UNEP：水體生態(tài)系統(tǒng)與可持續(xù)發(fā)展目標(biāo)：進(jìn)展衡量

3

Dataavailability

FigureES.1

PercentageofSDGenvironment-relatedindicatorswithsufficientdataforanalysisofprogress

ExecutiveSummary

100

80

59%

60

42%

40

34%

20

0

2018

2020

2022

ThismajorimprovementindataavailabilityresultsfromasustainedinvestmentbycountriesintheirnationalstatisticalsystemstocollectandreportdataforSDGindicatorsaspartoftheirsustainabledevelopmentprogrammes,supportedbycapacitydevelopmenteffortsbycustodianagencies.

Thefurtherdevelopmentofmethodologiesthatusenewdatasourcesalsocontributestoimproveddataavailability.Manynationalstatisticaloffices(NSOs)arealreadyexperimentingwithusingbigdataintheproductionofofficialstatistics.Currently,thedominantbigdatatypesincludeEarthObservation(EO)data,citizensciencedataandothersensornetworkdata,combinedwithadvancedanalyticaltechniques(e.g.machinelearning,geospatialmodellingandgeostatisticalmodelling).

TheUnitedNationsEnvironmentProgramme’s(UNEP)Measuring

Progressseriesofreportsprovidesanoverviewoftheprogress

madeindataavailabilityforthe92environment-relatedSustainable

DevelopmentGoal(SDG)indicators,coupledwithimprovement

ordegradationinthetrendofeachindicator.Italsoexplores

thepotentialandlimitationsofusingstatisticalanalysisto

demonstrateinterlinkagesbetweenindicatorpairstobetterinform

policymakersofthesynergiesandtrade-offsbetweenSDGs.

Theindicatorsaredividedintofourcategories:(i)stateofthe

environment,(ii)driversofchange,(iii)stateofhumanwell-being

and(iv)socioeconomicandenvironmentalfactors.Thisreport

explorestheuseofmultivariatestatisticalanalysisusingwater-

relatedecosystems(freshwaterandmarine)asanexampleofthe

utilityofthisapproachtoexplorehowecosystemsareimpactedby

drivers,pressuresandactionsatmultiplescales.

Substantialimprovementinglobaldataavailability

Globalanalysisoftheprogressofthe92environment-related

SDGindicatorsdemonstratesanimprovementindataavailability,

resultingfromadditionaldatabeingreportedbycountriesleading

totheavailabilityofsufficientdatatoaggregateatregionaland

globallevels.In2022,theenvironment-relatedSDGindicatorswith

sufficientdatatoanalysewereestimatedat59percent,upfrom

42percentin2020and34percentin2018.Indicatorswithmore

dataavailablearemostlyfoundinSDG6onfreshwater,SDG7on

energy,SDG12onsustainableconsumptionandproduction,SDG

13onclimatechange,SDG14onlifebelowwaterandSDG15on

lifeonland,withthemostimprovementindataavailabilityreported

intheLatinAmericaandCaribbean,NorthernAfrica,andEurope

regions.

41%

21%

38%

51%

12%

37%

51%

17%

32%

55%

13%

32%

55%

14%

30%

55%

16%

28%

59%

16%

25%

50%

22%

28%

45%

16%

39%

80

60

40

20

Measuringprogress:Water-relatedecosystemsandtheSDGs

FigureES.2Environment-relatedSDGindicatorsdatatrend,

globallevel

Global

38%

41%

21%

Nodataorinsufficientdata

LittlechangeorPositivetrend

anegativetrend

Statusofenvironment-relatedSDGindicators

In2022,atthegloballevel38percentofthe92environment-relatedindicatorsshowedpositivechange,indicatingenvironmentalimprovement,and21percentshowedlittleornegativechange.ThemostindicatorsshowingpositivetrendswerethoserelatedtoSDG9oninfrastructure,SDG7onenergyandSDG6onfreshwater.

TheregionswiththehighestproportionofSDGenvironment-relatedindicatorsshowingenvironmentalimprovementaretheLatinAmericaandtheCaribbeanregion(39percent)andtheCentralandSouthernAsiasubregion(38percent).Theregionswiththelowestproportionofindicatorsshowingenvironmental

4

FigureES.3Environment-relatedSDGindicatorsdatatrend,

globalandregionallevels

54%

21%

25%

0

LatinGlobalCentralSub-WesternNorthernEasternEuropeOceaniaNorthern

America

andthe

andSaharanAsia

SouthernAfrica

Africaand

SEAsia

Caribbean

Asia

America

Positivetrend

Nodataorinsufficientdata

Littlechangeoranegativetrend

degradationareCentralandSouthernAsia(12percent),WesternAsia(13percent)andNorthernAfrica(14percent).

Whilemeasuringtheprogressofthe92environment-relatedSDGindicatorsfocusesonevaluatingtrends,itdoesnotassessthemagnitudeofthetrendsorprogresstowardsmeetingtargetsassociatedwithspecificindicators.

Advancingstatisticalmethodsforidentifying

interlinkages

Thisreportadvancesthestatisticalmethodstobetterassess

andunderstandtheinterlinkagesbetweenpairsofindicators

throughtheuseofmultivariatestatisticalanalysis.This

buildsonthemethodsusedinthepreviousreport,Measuring

Progress:EnvironmentandtheSDGs,whichexploredtheuseof

correlationanalysistoidentifytheinterlinkagesbetweenpairsof

indicators.Basedonthedriver-pressure-state-impact-response(DPSIR)framework,theanalysisidentifieshowonestateoftheenvironmentindicatorisrelatestoindicatorsofamultitudeofdriversofchangeaswellassocioeconomicandenvironmentalfactors.Thestatisticalanalysisfocusesonfreshwater-andmarine-relatedecosystemsandisconductedattheglobal,national(ColombiaandMongolia)andbasin(Poyangbasin,China)levels.

Globalpolicydiscussionsbenefitfromnewanalyticalapproachestounderstandingtheunderlyinginterlinkagesanddriversofindicatortrends.Theanalyticalapproachusedhasthepotentialtocontributetoamorepolicy-relevantintegratedanalysis.Theanalysisconfirmedmanyknowninterlinkagesbetweenfreshwater-andmarine-relatedecosystemsandvariabledrivers.Italsoidentifiedseveralnewinterlinkagesthatcannotbeeasilyexplainedwiththeexistingliterature,requiringfurtherinvestigationtoidentifywhetherthesearecovariatesornewlyidentifieddrivers.Considerationofthesenewdriversmaybehighlyrelevanttothedevelopmentofnewinnovativepoliciestoprotecttheseecosystems.

Evaluatingindicatorsatthenationallevelprovidesamorecomprehensiveandactionableinterpretationofkeyinterlinkages

thanatthegloballevel,butglobal-leveltrendsremaincriticaltoassessingoverallprogressinachievingtheSDGs.Auniqueaspectoftheanalysisistheinclusionofbothglobal-levelandnational-levelinterlinkages.Whilesomeinterlinkagesweredetectedatbothscales,otherswereonlyidentifiedatthemoregranularnationalscale.Thevariouspositiveandnegativerelationshipsidentifiedbetweenthestateoftheecosystem,directdriversofchange,stateofhumanwell-being,andsocioeconomicandenvironmentalfactorshighlighttheimportanceofconsideringtheimpactofindirectlyrelatedfactors.Whilesomeimpactingfactorsarecommoninglobalandnationalsettings,identifyingothernationalfactorsconsideredtohavesynergiesortrade-offswithwater-

relatedecosystemsisimperativetoinformthedevelopmentoftargetedpoliciesandinterventionstoprotecttheseecosystems.

Findingsforfreshwater-andmarine-related

ecosystems

Theanalysisidentifiedstronginterlinkagesrelatedtopoliciesthatintegratelandandwaterconservation,ensuresuitablewaterinfrastructureinurbanareas,providemitigationofpollutionandaddressimpactsfromwaterwithdrawalsassociatedwitheconomicactivity.Theanalysisrevealedmostlyexamplesofrelationshipsconsistentwithpublishedevidenceandintuition.Forexample,populationlivinginurbanareaswasfoundtobepositivelyinterlinkedtoadeclineinmarine-relatedecosystemindicators,confirmingtheimpactofeffluentsfromlargecitiesontheeutrophicationofcoastalareas.

Theinclusionofglobalandnationallevelsinthestatisticalanalysisprovidedanopportunitytoverifyglobalinterlinkages

withnationalcasestudiesandhighlighttheimpactofdatadisaggregation.Forinstance,conservationeffortswereconsistentlypositivelyinterlinkedwithfreshwater-relatedecosystemindicatorsatbothlevels,whilewater-useefficiencyindicatorswereinterlinkedwithfreshwater-relatedecosystemsonlyatthenationallevel.

Recommendations

Theanalyticalapproachhasexposedsomeofthecriticaldatagapsinwater-relatedecosystemsandhaschallengedthesuitabilityofsomeindicatorstodetectmeaningfulchange

inthehealthoffreshwater-andmarine-relatedecosystems.Thefreshwater-relatedecosystemassessmentwaslimitedtointerlinkagesbetweenvariousmetricsoftheareaoffreshwater

5

Measuringprogress:Water-relatedecosystemsandtheSDGs

ineachcountry.Similarly,thelackofdisaggregatedcatchment-leveldataconstrainedtheabilitytomeaningfullyassesscoastalecosystems.Whilethesedatasetsbenefitfromtheabilitytoprovideconsistentmeasurementusingremotesensingacrosstheglobe,theyarelimitedintheirabilitytomeasurethewaterquality,volumesorecosystemhealthofwaterbodies.Theremaybeopportunitiestofurtherutilizecitizenscience,satelliteimagery,low-costinsitumonitoringandbigdatatoproducemeasuresofwaterqualityand/orvolumewithinvariouswaterbodies.

ItiscriticalthatthesuccessesoftheSDGindicatorframeworkbetranslatedintodisaggregateddatacapableofinformingsubnationalpolicieswhilemaintainingcompatibilityataglobalscale.Dataandindicatorsarekeyforinformeddecision-makingandpolicydesigntoknowhowrealisticoptionsare,whatinconsistenciesmightresultfromdecisions,howthecostofsuchinconsistenciescanbemitigatedandhowtrade-offscanbeexplained.Consideringthatmostenvironmentalpolicies,includingwaterpolicies,aredevelopedatthenationalorsubnationalscale,disaggregateddataisneededtoinformpolicy.

Re-evaluatingthesuitabilityofthecurrentindicatormethodologiestoparsetruechangeintheenvironmentfromdataandmethodologicalartefactsisneededtobolsterdatacollectionforotherenvironment-relatedindicators.Moreover,theanalysisrevealedtheimportanceofincorporatingmoreecologically

relevantspatialgroupings.Catchment-basedorecosystem-basedaggregationsmayprovidemoreinsightintotheecologicaldimensionofmanyoftheinterlinkagesidentifiedforfresh