Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
Originalscientificpaper
Croat. j. for. eng. 37(2016)1 71
Selecting Evaluation Indices for Cleaner
Production of Plantation Logging in Southern China with Fuzzy Clustering
Methods
Aihua Yu, Tom Gallagher, Chen Zhao, Yao Zhao
Abstract
Over the years, China has shown a significant reduction in natural forest resources, while the increasing area of plantations has made greater contributions to the huge demand for wood. In southern China, these new plantations have produced some problems such as environmen-tal hazards of logging operations and the most reasonable use of forest resources. A new management process called »cleaner production« is defined as reducing pollution from its source, increasing the rate of utilization of resources, and preventing the generation of pollut-ants in the production of services and products. In recent years, cleaner production has been widely applied to industrial processes such as agriculture and other environmental industries. In order to make rational use of plantation resources, to achieve maximum economic effi-ciency and to reduce or remove the environmental hazards of logging operations, it is necessary to carry out an in-depth study of cleaner production on the process of logging operations. This paper aims to establish an index system for cleaner production evaluation of plantation log-ging. The fuzzy clustering method was used to initially screen twenty-nine indices. After screening by the fuzzy clustering method, six first-grade indices and twelve second-grade important indices were selected as formal evaluation indices. The six first-grade indices are 1) cutting area design index, 2) logging operation techniques index, 3) ecological environmental impact index, 4) utilization of resource and energy index, 5) sustainable development index, and 6) safety production management and protection index. A maximum and minimum matrix method and a correlation coefficient matrix method were used to establish the similar matrix in the fuzzy clustering method. The screening results were then compared. The com-parison shows that out of the twelve second-grade indices, ten are similar and two are different. The results suggest that the fuzzy clustering method is reliable for screening indices.
Keywords: plantation, logging operation, cleaner production (CP), evaluation indices, fuzzy clustering method.
tional forest resources inventory in China (2009–2013). Whiletheadditionalplantationharvestinghasmanyeconomicbenefits, ithasalsoproducedsomenewproblemsinsouthernChina,suchassoilerosionandwastetimberintheland.Somecriterianeededtobedevelopedtomeasurewhatisimportantintheperfor-manceoftheseplantations.Thispaperfocusesontheindexsystemforcleanerproduction(CP),whichisanevaluationofplantationlogginginordertorationallyuseplantationresources,achievethemaximumeco-
1. IntroductionWithasharpreductioninthenaturalforestover
thepastdecadeinChina,agreateremphasishasbeenputonplantationstomeetlumberdemand.There-centlycompletedEighthNationalForest Inventorysupportsthisassumption.Specifically,theproportionofplantationharvestinghasincreasedfrom39%to46%,andplantationharvestinghasincreasedto155millioncubicmetersannually(theresultsoftheeighthna-
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
72 Croat. j. for. eng. 37(2016)1
nomicbenefit,andreducetheeffectsofloggingontheenvironment.
CPhasbeendefinedin»TheLawofthePeople’sRepublicofChinaonPromotionofCleanerProduc-tion«bycontinuouslyadoptingmeasurestoimprovedesign,usecleanerenergyandrawmaterials,intro-duceadvancedtechniquesandequipment,improvemanagementandmakecomprehensiveuseofresourc-esaswellasothermeasures.CP isalsodefinedbyreducingpollutionfromitssource,increasingtheuti-lizationratioofresources,andreducingorpreventingthegenerationanddischargeofpollutantsduringpro-ductionandinprovidingservices.Thegoalistoallevi-ateoreliminateharmtohumanhealthandtheenvi-ronment(ZhaoandZhang2003).Thisnotonlyappliestoindustrialprocesses,butalsotoagriculture,plan-ning,construction,andservicesasthisisageneralis-sue.Basedon the industrydefinitionofCP and the
characteristicsofplantationlogging,CPinvolvesim-provingcuttingareadesignwiththelocalconditions,usingcleanerenergy,developingreasonableloggingtechnologiesandequipment,improvingmanagementpractices,minimizingpollution,saving,andprotect-ingtheforestryenvironmentandworkers.Theresultsshould be to achieve sustainable plantations. Themethodologyistoproduceamodelforplantationre-sourcesbyusingawholeenvironmentalstrategyfortheprocessesandproductionsofplantationlogginginordertoreduceoreliminateharmtohumanhealthand theenvironmentwhile fullysatisfyinghumanneeds. CPinplantationloggingisanimportantmeansforachievingsustainabilityofplantations.Thefinalgoalistomaximizethebalancebetweennaturalre-sources,energyuseandeconomicbenefitsandmini-mizetheharmtohumansandtheenvironment,allinordertosaveresources,reducewasteandprotecttheenvironmentwhileharvestingtimber.
2. Contents of CP evaluationfor plantation logging
Cuttingareadesign,loggingtechnology,ecologicalenvironmentimpact,resourceandenergyuse,andsustainablebusinessesofforestprotectionandman-agementforworkers,allcontributetotheCP evalua-tionforplantationlogging.Sotheevaluationnotonlyconsidersthelongevity,gradualness,andcomplexityoftheimpactsofloggingonforestecologicalsystems,butalsothemethodandintensityoflogging,scaleofproduction,andthestabilityandrecoveryoftheeco-logicalenvironment.
2.1 Evaluations for cutting area designCPforcuttingareadesignshouldincludethefol-
lowing:designingenergysavingprocesses,promotinglow-energytechniques,shorteningtheworkingtime,simplifyingequipment,andpayingattentiontoen-ergymanagement.Inaddition,usingloggingequip-menttobenefittheecologicalenvironment,reducingloggingwaste and combustiblematerials of forestfires,andimprovingthebenefitsofloggingby-prod-uctswerealsoconsideredwhenevaluatingtheimpor-tance of CP.In designing of an index forCP, the following
shouldbeconsidered:therationalityofloggingpro-cesses(includingthetypeofloggingsystem,indexofloggingintensity,indexofoutturnpercentageandout-put,indexofroaddesign,indexofbucking,indexofskiddingandtransportation,organizationofloggingteamandsoon),advancementofloggingtechnology,reliabilityofpreventativemeasuresforcuttingrenew-ableareas,andoperationmanagementconsiderations.
2.2 Evaluation of logging technologyCutting,skidding,andtransportationarethreeim-
portantprocessesintimberproductiontechnologyinaloggingoperation.Variousworkingmethodsandequipment types areused in eachprocess, so thatmanydifferenttimberproductionmodelsandeco-nomicbenefitscanbecompared.Basedontheinves-tigationofplantationloggingintheFujianProvince,clear-cuttingwas themain typeof cuttingmethod withtheexceptionofthecasesprovidedin»Regula-tionofForestHarvestingandRenew«.Chainsawop-erationwasthemainequipmentmethodoffelling.AngularsawwasonlyusedinafewChinafircollec-tiveforestareaswithmostlysmalldiameterlogs(di-ameter<8cm).Methodsofskiddingincludedaerialcableway,walkingtractor,pushcart,dirtchute,andmanpower.Transportationincludedtruck,farmve-hicle,shipping,andrafting(manpower)(Zhangetal.2008).CPofloggingmainlyreflectedtheinterferenceandacceptabilitytotheenvironment.ReducedImpactLogging(RIL)wasbasedonprinciplesofscienceandengineeringandacombinationofeducationandtrain-ing(Dykstra2001).RILrequiredspecificforestinves-tigationproceduresbeforelogging,includingaplanandconstructionofloggingroadsandlandings,reli-ableways forcuttingandbucking, skidding felledtimbersalongskidroads,skiddingsystemsforpro-tectingsoilandvegetation,andevaluationafterlog-ging. Inaddition,RILalso includedthe impactsofloggingonlandscapes,biodiversity,vegetation,water,andsoil(Long2006).
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 73
Evaluationforloggingmainlyincludesthefollow-ingitems:advanceoftechnology,rationalityofloggingprocesses,coordination,economicbenefits,securityandworkefficienciesofahuman-equipment-environ-mentsystem.Theeconomicbenefitwasbasedonre-ducingworkingcostswhilenotdestroyingthefor-estedecologicalenvironment.
2.3 Evaluation for ecological environment impactsForasmallcuttingarea,soilandvegetationinthe
ecologicalenvironmentwere themostdirectly im-pacted.Whenevaluating loggingpracticessuchascutting,skidding,andtransportation,itbecomesin-creasinglyimportanttoalwaysconsidertheeffectsontheecologicalenvironment.Theunreasonablecutting,skiddingandground
disturbanceimpactmanypartsoftheecologicalenvi-ronmentwhenlogging,includingtheeffectsonsoil,reserve,rivers,biodiversity,loadsofCO2inair,land-scape,andregionalclimate.Therefore,acleanerlog-gingmodelmustminimizetheaboveeffects,inordertomaximizetheecologicalenvironmentalbenefitsoflogging in the forest.
2.4 Evaluation for resources and energy usePlantationswereresourceusedforanevaluation
oflogging.TheforestresourceinChinaisconsideredascarcerawmaterialwhenevaluatingitsbiologicalandeconomicbenefit.So,inordertosaveontheuseofrawmaterialsaswellasmaintainhigherworkingabilityandlevel,largeroutputsandfewerresourcewastesshouldbeexpectatedofplantationlogging.Energyuseintheloggingprocessesmainlymeans
thatfuelandlubricantswereconsumedbyequipmentthatwasusedincutting,skiddingandtransportation.So,low-energyandcleanenergyequipmentshouldbeused.Also,theindexofevaluationforresourceandenergyuseincludedstandingtreeutilization,volumeofwastetimberincuttingarea,volumeofwastetim-berinloadingbay,loggingslashutilization,andthefuelconsumptionofequipmentusedduringlogging.
2.5 Evaluation for sustainable businesses of plantationChinahasimplementednewpracticesinforestry
development,thoughmostlyforafforestation,becauseofmanyfactors.Thesefactorsincludeharvestinginareaswhereforestharvestinghasfollowedthetradi-tionalmethodsandpatterns.Itcanbesaidthatintheseareaswedonotseetheforestforthetrees.Inafforesta-tionareas,someareasweredeforestedwithoutrefor-
estationandhadfalsereporting,whichledtoinac-curateforestresourcedatabase.Thesefactorshaveledto low-quality afforestation, reforestation missingfromtheforesteachyear,andanincreasedimpacttotheenvironmentandecologicalbalance,whichhavecausedirreversibledamagetowoodlandsites.Log-ginghasanimportanteffectontheecologicalenviron-ment,suchasreducingthedepthoflitterfallandde-stroying the surface soil. Especially in spring andsummer,increasedsurfacerunoffusuallyresultsinheavysoilerosion.Moreover,pushingtimberbyskid-dingequipmentnegativelyimpactedthesurfacesoilanddamaged theyoung treesandreserves (Wang1997).Evaluationofplantationsforsustainablebusiness-
esincludedtheutilizationrateofwood,renewablerateofcutoverland,andthesurvivalrateofregenera-tion.TheaimofCPforplantationloggingwassustain-ablebusinesses.
2.6 Evaluation of safety production management and protectionTomakeplantation loggingcleaner,production
mustbecleanerfirst.Thisincludescleanerawarenessandactionoftheproducers.Thiswasparticularlyre-flectedintheaspectsofsafetyonproductionandpro-tection.So,bettereducationofworkerswasthebasisfor achieving CP. If working conditionswere im-proved,employeescouldbenefitfromincreasedsafe-tyandabetteroverallworkingenvironment.
CPcouldminimizetheinjuryrateofoperatorsinworkingprocesses.Meanwhile,CPwasalsofoundtoensureworkerrightsandsafety.OnlylaborprotectionwasabletobecometheessenceofCP.Soforevaluationofsafetyproduction,oneaspectoftheevaluatedcon-tentconsistedofreducingoperationdamageofwork-ersandensuringworkerrightsandsafety.
3. Evaluation indices
3.1 The principles for determining evaluation indicesTheindexsystemforCPevaluationofplantation
loggingconsistsofmanystructuredandgraduatedindicesthatconnectandcomplementeachother.Itisacombinationof theevaluationofsustainableandhighefficientutilizationlevelsofresourcesanditsin-dex,andhowitdirectlyaffectstheresultsofforestresourcesmanagement andutilization levels.Cor-rectlyimplemented,theresultisanaccuraterepresen-tation of CPforplantationlogging.Sustainabledevel-opmenttheoryandecology-economy-societytheory
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
74 Croat. j. for. eng. 37(2016)1
weretheguidesforsettinguptheindexsystemfortheCPevaluationofplantationlogging,andtheywerealsobasedonthefollowingfiveprinciples:1)Methodscombinedqualitativeandquantitative
analysis.Inordertoensuretheaccuracyandscientificnature of the results for evaluating the CPofplanta-tions,thequantityindexshouldbeselectedtosetupaquantitativelyevaluablemodel.Ontheotherhand,theevaluatedobjectswerecompletedproductionpro-cessesthatinvolvedvariousquarters.Therefore,theindex systemofCPwasa completedand intrinsicclosed contact system.Absolute quantity, relativequantity,averagesandotherindicescouldbeused,andsomeindicescouldalsobeusedasqualityiftheycouldnotbemanagedbyquantitativeanalysis.2)Independence:thestatusofthesystemcouldbe
describedbyanumberofindices,butintercrossingofinformationalwaysoccurs.Inordertoestablishanindexsystem,representativeandindependentindicesmustbeselectedusingthescientificmethodtoim-proveitsaccuracyandscientificnature.3)Evaluationthroughthewholeprocess:theindex
systemnotonlyincludesthewholeproductionpro-cess,butalsotheproductitself.Inotherwords,CP evaluationsofplantationloggingaimedtoanalyzeandevaluatetherawmaterialandenergyconsump-tion,aswellaspollutantcreationanditstoxicityinthewhole process of design, production, storage, andtransportation.4)Improvingsustainability:CPisasustainableim-
provingprocess,anditrequiresthecompanytocon-tinuouslyachievehigherenvironmentalobjectivesonthebasisofexistingeconomic,technological,anden-vironmental indices.Therefore, for thepurposeofpromotingCP,differentCPobjectivesshouldbese-lected topromote sustainabledevelopmenton thebasisoftheexistingsituation.5)Simpleandfocused: the indexsystemforCP
couldnotcoveralltheprocessesintheplantationlog-ging.Generally,themostsimpleandfocusedindicesareimplementedeffectively.
3.2 Selection of the evaluation indexTheprocessbeginswithselectingsomeevaluation
indices on thebasis of contents andprinciples (asshowninTable1)(Yuetal.2009).Suchindicesshouldnotbecombinedtogetherdirectlybecauseofinforma-tion interference and the fact that this could have an impactontheevaluationresults(Chen2003).Clusteranalysisdividesdataintogroups(clusters)
suchthatsimilardataobjectsbelongtothesameclus-teranddissimilardataobjectstodifferentclusters.The
resultingdatapartitionimprovesdataunderstandingand reveals its internal structure. Partitional clustering algorithmsdivideupadatasetintoclustersorclasses,wheresimilardataobjectsareassignedtothesamecluster,whereasdissimilardataobjectsshouldbelongtodifferentclusters.Inrealapplications,thereisveryoften no sharp boundary between clusters so thatfuzzyclustering isoftenbetter suited for thedata.Membershipdegreesbetweenzeroandoneareusedinfuzzyclusteringinsteadofcrispassignmentsofthedatatoclusters.Fuzzyclusteringisthemethodthatcancapturetheuncertaintysituationofrealdataanditiswellknownthatthefuzzyclusteringcanobtainarobust result as comparedwith conventionalhardclustering (Silviu 2013). Conventional clusteringmeansclassifyingthegivenobservationasexclusiveclusters.Itcanbeclearlyseenwhetheranobjectbe-longstoaclusterornot.However,suchapartitionisinsufficienttorepresentmanyrealsituations.There-fore,afuzzyclusteringmethodisofferedtoconstructclusterswithuncertainboundaries,sothismethodal-lowsthatoneobjectbelongstosomeoverlappingclus-terstosomedegree.Inotherswords,theessenceoffuzzyclusteringistoconsidernotonlythebelongingstatustoclusters,butalsotoconsidertowhatdegreetheobjectsbelongtotheclusters(Sato-Ilicetal.2006).Oneofthemainadvantagesoffuzzyclusteringistheabilitytoexpressambiguityinanassignmentofob-jectstoclusters(Silviu2013).Acorrespondingfuzzysetwasusedtodescribetheuncertainties(Hanetal.2011).However,apartfromthis,experimentalresultsprovethatfuzzyclusteringseemsalsotobemorero-bustintermsoflocalminimaoftheobjectivefunction(Klawonn2004).Anotherdistinctadvantageoffuzzyclusteringoveritscrispcounterpartisthatthecon-tinuousrangeofthecombinatorialfunctionsturnsintosmoothfunctions.Thismakesitpossibletodesignal-gorithmsthataremorelikelytoattainaglobalsolu-tion,whereascrisptechniquesoftenwindupinthelocalsolution.(Rousseeuw1995).Thefuzzyrelationbetween samples is quantified in fuzzy cluster, sofuzzycluster ismoreobjectiveandaccurate (Yang2011).Inrecentyears,thefuzzyclusteringhasbeenwide-
lystudiedandappliedinavarietyofkeyareas(WangandZhang2011)suchasindatamining,economicanalysis,andselectionevaluationindices(Xuetal.2005).Lietal.(2008)usedthismethodtoselecttheevaluationindicesofstockinvestmentvalue,provid-inganempiricalbasisforartificialintelligencemeth-odsinthestockvalueofinvestment.Guanetal.(2009)appliedthefuzzyclusteringapproachinconstructingtheevaluation indexsystemofcorecompetenceof
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 75
corporationandachievedverygoodresults.Yanetal.(2008)forecastedtheheavyrainstormbasedonthefuzzyclustertypeinJiangsuprovinceandreducedthestormemptyreportedrates.Chibaetal.(2012)ana-lyzedthewebsurveydatawiththesimilarityfuzzyclusterandshowedabetterperformancebyusingnu-mericalexamples.Asecondaryselectionshouldbeusedbythefuzzy
clustermethod.Thebasicideaoffuzzyclusterwastoconstructthefuzzymatrixaccordingtoattributesofaresearchobject,andonthisbasistheclusteringrela-tionwasdeterminedaccordingtocertainsubordinaterelations (Peng 2003).
Theprocess of the fuzzy clustermethod is de-scribedbythefollowing3steps:1)Setupafuzzysimilaritymatrixbycalculatingsimilarcoefficientsbetweensamplesandvari-ables.
2)Transform the fuzzy similaritymatrix into afuzzyequivalencematrixwiththehelpoffuzzyoperation.
3)Classifythefuzzyequivalencematrixaccordingtoadifferentfuzzygraphλ.
3.3 Data processingThefuzzyclusterprocessbeginsbysettingnitems
asaclassification,theindicatorsetX = {X1,X2,...,Xn},withavailablem-dimensionalvectordescribingthesample,Xi = {X1i,X2i,...,Xmi},i=1,2,...,n.AsthereweresomequalitativeanalysisindicesinCP evaluation of plantationharvesting,thispaperusedafivegradeLik-ertscaletoevaluateindices,rangingfrom»veryim-portant(5)«to»notcare(1)«,todetermineabettermeasureofeachindex.Themembershipfunctionwasthendefined:twas
thenumberofclassificationsofcertainproperties,Cp for pclass,xwasanattributevalueofCp,u (x) = N (Cp) m,p=1,2,...,t. N (Cp)wasthenumberofattributeval-ues included in the class Cp.Followingtheabovepro-cess,datamatrixwasinitialized,ifyijrepresentsthepropertyvalueoftheithrowandcolumnj,then0≤yij≤1,and yijsizereflectsthedependenceofthepropertyvaluefortheproperty.Establishingthefuzzysimilarmatrix:Thedomain
U = {y1,y2,...,yn}wasconcerned,yi,yjrelationshipwasdescribedbyR (yi,yj).Thereweremanymethodstohelpsetupafuzzysimilaritymatrix,suchasthedis-tancemethod, correlation coefficientmethod, andmaximumandminimummethod.Boththemaximumandminimummethod and correlation coefficientmethodwereusedinthispapertosetupafuzzysim-ilaritymatrixfortheCPevaluationofplantationlog-ging, and also to compare the difference betweenthem.
(1) Maximum and minimum methodAvalueiscalculatedbytheeq.(1):
( ) ( )( )
m
ik jki , j k 1m
ik jkk 1
1,
min , ,
max ,
i j
y yR y y i j
y y=
=
= = ≠
∑∑
(1)
Where,yikwastheattributevalueofrowicolumnk;yjkwastheattributevalueofrowjcolumnk.
Table 1 CP evaluation indicators system for forest plantation loggi
Prim
ary
index
sys
tem
for c
p ev
aluat
ion
of p
lanta
tion
logg
ing
First level index Second level index
Cutting area design
Cutting area division (x1)
Cutting area survey (x2)
Engineering design (x3)
Production process design (x4)
Logging technology
Rationality of operation process (x5)
Advanced of operation technology (x6)
Efficiency (x7)
Human-machine-environment harmony (x8)
Economics and safety of ways to work (x9)
Impacts on ecological environment
Soil physical properties (x10)
Soil chemical properties (x11)
Soil and water conservation (x12)
Injury rate of retention tree in slash (x13)
Average wind speed and temperature (x14)
Biomass (x15)
Biodiversity (x16)
The rate of slash soil erosion area (x17)
Resources and energy use
The number of discarded wood in cutting area (x18)
Utilization rate of wood (x19)
Utilization rate of slash (x20)
The number of discarded wood in the landing place (x21)
Logging equipment fuel consumption (x22)
Sustainable development
Survival ratio of renew (x23)
Renew ratio of cutting area (x24)
Wood renewal utilization (x25)
Improvement of safety and management
Safety management (x26)
Equipment safety (x27)
Labor protection (x28)
Labor intensity (x29)
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
76 Croat. j. for. eng. 37(2016)1
AfuzzysimilaritymatrixRwassetupasfollows:
( ) ( ) ( )( ) ( ) ( )
( ) ( ) ( )
1 1 1 2 1 n
2 1 2 2 2 n
n 1 n 2 n n
, , ,, , ,
, , ,
R y y R y y R y yR R y y R y y R y y
R y y R y y R y y
… = …
(2)
Asshownintheabovematrix,reflexivityandsym-metryaresatisfiedduetoR(yi,yi)=1;R(yi,yj)=R(yj,yi),butdonotmeetthetransitivity.Therefore,thevaluescannotbeclassifieddirectly.Squaresmethodwasusedtocalculatetransitive
closureofthefuzzymatrix,sothatafuzzyequivalencematrixwassetupfromthefuzzysimilaritymatrixR (asshownbelow).
2 4 2kR R R R→ → → → (3)
RkwasthetransitiveclosureofthefuzzymatrixwhenRk0Rk= R2k.Inotherwords,Rkwasthefuzzyequivalencematrix.Thecubeoperationwascalculatedasfollows:
( )k 10ij ik jkm
A R R A R R=
= ↔ ∧= ∨ (4)
λwasthefuzzygraph:
( ) ( )ij ij n nn n0,1 ( )R r R r ××
= ∀ = ll l∈ (5)where:
( ) ijij
ij
1,0,
rr
rl
ll
≥= <,soRλwasthefuzzygraphofmatrixRo.
Differentvalueswouldbeassignedtoλfromlargetosmallafterthefuzzyequivalencematrixwassetup,anddifferentclassificationswouldbegainedbycalcu-lating l.Inotherwords,lwasassignedbytheactualneeds,andclassificationwasselectedbyl(Lietal.2003).Arepresentativeindexwasselectedfromeachclas-
sificationas the typical indexafterclassifying.Thespecificmethodwasthefollowing:first,correlationcoefficientsofeachclassificationwerecalculated;then,theaveragesofthesquaresofthecorrelationcoeffi-cientbetweenoneindexandtheotherwerecalculated,andthemaximumwasthetypicalindex.Theindexcouldbeputintotheindexset,ifonlyoneindexwasclassifiedandoneindexoftwoindicesexistedinclas-sification(SârbuandEinax2008).ThemainfactorsthatimpactedtheCPofplanta-
tionloggingwouldbetheindexofclusteronthebasisofliteraturereviewandinvestigations.Wedesignedthe questionnaire according to the content and fea-turesofcleanerproductioninplantationlogging,is-suedover100questionnairestoalmosttwentyfor-
estrycompaniesoruniversitiesbyemail,includingcollegesofforestry,forestryresearchinstitutes,forestengineeringenterpriseandsoon,andrecovered30validquestionnaires.AfivegradeLikert scalewasusedtoevaluateindices.AllthirtysampleswereusedfortheanalysisandtherawdataisshowninTable2.Themembershipfunctionwascalculatedfromthe
definitionitself,andaninitializationprocesswasusedfor these data. Accordingtotherawdata,membershipfunctionofeachattributeiscalculatedandshowninTable3.Inthefirstattribute,x1 meansthefirstindex(cuttingareadivision);{1–5}meanstheimportantde-gree(from»veryimportant{5}«to»notcare{1}«);0.37,0.40,0.23weretheproportionofrating{4},{5}and{3}in30samples,respectively.Aftermappingeachmem-bershipfunction,theresultinginitializeddataispre-sentedinTable4.Accordingtotheeq.(1)andcombiningtheinitial-
izeddata,fuzzysimilarmatricesRwereestablished.Matricesofcuttingareadesign(6),loggingtechnology(7),impactsonecologicalenvironment(8),resourcesandenergyuse(9),sustainabledevelopmentandsafe-typroduction(10),andlaborprotection(11)werecal-culatedwiththemaximumandminimummethod,asshownineq.(6)–(11).Inthesesimilarmatrices,eachvaluemeansthecorrelationoftwoindices;thehighervaluehasthestrongercorrelation,thusvalue1meansfullycorrelated.Forexample,inmatrix(6),thefirstrow,thevalues1,0.6693,0.8085,0.7380meanthecor-relationbetweenindexofthefirstandfirst,thefirstandthesecond,thefirstandthethird,thefirstandthefourth, respectively. In thefirst column, thevaluesmeaningasthefirstrow,andsoon.
1.0000 0.6693 0.8085 0.73800.6693 1.0000 0.6904 0.65440.8085 0.6904 1.0000 0.67910.7380 0.6544 0.6791 1.0000
(6)
1.0000 0.7408 0.6813 0.5171 0.64410.7408 1.0000 0.6429 0.6699 0.61030.6813 0.6429 1.0000 0.5211 0.52030.5171 0.6699 0.5211 1.0000 0.54130.6441 0.6103 0.5203 0.5413 1.0000
(7)
1.00000.51250.62850.52270.64660.66670.72930.60330.51251.00000.63840.55860.54720.52060.51450.52690.62850.63841.00000.65220.62090.68310.64860.66030.52270.55860.65221.00000.52830.55600.54430.57310.64660.54720.62090.52831.00000.70610.68790.61770.66670.52060.68310.55600.70611.00000.72210.67310.72930.51450.64860.54430.68790.72211.00000.65220.60330.52690.66030.57310.61770.67310.65221.0000
(8)
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 77
Tabl
e 2
Raw
dat
a fro
m th
e qu
estio
nnair
e
Inde
xR 1
R 2R 3
R 4R 5
R 6R 7
R 8R 9
R 10
R 11
R 12
R 13
R 14
R 15
R 16
R 17
R 18
R 19
R 20
R 21
R 22
R 23
R 24
R 25
R 26
R 27
R 28
R 29
R 30
X 14
45
44
53
55
43
53
54
55
53
44
55
54
34
33
4
X 22
33
44
34
34
43
22
33
11
52
34
45
53
32
34
3
X 33
33
45
55
35
54
43
54
43
45
44
55
54
43
44
5
X 42
44
24
43
43
42
24
22
24
44
34
55
53
33
25
3
X 55
33
45
34
54
44
55
54
45
55
54
55
54
44
44
5
X 64
55
34
34
33
44
33
45
45
54
34
35
43
54
35
3
X 75
35
44
54
53
45
43
45
45
34
43
24
34
44
44
4
X 82
54
35
35
33
42
31
33
21
54
53
45
52
32
24
3
X 93
23
34
55
43
35
53
23
33
42
43
34
34
33
25
3
X 10
44
45
43
24
31
24
41
33
23
24
44
55
33
22
34
X 11
44
35
44
45
44
45
43
55
55
43
35
34
44
44
44
X 12
45
53
43
44
43
45
53
43
44
44
35
55
43
33
43
X 13
42
43
13
53
13
34
44
33
23
34
32
53
22
23
33
X 14
53
24
41
35
23
24
23
34
35
45
12
54
22
22
22
X 15
54
33
41
44
34
44
33
32
23
33
52
55
43
22
32
X 16
33
41
45
54
24
23
23
24
31
43
24
55
22
32
43
X 17
41
14
31
41
23
41
33
35
41
31
34
43
33
23
52
X 18
54
45
55
54
45
34
53
54
43
35
33
44
34
44
43
X 19
54
43
55
44
55
45
43
45
35
55
33
44
34
44
54
X 20
54
44
33
51
23
44
34
34
24
45
23
44
33
22
44
X 21
53
55
43
43
45
43
55
43
34
35
33
44
43
44
54
X 22
24
44
44
54
33
43
25
35
52
45
13
54
22
22
42
X 23
45
55
55
53
34
53
54
43
54
43
12
54
44
43
53
X 24
22
22
42
41
21
23
51
21
23
44
32
55
32
22
42
X 25
32
44
44
53
13
14
43
33
25
24
32
55
32
22
42
X 26
45
54
53
45
45
34
44
45
35
55
32
55
33
33
32
X 27
54
44
45
54
35
54
44
54
35
34
23
44
44
44
43
X 28
42
41
32
32
15
44
14
43
32
44
33
43
44
43
44
X 29
52
43
32
33
32
45
42
53
44
24
33
34
33
23
33
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
78 Croat. j. for. eng. 37(2016)1
Table 3 Membership function of each attribute
( ){ }{ }{ }
1
1 1
1
0.37 40.40 50.23 3
xu x x
x
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
2
2
2 2
2
2
0.07 10.17 20.40 30.27 40.10 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }
3
3 3
3
0.23 30.40 40.37 5
xu x x
x
… ∈= … ∈ … ∈
( ){ }{ }{ }{ }
4
44
4
4
0.27 20.23 30.37 40.13 5
xx
u xxx
… ∈
… ∈= … ∈ … ∈
( ){ }{ }{ }
5
5 5
5
0.10 30.43 40.47 5
xu x x
x
… ∈= … ∈ … ∈
( ){ }{ }{ }
6
6 6
6
0.37 30.37 40.27 5
xu x x
x
… ∈= … ∈ … ∈
( ){ }{ }{ }{ }
7
77
7
7
0.03 20.20 30.53 40.23 5
xx
u xxx
… ∈
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
8
8
8 8
8
8
0.07 10.20 20.33 30.17 40.23 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }{ }
9
99
9
9
0.13 20.50 30.20 40.17 5
xx
u xxx
… ∈
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
10
10
10 10
10
10
0.07 10.20 20.27 30.37 40.10 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }
11
11 11
11
0.17 30.57 40.26 5
xu x x
x
… ∈= … ∈ … ∈
( ){ }{ }{ }
12
12 12
12
0.33 30.43 40.24 5
xu x x
x
… ∈= … ∈ … ∈
( ){ }{ }{ }
13
13 13
13
0.07 2,50.20 2,40.47 3
xu x x
x
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
15
15
15 15
15
15
0.03 10.20 20.37 30.27 40.13 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }
16
16 16
16
0.07 10.27 2,3,40.13 5
xu x x
x
… ∈= … ∈ … ∈
( ){ }{ }{ }{ }
17
1717
17
17
0.23 1,40.10 20.37 30.07 5
xx
u xxx
… ∈
… ∈= … ∈ … ∈
( ){ }{ }{ }
18
18 18
18
0.27 30.43 40.30 5
xu x x
x
… ∈= … ∈ … ∈
( ){ }{ }{ }
19
19 19
19
0.20 30.43 40.27 5
xu x x
x
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
20
20
20 20
20
20
0.03 10.17 20.27 30.43 40.10 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }
21
21 21
21
0.33 30.40 40.27 5
xu x x
x
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
22
22
22 22
22
22
0.03 10.27 20.17 30.33 40.20 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }{ }
23
2323
23
23
0.03 1,20.23 30.33 40.37 5
xx
u xxx
… ∈
… ∈= … ∈ … ∈
( ){ }{ }{ }{ }
24
2424
24
24
0.13 1,30.47 20.17 40.10 5
xx
u xxx
… ∈
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
25
25
25 25
25
25
0.07 10.17 20.40 30.23 40.13 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }{ }
26
2626
26
26
0.03 20.17 30.50 40.30 5
xx
u xxx
… ∈
… ∈= … ∈ … ∈
( ){ }{ }{ }
27
27 27
27
0.23 30.53 40.24 5
xu x x
x
… ∈= … ∈ … ∈
( )
{ }{ }{ }{ }{ }
28
28
28 28
28
28
0.10 10.17 20.40 30.30 40.03 5
xx
u x xxx
… ∈
… ∈= … ∈ … ∈ … ∈
( ){ }{ }{ }
29
29 29
29
0.20 20.33 3,40.14 5
xu x x
x
… ∈= … ∈ … ∈
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 79
Tabl
e 4
Initi
alize
d da
ta
Inde
xR 1
R 2R 3
R 4R 5
R 6R 7
R 8R 9
R 10
R 11
R 12
R 13
R 14
R 15
R 16
R 17
R 18
R 19
R 20
R 21
R 22
R 23
R 24
R 25
R 26
R 27
R 28
R 29
R 30
X 10.
370.
370.
400.
370.
370.
400.
230.
400.
400.
370.
230.
400.
230.
400.
370.
400.
400.
400.
230.
370.
370.
400.
400.
400.
370.
230.
370.
230.
230.
37
X 20.
170.
400.
400.
270.
270.
400.
270.
400.
270.
270.
400.
170.
170.
400.
400.
070.
070.
100.
170.
400.
270.
270.
100.
100.
400.
400.
170.
400.
270.
40
X 30.
230.
230.
230.
400.
370.
370.
370.
230.
370.
370.
400.
400.
230.
370.
400.
400.
230.
400.
370.
400.
400.
370.
370.
370.
400.
400.
230.
400.
400.
37
X 40.
270.
370.
370.
270.
370.
370.
230.
370.
230.
370.
270.
270.
370.
270.
270.
270.
370.
370.
370.
230.
370.
130.
130.
130.
230.
230.
230.
270.
130.
23
X 50.
470.
100.
100.
430.
470.
100.
430.
470.
430.
430.
430.
470.
470.
470.
430.
430.
470.
470.
470.
470.
430.
470.
470.
470.
430.
430.
430.
430.
430.
47
X 60.
370.
270.
270.
370.
370.
370.
370.
370.
370.
370.
370.
370.
370.
370.
270.
370.
270.
270.
370.
370.
370.
370.
270.
370.
370.
270.
370.
370.
270.
37
X 70.
230.
200.
230.
530.
530.
230.
530.
230.
200.
530.
230.
530.
200.
530.
230.
530.
230.
200.
530.
530.
200.
030.
530.
200.
530.
530.
530.
530.
530.
53
X 80.
200.
230.
170.
330.
230.
330.
230.
330.
330.
170.
200.
330.
070.
330.
330.
200.
070.
230.
170.
230.
330.
170.
230.
230.
200.
330.
200.
200.
170.
33
X 90.
500.
130.
500.
500.
200.
170.
170.
200.
500.
500.
170.
170.
500.
130.
500.
500.
500.
200.
130.
200.
500.
500.
200.
500.
200.
500.
500.
130.
170.
50
X 10
0.37
0.37
0.37
0.10
0.37
0.27
0.20
0.37
0.27
0.07
0.20
0.37
0.37
0.07
0.27
0.27
0.20
0.27
0.20
0.37
0.37
0.37
0.10
0.10
0.27
0.27
0.20
0.20
0.27
0.37
X 11
0.57
0.57
0.17
0.27
0.57
0.57
0.57
0.27
0.57
0.57
0.57
0.27
0.57
0.17
0.27
0.27
0.27
0.27
0.57
0.17
0.17
0.27
0.17
0.57
0.57
0.57
0.57
0.57
0.57
0.57
X 12
0.43
0.23
0.23
0.33
0.43
0.33
0.43
0.43
0.43
0.33
0.43
0.23
0.23
0.33
0.43
0.33
0.43
0.43
0.43
0.43
0.33
0.23
0.23
0.23
0.43
0.33
0.33
0.33
0.43
0.33
X 13
0.20
0.20
0.20
0.47
0.07
0.47
0.07
0.47
0.07
0.47
0.47
0.20
0.20
0.20
0.47
0.47
0.20
0.47
0.47
0.20
0.47
0.20
0.07
0.47
0.20
0.20
0.20
0.47
0.47
0.47
X 14
0.17
0.20
0.37
0.20
0.20
0.07
0.20
0.17
0.37
0.20
0.37
0.20
0.37
0.20
0.20
0.20
0.20
0.17
0.20
0.17
0.07
0.37
0.17
0.20
0.37
0.37
0.37
0.37
0.37
0.37
X 15
0.13
0.27
0.37
0.37
0.27
0.03
0.27
0.27
0.37
0.27
0.27
0.27
0.37
0.37
0.37
0.20
0.20
0.37
0.37
0.37
0.13
0.20
0.13
0.13
0.27
0.37
0.20
0.20
0.37
0.20
X 16
0.27
0.27
0.27
0.07
0.27
0.13
0.13
0.27
0.27
0.27
0.27
0.27
0.27
0.27
0.27
0.27
0.27
0.07
0.27
0.27
0.27
0.27
0.13
0.13
0.27
0.27
0.27
0.27
0.27
0.27
X 17
0.23
0.23
0.23
0.23
0.37
0.23
0.23
0.23
0.10
0.37
0.23
0.23
0.37
0.37
0.37
0.07
0.23
0.23
0.37
0.23
0.37
0.23
0.23
0.37
0.37
0.37
0.10
0.37
0.07
0.10
X 18
0.30
0.43
0.43
0.30
0.30
0.30
0.30
0.43
0.43
0.30
0.27
0.43
0.30
0.27
0.30
0.43
0.43
0.27
0.27
0.30
0.27
0.27
0.43
0.43
0.27
0.43
0.43
0.43
0.43
0.27
X 19
0.37
0.43
0.43
0.20
0.37
0.37
0.43
0.43
0.37
0.37
0.43
0.37
0.43
0.20
0.43
0.37
0.20
0.37
0.37
0.37
0.20
0.20
0.43
0.43
0.20
0.43
0.43
0.43
0.37
0.43
X 20
0.10
0.43
0.43
0.43
0.27
0.27
0.10
0.03
0.17
0.27
0.43
0.43
0.27
0.43
0.27
0.43
0.17
0.43
0.43
0.10
0.17
0.27
0.43
0.43
0.27
0.27
0.17
0.17
0.43
0.43
X 21
0.27
0.33
0.27
0.27
0.40
0.33
0.40
0.33
0.40
0.27
0.40
0.33
0.27
0.27
0.40
0.33
0.33
0.40
0.33
0.27
0.33
0.33
0.40
0.40
0.40
0.33
0.40
0.40
0.27
0.40
X 22
0.27
0.33
0.33
0.33
0.33
0.33
0.20
0.33
0.17
0.17
0.33
0.17
0.27
0.20
0.17
0.20
0.20
0.27
0.33
0.20
0.03
0.17
0.20
0.33
0.27
0.27
0.27
0.27
0.33
0.27
X 23
0.33
0.37
0.37
0.37
0.37
0.37
0.37
0.23
0.23
0.33
0.37
0.23
0.37
0.33
0.33
0.23
0.37
0.33
0.33
0.23
0.03
0.03
0.37
0.33
0.33
0.33
0.33
0.23
0.37
0.23
X 24
0.47
0.47
0.47
0.47
0.17
0.47
0.17
0.13
0.47
0.13
0.47
0.13
0.10
0.13
0.47
0.13
0.47
0.13
0.17
0.17
0.13
0.47
0.10
0.10
0.13
0.47
0.47
0.47
0.17
0.47
X 25
0.40
0.17
0.23
0.23
0.23
0.23
0.13
0.40
0.07
0.40
0.07
0.23
0.23
0.40
0.40
0.40
0.17
0.13
0.17
0.23
0.40
0.17
0.13
0.13
0.40
0.40
0.40
0.40
0.40
0.17
X 26
0.50
0.30
0.30
0.50
0.30
0.17
0.50
0.30
0.50
0.30
0.17
0.50
0.50
0.50
0.50
0.30
0.17
0.30
0.30
0.30
0.03
0.17
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.17
X 27
0.23
0.53
0.53
0.53
0.53
0.23
0.23
0.53
0.23
0.23
0.23
0.53
0.53
0.53
0.23
0.53
0.23
0.23
0.23
0.53
0.23
0.23
0.53
0.23
0.53
0.53
0.53
0.23
0.53
0.53
X 28
0.30
0.17
0.30
0.10
0.40
0.17
0.40
0.17
0.10
0.03
0.30
0.30
0.10
0.30
0.30
0.40
0.40
0.17
0.30
0.30
0.40
0.40
0.40
0.30
0.40
0.40
0.17
0.40
0.40
0.40
X 29
0.13
0.20
0.33
0.33
0.33
0.20
0.33
0.33
0.33
0.20
0.33
0.13
0.33
0.20
0.13
0.33
0.33
0.33
0.20
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.20
0.13
0.33
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
80 Croat. j. for. eng. 37(2016)1
1.0000 0.8198 0.7030 0.8040 0.68760.8198 1.0000 0.6828 0.8082 0.66370.7030 0.6828 1.0000 0.6760 0.65530.8040 0.8082 0.6760 1.0000 0.70500.6876 0.6637 0.6553 0.7050 1.0000
(9)
1.0000 0.5541 0.60300.5541 1.0000 0.51310.6030 0.5131 1.0000
(10)
1.0000 0.6837 0.5846 0.58630.6837 1.0000 0.5798 0.60160.5846 0.5798 1.0000 0.66670.5863 0.6016 0.6667 1.0000
(11)
Asshownintheabovematrices6–10,reflexivityand symmetry are satisfied due to R(yi,yi)=1; R(yi,yj)=R(yj,yi), but do not meet the transitivity.Therefore, values cannot be classified directly. So,fuzzyequivalentmatriceswereestablishedaccordingtotheeq.(3).Sixfuzzyequivalentmatricesofcuttingareadesign(12),loggingtechnology(13),impactsonecologicalenvironment(14),resourcesandenergyuse(15),sustainabledevelopmentandsafetyproduction(16),andlaborprotection(17)shouldbecalculatedbythesquaresmethod,asshownineq.(12)–(17).Matrices(12)–(17),reflexivity,symmetryandtran-
sitivityareallsatisfied,soclassificationwillbedonedirectly.Eachvalueinthematricesmeansthel value.
1.0000 0.6904 0.8085 0.73800.6904 1.0000 0.6904 0.69040.8085 0.6904 1.0000 0.73800.7380 0.6904 0.7380 1.0000
(12)
1.0000 0.7408 0.6813 0.6699 0.64410.7408 1.0000 0.6813 0.6699 0.64410.6813 0.6813 1.0000 0.6699 0.64410.6699 0.6699 0.6699 1.0000 0.64410.6441 0.6441 0.6441 0.6441 1.0000
(13)
1.00000.63840.68310.65220.70610.72210.72930.67310.63841.00000.63840.63840.63840.63840.63840.63840.68310.63841.00000.65220.68310.68310.68310.67310.65220.63840.65221.00000.65220.65220.65220.65220.70610.63840.68310.65221.00000.70610.70610.67310.72210.63840.68310.65220.70611.00000.72210.67310.72930.63840.68310.65220.70610.72211.00000.67310.67310.63840.67310.65220.67310.67310.67311.0000
(14)
1.0000 0.8198 0.7030 0.8082 0.70500.8198 1.0000 0.7030 0.8082 0.70500.7030 0.7030 1.0000 0.7030 0.70300.8082 0.8082 0.7030 1.0000 0.70500.7050 0.7050 0.7030 0.7050 1.0000
(15)
1.0000 0.5541 0.60300.5541 1.0000 0.55410.6030 0.5541 1.0000
(16)
1.0000 0.6837 0.6016 0.60160.6837 1.0000 0.6016 0.60160.6016 0.6016 1.0000 0.66670.6016 0.6016 0.6667 1.0000
(17)
Accordingtotheeq.(4)and(5),λcutmatriceswerecalculatedanddifferentclassificationsweregained.Sixclassifiedresultsofcuttingareadesign(Table5),loggingtechnology(Table6),impactsonecologicalenvironment(Table7),resourcesandenergyuse(Ta-ble8),sustainabledevelopmentandsafetyproduction(Table9),andlaborprotectionindices(Table10)canbecalculatedbyassigningtoλfromlargetosmallandclassification,asshowninTables5–10.Atotaloftwelveindiceswerechosenforevalua-
tion.Twoindiceswereselectedfromeachofthefol-lowingconditions:cuttingareadesign,loggingtech-
Table 5 The cluster result of cutting area design
l value Classification number Specific category
1 4 {X1},{X2},{X3},{X4}
0.81 3 {X1,X3},{X2},{X4}
0.74 2 {X1,X3,X4},{X2}
0.69 1 {X1,X2,X3,X4}
Table 6 The cluster result of logging technology
l value Classification number Specific category
1 5 {X5},{X6],{X7},{X8},{X9}
0.74 4 {X5,X6},{X7},{X8},{X9}
0.68 3 {X5,X6,X7},{X8},{X9}
0.67 2 {X5,X6,X7,X8},{X9}
0.64 1 {X5,X6,X7,X8,X9}
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 81
value of l=0.74,where{X2}wasapartdirectlyputintheindexset,butonetypicalindexshouldbeselectedfrom{X1,X3,X4}.Forloggingtechnology,specificcat-egories of {X5,X6,X7,X8} and {X9} yielded a value of l=0.67,where{X9}wasapartdirectlyputintheindexset,butonetypicalindexshouldbeselectedfrom{X5,
X6, X7, X8},similartotheothers.Similarindiceshavetoselectedbythecorrelationindexmethod.Here,theloggingtechnologyistakenasanexample.Thecorrelationindices r of X5,X6,X7,X8 should
becalculatedfirst,asshowninTable11.
Table 11 Correlation coefficients of X5, X6, X7, X8
rij X5 X5 X5 X5
X5 1.0000 0.2999 0.2723 0.0539
X6 0.2999 1.0000 0.1531 0.1322
X7 0.2723 0.1531 1.0000 0.1087
X8 0.0539 0.1322 0.1087 1.0000
ThecorrelationindicesR arecalculatedasfollows.
2 2 256 57 58
5 0.05573
r r rR
+ += = (18)
2 2 265 67 86
6 0.04363
r r rR
+ += = (19)
2 2 275 76 78
7 0.06843
r r rR
+ += = (20)
2 2 285 86 87
8 0.01073
r r rR
+ += = (21)
Inthisinstance,theresultsindicatethatR5>R6>R7>R8. Therefore, R5wasputintothecategorybecauseitwasthemaximum.Theresultsoftheabovecalculationsshowedthat
theindicesofloggingtechnologyweretherationalprocessesandeconomicsandsafewaystowork.So,theindicesofcuttingareadesign,impactsonecologi-calenvironment,environmentalbenefits,aswellassustainabledevelopmentandlaborprotectioncouldbecalculatedbytheabovemethods,andtheresultsareshowninTable12.TheindicesshowninTable12yieldedthefollow-
ingresults:first,therelativefuzzysimilaritymatrixwassetupbythemaximumandminimummethod;then, the fuzzy equivalencematrixwas calculatedfromsquaresandtransitiveclosure;finally,theindiceswereselectedbythecorrelationcoefficientmethod.
Table 7 The cluster result of ecological environmental impact
l value
Classification number
Specific category
1 8 {X10} {X11},{X12}.{X13},{X14},{X15},{X16},{X17}
0.73 7 {X10,X16} {X11},{X12}.{X13},{X14},{X15},{X17}
0.72 6 {X10,X15,X16},{X11},{X12}.{X13},{X14},{X17}
0.71 5 {X10,X14,X15,X16},{X11},{X12}.{X13},{X17}
0.68 4 {X10,X12,X14,X15,X16},{X11},{X13},{X17}
0.67 3 {X10,X12,X14,X15,X16,X17},{X11},{X13}
0.65 2 {X10,X12,X13,X14,X15,X16,X17},{X11},
0.64 1 {X10,X12,X13,X14,X15,X16,X17,X11},
Table 8 The cluster result of utilization of resources and energy
l value Classification number Specific category
1 5 {X18},{X19},{X20},{X21},{X22}
0.82 4 {X18,X19},{X20},{X21},{X22}
0.81 3 {X18,X19,X21},{X20},{X22}
0.71 2 {X18,X19,X21,X22},{X20}
0.7 1 {X18,X19,X21,X20,X22}
Table 9 The cluster result of sustainable development
l value Classification number Specific category
1 3 {X23},{X24},{X25}
0.6 2 {X23,X25},{X24}
0.5 1 {X23,X25,X24}
Table 10 The cluster result of safety production management and protection
l value Classification number Specific category
1 4 {X26},{X27},{X28},{X29}
0.68 3 {X26,X27},{X28},{X29}
0.67 2 {X26,X27},{X28,X29}
0.6 1 {X26,X27,X28,X29}
nology,impactsonecologicalenvironment,resourcesandenergyuse,sustainabledevelopmentandsafetyproduction,andlaborprotection.Forthecuttingarea,specificcategoriesof {X1,X3,X4} and {X2} yielded a
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
82 Croat. j. for. eng. 37(2016)1
(2) Relation coefficient methodAfuzzysimilaritymatrixwassetupbytherelation
coefficientmethod,andthentheindicesofCP evalu-ationforplantationloggingcanbecalculatedbythestepsthatweresimilartothemaximumandminimummethod.Aftertherawdatawasinitialized,therelationcoef-
ficientsbetweeneachindexwerecalculated,andtheabsolutevalueoftherelationcoefficientscanbetheelementsfordeterminingthefuzzysimilaritymatrix.Sixfuzzysimilaritymatricesofcuttingareadesign(22),loggingtechnology(23),impactsontheecologicalenvironment(24),resourcesandenergyuse(25),sus-tainabledevelopmentandsafetyproductionmanage-ment(26)andlaborprotection(27)werecalculatedbyrelationcoefficientmethod,asshownineq.(22)–(27).
1.0000 0.1606 0.1473 0.05740.1606 1.0000 0.1168 0.08520.1473 0.1168 1.0000 0.37240.0574 0.0852 0.3724 1.0000
(22)
1.0000 0.2999 0.2723 0.0539 0.10840.2999 1.0000 0.1531 0.1322 0.00670.2723 0.1531 1.0000 0.1087 0.29410.0539 0.1322 0.1087 1.0000 0.07450.1084 0.0067 0.2941 0.0745 1.0000
(23)
1.00000.04600.00250.16570.09030.01860.42630.18350.04601.00000.16770.02140.37870.10730.10530.03100.00250.16771.00000.11790.09160.24810.08520.07480.16570.02140.11791.00000.17410.09760.14010.02970.09030.37870.09160.17411.00000.33940.39370.19630.01860.10730.24810.09760.33941.00000.15460.08690.42630.10530.08520.14010.39370.15461.00000.03590.18350.03100.07480.02970.19630.08690.03591.0000
(24)
1.0000 0.3713 0.0046 0.0516 0.13450.3713 1.0000 0.0218 0.2802 0.37440.0046 0.0218 1.0000 0.0237 0.29170.0516 0.2802 0.0237 1.0000 0.00760.1345 0.3744 0.2917 0.0076 1.0000
(25)
1.0000 0.0346 0.14120.0346 1.0000 0.10190.1412 0.1019 1.0000
(26)
1.0000 0.2620 0.1077 0.25880.2620 1.0000 0.0470 0.1584 (27)
Sixfuzzyequivalencematricesofcuttingareade-sign(28),loggingtechnology(29),impactsontheeco-logicalenvironment(30),resourcesandenergyuse(31),sustainabledevelopmentandsafetyproductionmanagement(32)andlaborprotection(33)werecal-culatedbythesquaresmethod,asshownineq.(28)–(33).
1.0000 0.1606 0.1473 0.14730.1606 1.0000 0473 0.14730.1473 0.1473 1.0000 0.37240.1473 0.1473 0.3724 1.0000
(28)
1.0000 0.2999 0.2723 0.0539 0.10840.2999 1.0000 0.1531 0.1322 0.00670.2723 0.1531 1.0000 0.1087 0.29410.0539 0.1322 0.1087 1.0000 0.07450.1084 0.0067 0.2941 0.0745 1.0000
(29)
Table 12 CP assessment indicators of plantation logging – six first grade indices and twelve second grade indices (max and min matrix method)
Cutting area design Logging technologyImpacts on ecological
environmentResources and
energy useSustainable
developmentSafety production management
and protection
Cutting area survey Rational processesSoil physical properties
Utilization ratio of wood
Survive ratio of renew Safety management
Engineering designEconomics and safety
of ways to workBiodiversity
Utilization ratio of slashes
Renew ratio of cutting area
Labor protection
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 83
1.0000 0.37870.24810.17410.39370.33940.42630.19630.3787 1.00000.24810.17410.37870.33940.37870.19630.2481 0.24811.00000.17410.24810.24810.24810.19630.1741 0.17410.17411.00000.17410.17410.17410.17410.3937 0.37870.24810.17411.00000.33940.39370.19630.3394 0.33940.24810.17410.33941.00000.33940.19630.4263 0.37870.24810.17410.39370.33941.00000.19630.1963 0.19630.19630.17410.19630.19630.19631.0000
(30)
1.0000 0.3713 0.2917 0.2802 0.37130.3713 1.0000 0.2917 0.2802 0.37440.2917 0.2917 1.0000 0.2802 0.29170.2802 0.2802 0.2802 1.0000 0.28020.3713 0.3744 0.2917 0.2802 1.0000
. (31)
1.0000 0.1019 0.14120.1019 1.0000 0.10190.1412 0.1019 1.0000
(32)
1.0000 0.2620 0.1077 0.25880.2620 1.0000 0.1077 0.25880.1077 0.1077 1.0000 0.10770.2588 0.2588 0.1077 1.0000
(33)
Laterstepswerequitethesameusingthemaxi-mum andminimummethod, and the indices areshowninTable13.ResultsfromTable12andTable13werequitesim-
ilar.Onlytwoindicesweredifferent,whichindicatesthatbothofthemethodswerereliableforcalculatingtheindex.Usingbothmethodsstrengthenstheanaly-sis.
4. DiscussionInthisstudy,wehaveselectedsomeevaluation
indices of CPonthebasisofplantationloggingandsetprinciples.CPhasbeenwidelyappliedinallkindsofindustry,butseldomusedinforestry.Inrecentyears,thetheoriesandconceptsofCPhavebeenconsideredfortheharvestingareainChina,buttheyonlydis-cussedtheconcept,roleoftarget,andnorealanalysisof theproducts.While theproductionprocesswasgenerallyfoundtobeenvironmentallyacceptabledur-ingtimberharvesting,amoreformalprocessisneed-edforanaccurateevaluation(Qiang2000,Zhao2000,Zhao2008,Yu2009).Therewasalackofspecificsandtargeted results inpractice, andguidancewasnotstrong in the forestenterprise.Thefindingsof this
studyindicateimplementationofcleanerproductionin China could have a guiding role in forest engineer-ingcompanies.Toevaluatethecontentsofcleanerproductionin
plantationharvestingoperations,wedistributed100questionnairestoprofessorsinuniversity,researchersinforestryresearchinstituteandloggersorworkersinforestryenterprisesandrecoveredthirtyvalidques-tionnaires.Thelowparticipationmayindicatethatmostofthemputlessemphasisoncleanerproduction,especiallytheforestengineeringenterprisesinChina.Ithasbeenproposedthat theChinesegovernmentshouldincreasepublicityandeducationinthisarea.Likeotherindustries,implementationcouldincludethe introductionof incentivesorrelevant lawsandregulationstoensurethatmoreattentionispaidonthecleanerproductionintheforestryenterprises.Accordingtotheinvestigationresultsbasedonthe
thirtysubmittedresponses,thefuzzyclusteringmeth-odwasusedtoinitiallyscreentwenty-ninesecond-gradeindices.Sixfirst-gradeindicesandtwelvesec-ond-gradeindiceswereselectedasformalevaluationindices.Asweallknow,theothersecond-gradeindi-cesnotselectedarealsoimportant,buttoomanyin-diceswillbedifficulttoanalyzeinpracticeinforestcompanies.Accordingtotheprinciplesfordetermin-ingevaluationindices,theindexsystemforCP could notcoveralltheprocessesintheplantationlogging.Generally themostsimpleandfocused indicesareimplementedeffectively.Thepurposeofthequestion-nairewasissuedfortheforestryenterprises,withthemoreimportantindicesrelatedtotheimplementationofcleanerproductioninplantationlogging.Accordingtothe30validquestionnaires,thefuzzyclustermeth-odswereusedtoselectthemoreimportantindices.Thisindicatesthattheselectedsecond-gradeindicesaremoreimportantthanothersecond-gradeindicesnot selected according to the results of questionnaires andfuzzyclustermethods.Atthesametime,severalmethodswereusedto
screenevaluationindices,suchasthegraycorrelationmethod,analytichierarchyprocess,etc.,(Shen2002,Xu2007)butthefuzzyclusteringanalysiswaswidelyusedforobjectivityandaccuracy(Yang2011).There-sultsshowedthatthefuzzyclusteringmethodisreli-ableforscreeningindices.CPofplantationlogginghaditsownadvantagescomparedwithotherCPob-jectivesthatwerecombined,suchassavingresources,reasonableuseofrenewableenergyinforests,protect-ingtheenvironmentinforestsandeducatingperson-alprotectionforworkers.ThebasicprinciplewithCP is»preventionmustcomefirst,treatmentshouldonly
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
84 Croat. j. for. eng. 37(2016)1
beneededinspecialcircumstances«inallthestagesoftheloggingprocess.Ourinitialfindingsindicatethemethodsofusing
advanced technology and improving process andequipment.Differentloggingmethods(includedcut-ting,bucking,skiddingandtransportation)wereusedincuttingareasduetodifferentterrainconditions,andeventheworkingefficienciesdifferedwhenusingthesameloggingmethodsinthesamecuttingarea.Forexample,skiddingwithcablewaywassuitableforhillsormountaincommunities,butskiddingwithtractorswassuitableforflatcommunities.Theharvestingma-chinehasbeenusedduetoitshighworkefficiency;however,itisnotsuitableforallcuttingareasbecauseofworkingconditions.Therefore, it isbettertouseadvanced technologyand improveprocessingandequipmentbasedonlocalconditions.Secondly,inthewaysofsavingandrationallyus-
ingresourcesandenergy,thetreewastherawmate-rial for CPoflogging.Thebesttimetologwaswhenthe treebiomasshasbeenat itsmaximum.Ontheotherhand,thespecialcharacteristicsofCP for log-gingwerethecomplexworkingconditions,fertilityoftheslash,overlappingoftheworkingsystemandeco-logicalsystem,complexityandfuzzinessofmultiplefactors.Inrecentyears,twoareashavebeenstudiedon
plantationlogging.Oneareawastheimpactsoflog-gingontheecologicalenvironment,whicincludedthesoils,vegetation,treesurvival,wildanimals,biologicaldiversity, landscapes,anduseofslash(Wang2005,Rab1994aand1996b,Li1994,Qiu1998,Chen1999,Crokeetal.2001aand2006b,Blumfieldetal.2003,Hartantoetal.2003,Birdetal.2004,Penningtonetal.2004,Radeletal.2006,Demiretal.2007,Langeretal.2008,Freyetal.2009,Walmsleyetal.2009,Stoffeletal.2010)Theotherstudywasdoneontheecologicalloggingprocessesandtechnology,whichincludedtheevaluationofecological,economicandcombinedben-efits(Huang1995,Deng2005,Zhang2005,Spinelliet
al.2012,Berhongarayetal.2013).ManyevaluationsystemsforCPhavebeenwidelyusedinchemicalengineering, services,andagriculturesallover theworld.StudiesonCPforforestspointedoutthecon-ception,overallgoals,guidelines,andtheimplemen-tationofthemeasureswithoutfurtherinvestigations.InChina,somerulesandregulationsofCPhavebeenaccomplishedinafewlargeloggingenterprises,asindicatedinthearticle»TheMeasuresforCPbyGen-heForestryBureau«.Unfortunately,therewasnotanyfurtheranalysisinvolvingcontents,indexsystemforevaluation,andmethods forevaluation.Therefore,morestudieswillbeneededonCPforplantationlog-ging.
5. ConclusionsInthispaper,anindexsystemofCP evaluation for
plantationloggingwassetuponthebasisofCP,ac-cordingtothecharacteristicsofplantationlogging.Thecontentsofsuchanindexsystemincludedthefollowingsixfirst-gradecriteria:cuttingareadesign,loggingtechnology,impactsontheecologicalenviron-ment,resourcesandenergyuse,sustainabledevelop-mentandsafetyproductionmanagement,andlaborprotection.Thefuzzyclustermethodwasusedtose-lecttheindices.Finally,twelvesecond-gradeindiceswereselectedastheevaluationindices.Acomparisonusingbothmaximumandminimumanalysisandcor-relationanalysisshowedthat10ofthe12indiceswereacceptable.Thetwelvesecond-gradeindicesselectedarejustthefirststepforCP of logging. According to theresultsofselecting,thesemadethestandardforCP evaluationofplantationlogging.Then,accordingtothestandards,cleanerproductionauditwillbeimple-mentedtochecktheforestcompanies,expectingforestcompaniestomeetthestandards.Theimplementedguidelines of CPinplantationloggingwillgivethemtherightdirections.So,theresultsofselectingindiceswillbebeneficialforsustainableproductionandman-agementofplantationforestsinChina.
Table 13 CP assessment indicators of plantation logging – six first grade indices and twelve second grade indices (correlation coefficient matrix method)
Cutting area design
Logging technologyImpacts on the
ecological environmentResources and energy
useSustainable
developmentSafety production management
and labor protection
Cutting area survey
Rational processes Soil physical propertiesUtilization ratio of
woodSurvive ratio of
renewSafety management
Production process design
Economics and safety of ways to work
BiodiversityLogging equipment fuel
consumptionRenew ratio of
cutting areaLabor protection
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 85
AcknowledgmentsThisresearchreceivedsupportfromInternational
ScienceandTechnologyCooperationProjectsofChina(China–Finland)(2006DFA32840):StudyonHarvest-ingModelforPlantationForestBasedonIndustrialEcology.Theprojectwasfundedbythe»PriorityAca-demicProgramDevelopmentofJiangsuHigherEdu-cationInstitutions(PAPD)«.
6. ReferencesAnon.:TheresultsoftheeighthnationalforestresourcesinventoryinChina(2009–2013).Availableonhttp://www.forestry.gov.cn/main/65/content-659670.html
Long,A.J.,2006:Environmentallysoundforestharvesting.UniversityofFlorida,IFASExtension,SS-FOR-6:1–11.
Frey,B.,Kremer,J.,Rüdt,A.,Sciacca,S.,Matthies,D.,2009:Compactionofforestsoilswithheavyloggingmachineryaffectssoilbacterialcommunitystructure.EuropeanJournalofSoilBiology45(4):312–320.
Berhongaray,G.,Kasmioui,E.O.,Ceulemans,R.,2013:Com-parativeanalysisofharvestingmachinesonanoperationalhigh-densityshortrotationwoodycrop(SRWC)culture:One-processversustwo-processharvestoperation.Biosys-temsEngineering58:333–342.
Bird,S.B.,Coulson,R.N.,Fisher,R.F.,2004:Changesinsoilandlitterarthropodabundancefollowingtreeharvestingandsitepreparationinaloblollypine(Pinus taedaL.)planta-tion.ForestEcologyandManagement202(1–3):195–208.
Blumfield,T.J.,Xu,Z.H.,2003:Impactofharvestresiduesonsoilmineralnitrogendynamicsfollowingclearfallharvest-ingofahooppineplantationinsubtropicalAustralia.ForestEcologyandManagement179(1–3):55–67.
Chen,H.J.,1999:EcologicalenvironmentanalysisofAbies holophyllaplantationsunderdifferentcuttingsystems.Jour-nalofForestryResearch10(3):181–182.
Chen,K.Y.,2003:Ecosystemhealth:ecologicalsustainabilitytargetofstrategicenvironmentassessment.JournalofFor-estryResearch14(2):146–150.
Chiba,R.,Sato-llic,Mika,2012:AnalysisofWebSurveyDatabasedonSimilarityofFuzzyClusters.ProcediaComputerScience12:224–229.
Croke,J.,Peter,H.,Peter,F.,2001:Soilrecoveryfromtrackconstructionandharvestingchangesinsurfaceinfltration,erosionanddeliveryrateswithtime.ForestEcologyandManagement143(1–3):3–12.
Croke,J.,Nethery,M.,2006:Modelingrunoffandsoilero-sioninloggedforests:Scopeandapplicationofsomeexist-ingmodels.Catena67(1):35–49.
Demir,M.,Makineci,E.,Yilmaz,E.,2007:Investigationoftimberharvestingimpactsonherbaceouscover,forestfloor
andsurfacesoilpropertiesonskidroadinanoak(Quercus petreaL.)stand.BuildingandEnvironment42(3):1194–1199.
Deng,S.M,2005:EvaluationonEconomicBenefitsofOp-erationModesofLoggingCommonlyUsedinanPlantationForest.ForestEngingeering21(1):57–59.
Dykstra,D.P.,2001:Reducedimpactlogging:conceptsandissues.In:ApplyingReducedImpactLoggingtoAdvanceSustainableForestManagement.Asia-PacificForestryCom-mission InternationalConferenceProceedings,Kuching,Malaysia,Feb26–Mar1,15–31.
GillmanG.P.,Sinclair,D.F.,Knowlton,R.,Keys,M.G.,1985:TheeffectonsomesoilchemicalpropertiesoftheselectiveloggingofanorthQueenslandRainforest.ForestEcologyandManagement12(3–4):195–214.
Guan,Y.X.,Wang,Y.L.,Guo,X.Y.,2009:Applyfuzzycluster-ingapproachinconstructtheevaluationindexsystemofcorecompetenceofcorporation.Mathematics inPracticeandTheory3:1–7.
Han,P.,Wang,J.H.,Ma,Z.H.,Lu,A.X.,Gao,M.,Pan,L.G.,2011:ApplicationofFuzzyClusteringAnalysisinClassifica-tionofSoilinQinghaiandHeilongjiangofChina.ComputerandComputingTechnologiesinAgricultureIV;IFIPAd-vancesinInformationandCommunicationTechnology344:282–289.
Hartanto,H.,Prabhu,R.,Widayat,A.S.E.,Asdak,C.,2003:Factorsaffectingrunoffandsoilerosion:plot-levelsoillossmonitoringforassessingsustainabilityofforestmanage-ment.ForestEcologyandManagement180(1–3):361–374.
He,H.S.,Mladenoff,D.J.,Gustafson,E.J., 2002:Studyoflandscape change under forest harvesting and climatewarming-inducedfiredisturbance.ForestEcologyandMan-agement155(1–3):257–270.
Huang,G.P.,1995:Optimalsolutionofcargotimberproduc-tionincuttingarea. ForestEngineering11(3):21–24.
Klawonn,F.,2004:Fuzzyclustering:Insightandanewap-proach.Mathware&softcomputing11:125–142.
LisaLanger,E.R.,Steward,G.A.,Kimberley,M.O.,2008:Veg-etationstructure,compositionandeffectofpineplantationharvestingonriparianbuffersinNewZealand.ForestEcol-ogyandManagement256(5):949–957.
Lee,C.S.,Leitmann,G.,1994:Astabilizingharvestingstrat-egyforanuncertainmodelofanecologicalsystem.Comput-ers&MathematicswithApplications27(9–10):199–212.
Li,Y.F.,Li,P.Y.,2008:Evaluationindexesselectionofstocksinvestmentvaluebasedonfuzzyclustering.JournalofYan-shanUniversity32(6):551–556.
Li,M.,Gu,Y.D.,Feng,Y.B.,Wang,J.Y.,2003:AMethodtoDecreasetheDistortionofFuzzy.JournalofBeijingNormalUniversity(NaturalScience)39(5):601–605.
Peng,Z.Z.,2003:Fuzzymathematicsanditsapplication.Wuhan:WuhanUniversitypress.
A. Yu et al. Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87)
86 Croat. j. for. eng. 37(2016)1
Pennington,P.I.,Laffan,M.,2004:Evaluationoftheuseofpre-andpost-harvestbulkdensitymeasurementsinwetEucalyptusobliqueforestinSouthernTasmania.EcologicalIndicators4(1):39–54.
Qiang,X.B.,Xing,L.P.,Zhao,H.,2000:Thetheoryandmeth-odofcleanerproductioninharvestingarea.ForestEngineer-ing16(6):6–8.
Qiu,R.H.,1998:TheoryandresearchofEcologicalForestryOperations.ForestryScienceandTechnology1:7–10.
Rab,M.A.,1994:Changesinphysicalpropertiesofasoilassociatedwith loggingofEucalyptus regnans forest insoutheasternAustralia.ForestEcologyandManagement70(1–3):215–229.
Rab,M.A.,1996:SoilphysicalandhydrologicalpropertiesfollowingloggingandslashburningintheEucalyptusreg-nansforestofsoutheasternAustralia.ForestEcologyandManagement84(1):159–176.
Radel,V.C.,Mladen,D.J.,Gustafson,E.J.,2006:ModelingforestharvestingeffectsonlandscapepatternintheNorth-westWisconsinPineBarrens.ForestEcologyandManage-ment236(1):113–126.
Rousseeuw,P.J.,1995:Discussion:FuzzyClusteringattheIntersection.Technometrics37(3):283–286.
Sato-Ilic,M.,Jain,C.L.,2006:InnovationsinFuzzyCluster-ing-TheoryandApplications.StudiesinFuzzinessandSoftComputing.Springer,205p.
Sârbu,C.,Einax,J.W.,2008:Studyoftraffic-emittedleadpol-lutionofsoilandplantsusingdifferentfuzzyclusteringal-gorithms.AnalBioanalChem390(5):1293–1301.
Silviu.B.,2010:Fuzzyclustering.Detailsseeathttp://sci-ence5.net/f/fuzzy-clustering---ubb,-cluj-w14253.html
Shen,Z.Y.,Yang,Z.F.,2002:Grayassociateanalysismethodinscreeningofindexsystem.Mathematicsinpracticeandtheory32(5):728–732.
Spinelli,R.,Schweier,J.,Francesco,F.D.,2012:Harvestingtechniquesfornon-industrialbiomassplantations.Biosys-temsEngineering113(4):319–324.
Stoffel,J.L.,Gower,S.T.,Forrester,J.A.,Mladenoff,D.J.,2010:Effectsofwinterselectivetreeharvestonsoilmicroclimateand surface CO2fluxofanorthernhardwoodforest.ForestEcologyandManagement259(3):257–265.
Walmsley,J.D.,Jones,D.L.,Reynolds,B.,Price,M.H.,Healey,J.R.,2009:Wholetreeharvestingcanreducesecondrotationforestproductivity.ForestEcologyandManagement257(3):1104–1111.
Wang,L.H.,1997:Rationalharvestingpolicyandsustainabledevelopmentofcommunityinforestregions.JournalofFor-estryResearch8(1):50–53.
Wang,L.H.,Yang,X.C.,2005:HarvestingandEnvironmentofForestry.Harbin:NortheastForestryUniversityPress.
Wang,W.N.,Zhang,Y.J.,2007:Onfuzzyclustervalidityin-dices.FuzzySetsandSystems158(19):2095–2117.
Xu,H.,Luo,C.,Liu,Z.G.,2007:Studyontheselectionmeth-odofevaluationindicesbyAHP.Chinaoffshoreoilandgas19(6):415–418.
Xu,H.Y.,Wang,G.A.,Wang,W.S.,2005:Astudyoffuzzyclusteringanalysisindatamining.ComputerEngineeringandApplications41(17):177–179.
Yan,M.L.,Wang,M.,Yu,B.,2008:AheavyrainfallforecastmethodbasedonfuzzyclustertypingbyusingapplicationandinterpretationofNWP.ScientiaMeteorologicaSinica28(5):581–585.
Yang,C.L.,Yang,C.Y.,2011:Applicationoffuzzyclusterinpredictioncoalandrockdynamicdisasters.ProcediaEngi-neering26:1541–1546.
Yu,A.H.,Zhao,C.,Huang,Y.,2009:AStudyonCleanerPro-ductionEvaluationofForestPlantationLogging.ActaAgri-culturaeUniversitatisJiangxiensis31(2):311–316.
Zhang,Z.X.,Zhou,X.N.,Zhao,C.,Chen.Y.F.,2008:SelectingoftheOptimumOperationModelofEcologicalHarvestingandTransportation inSouthernArtificial ForestArea inChina.ScientialSilvaeSiniae44(5):128–134.
Zhang,Z.X.,2005:StudyonplantationharvestingoperationsSysteminnorthwesternFujian,NanjingForestryUniversity.
Zhao,J.R.,Zhang,D.L.,2003:CleanerProductionPromotionLawQ&A[M].Beijing:AcademyPress,15–20.
Zhao,H.,Li,X.L.,Qiang,X.B.,2000:SummaryofthecleanerproductionofSilviculturalenterprise.ForestryEnterpriseofChina(4):42–43.
Zhao,K.,Zhao,C.,2008:Cleanerproductionappliedintim-berharvestingandutilizationoffastgrowingforest.Forestresourcesmanagement2:47–50.
Selecting Evaluation Indices for Cleaner Production of Plantation Logging in Southern China ... (71–87) A. Yu et al.
Croat. j. for. eng. 37(2016)1 87
Received:October15,2014Accepted:April2,2015
Authors’address:
Assoc.prof.,AihuaYu,PhD.e-mail:[email protected].,ChenZhao,PhD.e-mail:[email protected],YaoZhao,PhD.e-mail:zhaoyaonfu@163.comDepartmentofForestEngineeringNanjingForestryUniversity159RongPanRoad210037Nanjing,JiangsuprovinceP.R.CHINA
Assoc.prof.,TomGallagher,PhD.*e-mail:tgallagher@auburn.eduSchoolofForestryandWildlifeSciencesAuburnUniversity3425ForestryandWildlifeSciencesBuilding36830Auburn,AlabamaUSA
*Correspondingauthor