakingintoaccountspatialeffectduetothedisplacementofgoldminingactivity(WG)asacombineddistance-boundaryweightsasfollows:

W!!" =!!"!!"!

!!!!!!"!

+ 1

W! =W! !! ⋯ W! !"⋮ ⋱ ⋮

W!!" ⋯ W!!!

Finallyweobtainthefollowingtheoreticaldeforestationmodel(asademandfunctionforcleared

land)accountingforthetworegionalinteractions:

!! = !!!!!!!! !!!!! !!

IV.2 EmpiricalEstimations

Afterdevelopedintheprevioussectionthetheoreticalmodelofregionalspillovereffect,this

section describe data and empirical strategy used to test this model and assumptions made

regardingthetwopresupposedspillovereffects.

IV.2.1 Dataandvariables

The explained variable of the model is the deforestation. Data of deforestation were

extractedannuallyovertheperiod2000-2011fromspatialHansendata.Thisallowsestimatingthe

model using same source of data for all regions and thus avoiding the estimation bias due to

heterogeneityindatasources.Indeed,theHansendatahaveadvantagetobetreatedfollowingthe

sameinterpretationmethodonthewholeworldregionsmakingdatacomparablefromoneregionto

another.Inthesamespiritandconsideringthatthestudiedcountrieshavenotthesameforestand

deforestation definitions, we have to fix common definitions to estimate a generalized model of

deforestationovertheGuianaShield.FinallyconsideringthatHansendatacansufferfrombiasdue

to seasonality effects or lack of high resolution data completed by low-resolution remote sensing

data, we used forest and deforestation definitions sufficiently restrictive to relate only true

disturbancesof forestcoverwithoutartifact.Thusonlypatchesofdeforestation largerthan0.5ha

7Distancebetweentwocountriesisapproximatedbythedistancebetweentheirrespectivecapitalcity.

thatoccurson forestareaencompassingmore than30%of treecoveroncontinuousareashigher

than 1 hectare,were taken into account. Forest area included in themodel to account for forest

accessibility,areextractedfromthesamesourceusingthesamemethod(Hansen,2013).

Thevariableofagriculturalprice iscompiledannuallyastheagriculturalproducerprice indexfrom

FAOstatforSuriname,Guyana,Amapá(asastateofBrazil)andFrenchGuiana(asadepartmentof

France) (source: FAOstat, 20158). The variable of population is the total population data of each

region that comes fromFAOstat forGuyana, SurinameandFrenchGuiana (source: FAOstat, 2015)

andfromIPEAfortheStateofAmapá(IPEA,20159).ThevariableofGrossDomesticProductcomes

fromWorldBankdatabaseforSurinameandGuyana(WorldDataBank,201510)andfromINSEEand

IPEA for FrenchGuiana and State of Amapá respectively (sources: INSEE, 201511; IPEA, 2015). The

exchange rates used asmeasure of international competitiveness are compiled fromWorld Bank

database(source:WorldDataBank,2015).

According to the literature, the domestic gold price variable is estimated by two variables: The

internationalgoldpriceandtherealeffectiveexchangerateincludinginflationandcurrencychange

effects (Baur, 2013). The international gold price is sourced from World Gold Council database

(source: World Gold Council, 201512) and REER index is coming from Bank for International

Settlements (source:BIS, 201513) andhasbeencombinedwithnominalexchange rate fromWorld

Bank(sources:WorldDataBank,2015).

Eachdata,summarizedinfigure4,weretransformedusinga3-yearsmovingaverageapproachover

theperiod2000-2011.Thisallowssmoothingdatatoavoidtoomuchvolatilityandheterogeneityon

timeseries.Thismethod isparticularlyrelevantwhensourcesofdataaredifferent.Moreover,this

methodallowsavoidingbiasintheestimationsduetonaturalandseasonalityeffectswhichcanbe

present in deforestation data. Finally these panel data are used to estimate themodel over four

regionsand8years(2002-2009).

Figure4:Summarydataandsources

8Dataavailableat:http://faostat3.fao.org/home/E9Dataavailableat:http://www.ipeadata.gov.br10Dataavailableat:http://databank.worldbank.org/data/home.aspx11Dataavailableat:http://www.insee.fr/fr/bases-de-donnees/?page=statistiques-locales.htm12Dataavailableat:http://www.gold.org/statistics13Dataavailableat:http://www.bis.org/statistics/eer.htm

Guyana Suriname State,of,Amapa French,Guiana GuianasDeforestation,(ha) Average 4,824 3,585 12,326 2,698 5,858

(Hansen,(2013) Min 3,751 2,622 11,041 1,889 1,889Max 6,684 5,744 13,872 3,159 13,872

Forest,area,(1000,ha) Average 19,054 13,838 12,211 8,165 13,317(Hansen,(2013) Min 19,053 13,835 12,208 8,156 8,156

Max 19,058 13,839 12,216 8,168 19,058Gross,Domestic,Product,(1000,$,constant,2005) Average 832,812 1,817,013 1,980,421 3,571,219 2,050,366

(World(DataBank,(2015;((INSEE,(2015;(IPEA,(2015) Min 813,771 1,490,353 1,281,183 2,034,588 813,771Max 883,821 2,116,108 2,834,754 5,128,380 5,128,380

Population Average 764,458 501,167 580,564 190,250 509,110(FAOstat,(2015,(IPEA,(2015) Min 751,000 480,000 516,694 165,333 165,333

Max 781,000 520,000 636,433 214,000 781,000Agricultural,Price,Index,($) Average 122 110 107 107 112

(FAOstat,(2015) Min 69 66 82 99 66Max 185 161 137 117 185

International,Gold,Price,($) Average(World(Gold(Council,(2015) Min

MaxExchange,Rate Average 199 2,67 2,38 0,81 51

(World(DataBank,(2015) Min 191 2,38 1,86 0,71 1Max 204 2,75 2,97 1,02 204

Real,Effective,Exchanche,Rate,Index Average 107 109 88 98 100(BIS,(2015) Min 100 99 72 91 72

Max 119 135 106 102 135Note:(Data(sources(are(in(brackets

1,023315593

IV.2.2 Econometricstrategy

Toestimatetheparametersofthedeforestationfunctiondevelopedintheprevioussection

weusedapaneldataeconometricstrategy.Thuswefirstlinearizedthedemandfunctionforcleared

land(i.e.deforestation)developedinsectionV.1usingnaturallogarithmtransformationasfollows:

ln !!,! = ! + !!!

!" !!,! + ! !! ln !!,! + ! !! ln (!!,!)

Where t= [2002,2009] is theyearand i=[1,4]and j=[1,4] the regions,with i≠ j.D is theexplainedvariable deforestation. c the constant term and β are the coefficients of the logarithm of the Kexogenousvariables (i.e.population, forestcover,GDP,Agriculturalprice,goldpriceandexchange

rate). ρ and WD are respectively the coefficient and the normalized weighted matrix of theendogenous lag variable. WG is the spatially weighted matrix of the domestic price of gold Gnormalizedandαtheassociatedcoefficient.

Firstly,beforetestingspatialautocorrelation,weestimatedthefollowingmodelusingtheOrdinary

LeastSquares(OLSmodel)method:

ln !!,! = ! + !!!

!" !!,! + ! !! ln !!,! + ! !! ln !!,! + !!,!

!!,! ~ !!"(0,!!)

Where η the error terms independent and identically distributed. Note that according to the

Hausman test and F-test we reject respectively fixed effect and random effect specification.

Furthermorenormalityandhomoscedascticityofresiduesarenotrejectedaccordingtorespectively

theBeraandJarquetestandwhitetest(cf.figure6).Wethentestedspatialeffectsusingboththe

Moran’I and SARMA tests that confirm the presence of spatial autocorrelation (cf. figure 6).

Therefore inordertodetecttheappropriateformofspatialautocorrelation,weusedthecommon

sequential test series outlined in Anselin (Anselin and Florax, 1995). For that we estimated two

othersmodels.We firstlyestimatea spatial autocorrelationmodelwithanendogenous spatial lag

variable(SACmodel)inthepreviousmodelasfollows:

ln !!,! = ! + !!!

!" !!,! + ! !! ln !!,! + ! !! ln (!!,!) + !!,!

Second,weestimatedmodel includingspatialerrorswithoutendogenousspatial lagvariable (SEM

model)asdescribebelow:

ln !!,! = ! + !!!

!" !!,! + ! !! ln (!!,!) + !!,!

!!,! = ! !!!!,! + !!,!with !!,! ~ !!"(0,!!)

with ε the error terms spatially correlated. Comparing the significativity of Lagrangian Multiplier

spatial lagtestandLagrangianMultiplierspatialerrortest intheirstandardandrobustversion,we

finally chosen a mixed-regressive-spatial autoregressive model with a spatial autoregressive

disturbance (SARAR model) using maximum Likelihood estimation method. The model combined

thusSEMandSACandcanformallybewritten:

ln !!,! = ! + !!!

!" !!,! + ! !! ln !!,! + ! !! ln (!!,!) + !!,!

!!,! = ! !!!!,! + !!,!

Note that beyond expected spatial effect, the estimations highlighted presence of positive spatial

autoregressivedisturbance.ThismaysignalforcommonshocksofdeforestationinsidetheGuianas

(including Amapá state) that are not taken into account in the model. As weighted matrix of

disturbances is represented by weighted GDP per capita, these common shocks may be due to

regional or global economics that affected countries in same way (international price shocks,

international crisis, etc.). Even if data of deforestation were transformed in the 3 years moving

average, some seasonality effect can persist and explained one part of this common disturbance

inside the Guianas (including State of Amapá). Robustness checking has been conducted using

different spatially weighted matrix and by estimating each spatial models by fixed effects. No

significantdifferenceintheparametersofinterestwashighlighted.

V| Resultsandimplications

V.1 Econometricresults

Beforetodescribetheestimatedregionalinteractionseffects,themainpurposeofthisstudy,

the followingsectiondescribe themain resultsof thedeforestationmodel regarding the fitnessof

theestimationsandthestatisticrelevanceofunderlyingcausesintroduced.

V.1.1 Resultsofestimateddeforestationmodel

Estimations of the deforestation model are globally significant and explain about 94% of

variations of deforestation across theGuianas (including Amapá State) (cf. figure 5). All results of

econometricmodelaresummarizedinfigure6.

Thevariableofagriculturalpriceistheonlystatisticallynon-significantvariableprobablybecauseof

lackofconsistentofdatasourcesacrosstheregionand/oracompositepricevariablethatdoesnot

representrealincentivesforagriculturaldevelopmentintheregion.Furthermoreprobablyimpactof

agricultural development is partially captured by global economic development already present in

themodel.

Figure5:Observeddeforestationvspredictedvaluesbythemodel

As an interesting result, the estimations highlighted an inverted U-shaped relationship between

incomegrowthanddeforestation. In the literature thiseffect isnamedtheEnvironmentalKuznets

Curve relationship (Koop, 1999; Culas, 2007; Choumert, 2013) and assumed that from a certain

incomelevel(estimatedhereto4,000$atconstant2005value)amarginalincreasingofincomelead

tolessdeforestationcomparedtothefirststageofdevelopment.NotethatexceptforGuyanathat

not reaches this turning point, all others nowhave. Suriname andAmapáhavebeen reached this

4000$turningpointattheendofthestudiedperiod(i.e.near2010).FrenchGuianawaswellabove

thatpointthroughoutthestudyperiod.

Note also that gold price appears as the main explicative variable of the estimated model of

deforestation. Indeed following theestimations the increasingofgoldprice in thepast couldhave

contributedfrom50to70%ofdeforestationovertheperiod.

Finally, as expected, population and exchange rate are statistically significant underlying cause of

deforestationinGuianas(includingAmapáState).

Abscissa represents theobserveddeforestationdata for all countriesand for all years.

Ordinatearethepredictivevalueofdeforestationbythemodel.Moreobservationsare

closetothebisectrix,thebetterthequalityofthemodel.

Figure6:Econometricresultsandstatisticaltests

Hausman'test'chi2' 0.73(0.8662)

Normality'test'Bera'&'Jarque'LM'test 0.9820(0.6120)

Heteroscedasticity'White'test 7.24(0.5108)

Statistical'tests

As'appropriated'standardized'zFstatistic'and'tFstatistic'are'reported'in'brackets

spatial'autocorrelation'tests

Weight'matrix SARMA'test LM8LAG LM8ERR RLM8LAG RLM8ERR

W1 8.0654'** 1.8256 3.4265'* 4.6389'** 6.2398'**

(0.0313) (0.1766) (0.0642) (0.0177) (0.0125)W2 7.0914** 2.5370 4.3118** 2.7796* 4.5544**

(0.0288) (0.1112) (0.0378) (0.0955) (0.0328)W3 5.0359'*** 2.5670 5.6381** 10.6740'** 8.1070***

(0.0048) (0.1091) (0.0176) (0.0248) (0.0044)Note:'***,**,*'denote'the'significance'of'parameters'at'1%,'5%'and'10%'respectively.''As'appropriated'

standardized'z8statistic'and't8statistic'are'reported'in'brackets

Model&of&Deforestation SARAR$W1 SARAR$W2 SARAR$W3 SAC$W1 SAC$W2 SAC$W3 SEM$W1 SEM$W2 SEM$W3 SARAR$W1&(FE) SARAR$W2$(FE) SARAR$W3$(FE) OLS

Forest$area 56.5239*** 57.1606*** 57.3443*** 56.0555*** 55.7540$*** $54.3172*** 57.51486$*** 57.257568*** 57.063122$*** $84.88439$ 70.49606 62.24534 $57.198766***(35.65) (&310.28&) (319.34) (310.06) (311.03) (313.03) (319.29) (317.86) (316.80) (1.03) (0.89) (0.77) (311.10)

GDP 39.3881*** 37.8555*** 33.8739*** 22.5331*** 21.1723$*** 17.8148*** 32.39062$*** 33.54321*** 32.57827$*** $43.32166$*** 45.90984$*** 49.90416*** 27.27592$**(258.18&) (113.98) (343.82) (4.44) (4.93) (4.90) (579.20) (210.57) (286.72) &(4.98) (5.17&) (4.73) (2.20)

GDP2 50.9257*** 50.8914$*** 50.7934*** 50.5337*** 50.5007$*** 50.4200*** 5.770227*** 5.7916152***5.7629076$***$ 51.02987$*** 51.093994$*** 51.193797*** 5.6502839**(366.92) (367.71) (370.60) (34.53) (35.01) (34.96) (357.93) (349.16) (373.30) (34.96&) (35.17) (4.67) (32.24)

Population 3.1542*** 3.2188$*** $3.2277$*** 2.4336*** 2.3460$*** 1.9223$*** 3.089637*** 3.066461*** 3.11597$*** 7.16004$*** 7.582604$*** 8.238846*** 3.031954$***(9.04) (10.15) (13.59) (13.24) (13.62&&) (14.44) (22.38) (22.62) (22.64) (4.95&) (5.02) (3.63) (13.57)

Agricultural$price$index 0.1156 0.0982 0.3256 0.0472 0.0704 0.1268$ 5.0426463$ .0280082 .3183754 .4061767$ .4043095$ .2330789 5.0256209$(0.18) (0.19) (0.71) (0.13) (0.23) (0.47) (30.09) (0.06) (0.72) (1.24) (1.41) (0.78&) (30.04)

Gold$price 4.7255*** 4.6023$*** $4.4682$*** 3.1064*** 2.9373$*** 2.5359$*** 6.107794*** 4.143093*** 4.300517$*** 4.015303$*** 4.365482$*** 4.898584$*** 4.040435$***(7.19) (8.61) (8.51) (4.92) (5.42) (5.61) (6.78) (9.33) (8.27) (3.25) (3.64) (3.97&) (3.09)

Spatially$lagged$Gold$price 54.2255*** 54.1941$*** 54.4128*** 52.9989*** 52.8752$*** 52.5448$*** 55.681283*** 53.862787*** 54.274605$*** 53.949212*** 54.307428$*** 54.641547*** 53.667286$***(34.44) &(34.49) (35.28) (34.08) (34.40) (34.60) (34.53) (35.36) (35.32) (33.14) &(33.52) (33.60) (32.73)

Exchange$rate 0.3112*** 0.3505$*** 0.4158*** 0.3193*** 0.3127$*** 0.2154$*** .3454506*** .3806487*** $.3997512$*** .244965 .1939502 .3010481 .3167192(3.67) (4.05) (6.41) (4.64) (5.36) (4.56) (5.49) (6.03) (6.54) (0.63) (0.53) (0.91) (3.03)

Rho 5.4037679$***$$ 5.259046$** 5.0572212 $5$.3646486***$5$.3917854$*** $5$.4637785$*** 5.5190449$*** 5.4477377** 5.5457887(33.09) (32.39) (30.37) &(33.07&) (33.65&)& &(3&3.12) (32.70&) (32.40) (31.44&)

Lambda .6371065*** .5954989$*** .5601038$*** .4501137*** .4708554*** .5402123*** .7657592$*** .7647325$*** .7770689***(5.73) (5.08) (4.89) (4.09) (4.45) (4.79) (11.18&) (10.80) (8.74)

Constant 5222.7903$*** 5251.4248$*** 5261.3875*** 5231.8726*** 5260.1641$*** 5264.1566$*** 5252.1439*** 5270.3629*** 5266.4677***$ 5201.7664(314.20) (320.07) (323.05) (33.05) (33.45) (33.64) (321.93) (25.66&) (320.73&) (31.59)

R2$adj 0.9446 0.9432$ 0.9402$ 0.9282$$ 0.9257 0.9166 0.9397 0.9395 0.9388 0.6086 0.5930 0.6113 0.9486Log$Lik 28.2099 28.3587 28.6174 25.1127 25.7214 25.8053 27.1327 27.9712 28.6058 32.6207 32.9857 31.7400

direct'effect

Fixed&effect Fixed&effect Fixed&effect

V.1.2 Resultsofestimatedregionalinteractionseffects

The estimation results do not reject the presence of regional interactions of deforestationoverthestudiedperiod.Thusthetwospillovereffectstestedinthismodel(i.e.themarketleakageeffectandtheactivityshiftingofSSGMactivities)appearasstatisticallysignificant.Figure 7 shows the leakage effect of deforestation related to gold mining activity. This effect iscapturedinthemodelbytheunequaldistributionofdomesticgoldpricesbetweencountries.Thusan increase in the domestic price of gold by one country simultaneously causes an increase indeforestationinthecountryandadecreaseindeforestationinneighboringcountries.Thiseffectiseven stronger than the countries are geographically close andborder. It shouldbenoted that theintensityoftheseleakageisnotuniformacrosscountries.Indeed,asshowninFigure7,theStateofAmapáistheleastaffectedbythiseffectfromneighboringcountries (i.e. in the welcome of these leakage effect) even though it is greater impact onneighboringcountries (i.e. in theprovisionof these leakageeffect). Incontrast,FrenchGuianaandSurinamewelcomedmoreleakageeffectsfromneighboringcountriesthantheywereoriginally.Notehowever that ifSurinameseemstobe impactedsignificantlyby leakages fromtheStateofAmapáand Guyana, it is at the origin of much leakage effect toward the French Guiana. French Guianareceives however amajority of leakage effect from goldmining activity that comes from State ofAmapá. Guyana seems suffer mostly to the leakage effects coming from Suriname and State ofAmapá.Itshouldbenotedthatthedomesticgoldpricetakesintoaccounttheeconomicconditionsofeachcountry. Therefore, a country becomesmore vulnerable to the leakage effect due to goldminingactivitywhen ithashighereconomicgrowth, inflation, rateofexchange,etc...comparedtootherscountries.Ofcourse,someeconomiceventsornewpoliticalevents(e.g.commandandcontrolpolicy,...)canincreaseorreducetheintensityofcross-borderdisplacementofgoldminingactivities.Thus,Figure7illustratesmost risky leakageeffectwhenapolicy, directly or indirectly affecting the incentives togoldmining,isconductedunilaterallywithintheGuianaShield.Moreover, thesecond leakageeffecttaken intoaccount inthemodel isnotrejectedandseemstoconfirmthepresenceof leakageofdeforestation linkedtothemarket.Overall, it isestimatedthattheleakageeffectisabout40%.Inotherwords,alittlelessthanhalfoftheincrease(ordecrease)ofdeforestation in one country is offset by a decrease (or increase) of deforestation in neighboringcountries.However, as illustrated in Figure 8, there is a large disparity in these interactions and effects ofleakagebetweencountries.Accordingtoourestimates,Surinameisthecountrywhoseitsvariabilityofdeforestationhasthehigherinfluenceovertheothercountries.Alargemajorityoftheseleakageeffectsofdeforestation (negativeorpositive) takeplacebetweencountries thatareeconomicallysimilar,andthustheseeffectsareparticularlystrongbetweenGuyanaandtheStateofAmapá.Thisconfirms that economic competitionbetween countries is adriverof leakageofdeforestation.Onthecontrary,theFrenchGuianaisthelessvulnerablecountrytotheleakageeffectofdeforestationfromitsneighboringcountries.ThiscanbeexplainedbythefactthatFrenchGuianahasarelativelydifferent economic structure and thus is less dependent competitiveness of its neighboringcountries.Onthecontrary,GuyanaandtheStateofAmapáarethemostsensitivecountriestotheseeffects.

TheseresultsshowedthepresenceofeconomicinteractionsintheGuianaShieldthatseemsleadtoenvironmental interactions between countries such as leakage effect of deforestation. Figure 8shows that the leakage effectsmay bemore intensewhen the economic and / or environmentalpolicies that affect directly or indirectly the land use sectors, are unilaterally implemented anduncoordinated.

Guyana SurinameFrenchGuiana

StateofAmapá

SSGMleakageeffect

+++

+

+

++++

++

++ ++

+++

++

Thefigure7illustratesregionalinteractionsofdeforestationduetogoldminingactivitiesinsidetheGuianaShield.Thecolorfularrowsrepresentthedirectionalflowofleakageofdeforestationduetoadisplacementofgoldminingactivitybetweentwocountriesandthewidthofthearrowstheintensityofthisleakageeffect.StateofAmapáprovidesthehigherestimatedleakageeffectduetogoldminingactivitywhereasitisthelessimpactedregionbythiseffect.SurinameandFrenchGuianaare themostsensible regionsto this leakageeffectbyhostingpotentiallythemostSSGMparticularly fromAmapáState.Guyana interacts themost(providesandreceivesleakageeffects)withSuriname.

Figure7:Regionalinteractionsofdeforestationduetogoldminingactivity

Guyana SurinameFrenchGuiana

StateofAmapá

MarketSpillovereffect

++++

+++

++

++++

++

+++

++

++The figure 8 illustrates regional interactions of deforestation inside theGuiana Shield. The colorful arrows represent the directional flow of negative spillover ofdeforestationbetweentwocountriesandthewidthofthearrowstheintensityofthisspillovereffect.Surinameprovidesthehigherspillovereffectamongthestudiedcountries.Onthecontrary,GuyanaandStateofAmapáarethemostvulnerablecountriesintermofhostingmarketleakageeffects.

Figure8:Endogenousregionalinteractionsofdeforestation

V.2 ImplicationsandperspectivesThisstudyprovidesafirstlineofthoughtonregionalinteractionwithintheGuianaShield.Throughascientific approach,we tried i) to identify the causes of regional interactions that could lead to adisplacement of deforestation, ii) to develop a deforestation model including these effect and ii)statisticallytesttheseeffectsfromhistoricalobservations.IftheresultsdonotrejectthehypothesisontheexistenceoftheseleakageeffectswithintheGuianaShield, they do not allow either to say with certainty that the effects of leakage are as we haveassumed and described. Future work should deepen and refine the knowledge of these regionalinteractions. Particularly, other factors of regional interactions (e.g. socio-cultural drivers), notincludedinthemodelduetolackofdataortechnicaldifficultiestoincludetheminmodel,couldbestudied.On theotherhand, thedifficulty tobuilda commonanduniformdatabase forall regionslimits the scope of the statistical results and the implications that can be drawn. Finally, if thestatistical techniques used are among the most recent, they do not allow, however, in goodgeneralization tool to assess comprehensively all the regional interactions effects, sometimes toocomplex or episodic. Nonetheless, according data and statistical techniques currently available,exerciseperformedinthisstudyprovidesafirstreliable lightingonregional interactionswithintheGuianaShield,andthusprovidesasolidbasisforfutureexplorationsontheseissues.Assawintheresults,regionalinteractionsintermsofdeforestationwereoccurredovertheperiod2002-2009 on the Guianas (including State of Amapá). As expected, estimations revealed twopredominantleakageeffects:aleakageeffectduetogoldminingactivityandanendogenousleakageeffectofdeforestation.Thefirsteffectseemsmostlydependingoneconomicincentivestolocalizethegoldminingactivities.OnepartofthegeographicaldistributionofgoldproductionattheGuianaShieldscale(particularlySSGMactivities)wasexplainedby thedifference in thegrowth rateofdomesticgoldprice.As thegoldpriceisfixedinternationallyandthatinflationandexchangeratedependsonnationaleconomicconditions, this leakageeffectmaybehard tobrake.However someactivitiescouldhelp limit thiseffect. Indeed, individually this implicates to increase efforts to monitor and control gold miningactivities implementation andparticularly informal ones. Thus early detectionof the implementedactivities,especiallyusingremotesensingdata, isessentialtocontrolandquicklyintervenetofightagainst the rapid expansionof goldmining activities (eg. Rhamet al., 2015).However an efficientmonitor and control system implemented unilaterally by a country will riskmoving operations toother countries (e.g Harpie operation in French Guiana). The implementation of an individual butcoordinatedmonitoringandcontrollingpolicybyeachisthenaprerequisiteto limitriskof leakageduetogoldminingactivityshifting.Howeverthemonitoringandcontrollingmaybeefficientactivitytolimitexpansionofgoldminingbutitdoesnotallowspreventingtheirimplementation.Therefore,preventiveactionsareneededtopre-identifytheriskofgoldrush.Aswedemonstrated,leakageeffectcomespartiallyfromthedifferenceofdomesticgoldprice,sotheidentificationoftherisky episodes for a country should be take as regional issue. Thus at regional scale it would berelevanttodevelopandmonitoraearlywarningsystembasedforexampleona“domesticgoldpricemeasures” allowing to highlight an excessive cross-country variability. Beyond this study, this willneeded firstly to strengthen the understanding on underlying forces of SSGM sector and deepenknowledgeonrelationshipbetweendrivers(asdomesticgoldprice)andleakageeffectsandcollectappropriateddata(Clifford,2011).Secondly, leakageeffectcomesfromanendogenousprocessofdeforestationonthewholeregion.Estimations show that empirically while deforestation increased in a country, deforestation

decreasedinotherscountries.Thiseffectisevenstrongerthancountrieshaveeconomicsimilaritiessuggestingamarketleakageeffect.Indeed,accordingtothedriversofdeforestationintheGuianas(includingAmapá),thisexternalitymaycomesfromeconomiccompetitivenessonbothinternationalandregionalmarketsfromextractiveindustryand/orlandusesector.Uncoordinatedpoliciesonlanduse sectors are probably the main issue at the origin of this effect assuming that economic orenvironmentalpolicyledbyacountrymaymodifydistributionoflanduseofneighboring.ThiseffectconfirmsthatdeforestationistransboundaryissueovertheGuianas.Therefore,twopropositionsofjointactivitycanhelptobrakethisnegativeexternality:i)PromoteregionaldialogueandeconomicintegrationandcomplementarityespeciallyinthelandusesectorovertheGuianasandii)PromoteregionalapproachforfightingagainstdeforestationpolicyasREDD+.As already highlighted by Haden (1999), difference in population, wealth and political powerbetweenGuianaShieldcountriesdiscourageduptonowjointpoliticalaction.Consequentlyevenitisunreasonable to think to a full economic and political integration of Guiana Shield, several trackscould allow limiting negatives externalities of unilateral policies. First is the promoting of regionaldialogueoneconomicandpolitic landuse issues inorder to strengthenunderstandingof regionalinteractions.Second,cross-countryactivitiesshouldbepromotedasthelandusesectorappearsastransboundaryissueintheGuianaShield.Forexample,promotingregionaldialogueandpoolingoffund could allow to implement and financing regional projects as for example implementation oftransboundary protected areas and/or regional land use monitoring system. Thirdly, promoteeconomicdiversificationin landusebetweentheGuianaShieldcountriesandimprovetheregionaleconomic integration and complementarity could limit leakage effect. Thismay seemsparadoxicalbecauseresultsshowedthatregionaltradeisasourcesofleakage,butweassumeareverses“Dutchdesease”effect(CordenandNeary,1982)wherethreatsmainlycomesfromproductionvolatilityofland use sectors induced by competition rather than regional trade itself (Heemskerk, 2001).Therefore diversification of productions, especially in agricultural sector, can help to limitenvironmentalexternalitiesresultingfromhighcommoditiespriceinstability. Inthesametime,thepromotingofregionalintegrationandeconomiccomplementarity,throughforexampleincreasingofmulti-lateral trade agreements, promoting of cross-border communications infrastructure14 andharmonizationoftradelegislation,shouldreducenegativeexternalities(Abdenur,2013).More mechanically, estimations showed that deforestation increased in a country while thedeforestationdecreasedinotherscountries.Therefore,anefficientpolicyledbyacountrytoreducedeforestation may be appear inefficient regarding the regional scale. This is a strong implicationparticularlyfortheREDD+mechanisminwhicheachcountryisnowactivelyinvolved.IndeedincaseofuncoordinatedimplementationofREDD+wecanimaginethattheleastadvancedcountriesintheREDD+ process will welcome leakage and thus must redouble efforts to reduce both their owndeforestationanddeforestationdue to the leakageeffects. ThereforeeveryGuiana shield countryhavearealinterestthatREDD+policybethoughtatregionalscaleandactivitiesaresynchronized.InthiscontextregionalsharingofinformationandmethodsforeachstageofREDD+implementationisessentialespeciallyforneighboringandeconomicallysimilarcountries.Atthisstage,thesharing,theusingandthecoordinationofMRVsystemisaconcreteexampleofasynchronizedactivityinREDD+implementation.Finally, eachpolicy focusedon reducingdeforestationhas tobe coordinatedat the regional scale,otherwise stronger regulation in one country may pass unsustainable extraction onto neighbors(Veeningetal,1996).SustainableuseofnaturalresourcesattheGuianaShieldscalerequiressomekindofregionalagreementandcooperationonresourceusepolicy(Haden,1999).

14Wenotdiscussherethedeforestationdrivenbyroadsdevelopment(Soares-Filhoetal.2004,Soares-Filhoetal.2006,Nepstadetal.2002),butwefocusonopportunitytradeenhancementduetotransboundaryroadsasdriverofregionalintegration.

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