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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