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Zoll, Adaptation in Austin—1
ClimateAdaptationandSocialVulnerabilityinAustin,Texas
by
DeidreZoll
Fall2017
ClimateAdaptationandSocialVulnerabilityinAustin,Texas
Introduction
Howcitiesmitigateclimatechange,adapttoimpacts,andimprovetheresilienceof
vulnerablepopulationsaresomeofthedefiningchallengesofourgeneration.Global
temperaturesarerisingduetoanthropogenicfactors,withdetrimental,irreversible
impactsforpeopleandnature.Incities,predictedimpactsrangefromsealevelriseand
floodingtoincreasedfoodinsecurity,withwideagreementthatmanyoftheseriskswill
havedisproportionateimpactsonsociallyvulnerablegroupsregardlessofwhichcountry
theylivein(IPCC,2014).Thispaperfocusesonurbanplanningresponsestopredictionsfor
increasedfloodingfromextremeprecipitationeventsduetoclimatechangeinAustin.
Citiesfocusedonfloodingaredeployingavarietyofadaptationinterventionsthatrange
fromsmall-scalegreeninfrastructuresolutionslikeraingardenstomajorengineering
projectslikefloodwalls.Asclimateadaptationpilotprojectshavebeendeployed,asmall
butgrowingnumberofresearchershaveraisedconcernsthattheseinterventionsmay
exacerbateexistingsocialvulnerabilityorcreateadditionalnewformsofinequality
(Anguelovskietal.,2016;Muñoz&Tate,2016;Shietal.,2016).Understandingthespatial
relationshipbetweensocialandenvironmentalvulnerabilityandadaptationresponsesis
Zoll, Adaptation in Austin—2
criticalforcityplannerscommittedtointerventionsthatameliorate,orattheveryleastdo
notincrease,socialvulnerabilitytoclimatechange.
AsmallnumberofU.S.citiesareimplementingclimateadaptationmeasures,and
manyofthesemeasureshavenotbeeninplacelongenoughtoevaluate.WhileU.S.cities
havebeenworkingonclimatechangeissuessincethe1990s,adaptationplanningisa
relativelynewfieldthatgainedmomentumduringtheearly2000s.Approximately60U.S.
citieswithpopulationsgreaterthan50,000,aredevelopingorimplementingclimate
adaptionplans(Schrock,Bassett,&Green,2015),however,mostofthosecitiesarestillin
theplanningphase(Bierbaumetal.,2013;Ford,Berrang-Ford,&Paterson,2011;Lyles,
Berke,&HeimanOverstreet,2017).WithintheU.S.adaptationliterature,mostflooding,
socialvulnerability,andclimateadaptationresearchisfocusedoncoastalfloodingbecause
ofthedoubleenvironmentalriskofsealevelriseandextremeweatherevents.Thatwork
hasthreemajorfindings.First,measuresofsocialvulnerabilitycanidentifycommunities
thatfacegreaterexposuretofloodingriskandtougherrecoverypaths(Bautista,Hanhardt,
Osorio,&Dwyer,2014;Englishetal.,2013;Graham,Debucquoy,&Anguelovski,2016;
Maldonado,Collins,Grineski,&Chakraborty,2016;vanZandtetal.,2012).Second,
measuresofrace/ethnicityandincomearesocialvulnerabilityvariablesthathave
significantimpactsinregardstoriskandadaptivecapacity(Bautistaetal.,2014;Englishet
al.,2013;Grahametal.,2016;Maantay&Maroko,2009;Maldonadoetal.,2016;vanZandt
etal.,2012).Third,wheresocialimpactsfromadaptationplanshavebeenevaluated,
researchhighlightsthatadaptationinitiativescanexacerbatesocialinequalitiesespecially
aroundissuesofclassandrace(Anguelovskietal.,2016;Bautistaetal.,2014;Grahamet
al.,2016;Shietal.,2016).
Thecurrentbodyofquantitativeresearchexaminingtherelationshipbetween
urbanclimateadaptationandsocialvulnerabilityisextremelylimitedandtendstofocus
on:decision-makingandgovernance;planevaluation;orcasestudiesfromthefewcities
thathaveundertakenadaptationefforts(Broto&Bulkeley,2013;Vogel&Henstra,2015;
Wheeler,2008).Verylittlequantitativeworkexiststounderstandthespatialrelationship
betweensocialvulnerabilityandclimateadaptationsitinginU.S.cities(Araosetal.,2016;
Shietal.,2016;Woodruff&Stults,2016).Thisgapisalsopresentinresearchthatevaluates
climateadaptationplansassociatedwithinlandflooding.Thereissomeresearchinthe
Zoll, Adaptation in Austin—3
hazardsliteraturethatexaminestheimpactsoffloodbuyouts(Muñoz&Tate,2016;Tate,
Strong,Kraus,&Xiong,2016).Whilethosebuyoutsarenotexplicitlytiedtoadaptation
planning,theydomimica“plannedretreat”adaptationpath.Importantly,thesestudies
alsofindracialandclassinequalitiesassociatedwithfloodbuyouts(Muñoz&Tate,2016).
Toaddresstheseliteraturegaps,thispaperusesGISspatialanalysisandlogistic
regressiontoanswertwodescriptivequestions:
1) Arecensusblockgroupswithhigherpercentagesoflow-income,non-White,and
sociallyvulnerablegroupsmorelikelytobelocatedinfloodzones?
2) Istherearelationshipbetweenmeasuresofincome,race,socialvulnerability,flood
risk,andtheprobabilitythattheCityofAustinwillsiteaclimateadaptation
interventioninacensusblockgroup?
Design
ResearchforthispaperwasconductedinAustin,Texas,whichhasbothsignificant
inlandfloodingriskandlimitedbutgrowingeffortstoincreasefloodresilience.Floodingis
consideredoneofthedeadliestnaturalhazardsintheUnitedStates,andTexasconsistently
hasthehighestnumberofflooddeathanddamagesinthecountry(CityofAustin,2016b).
Between1996-2014,Austinexperienced76floodeventsresultingin10casualties,50
injuries,and$105millioninpropertydamage(CityofAustin,2016b).KatherineHayhoe’s
(2014)climatechangeforecastforthecityindicatesthatAustinwillseelimitedchangesin
averageannualprecipitation,butwillseeincreasesinextremerainfalleventswhichcould
intensifythefrequencyandscaleoffloodinginthecity(Hayhoe,2014).Inresponsetopast
floodeventsandthesefuturepredictions,theCityofAustinhasundertakeninitialplanning
assessmentsandcharacterizedexistingandfutureprojectsthatcouldimproveflood
resilienceinthecity(CityofAustin,2014).Theseinitialeffortsofferanopportune
reflectionpointwhilealsoprovidingbaselinedataagainstwhichtotrackfuture
developments.
Usinganon-experimentalresearchdesign,thisstudyreliesonspatialanalysisand
logisticregressiontoquantitativelydescriberelationshipsbetweenincome,race,social
vulnerability,floodrisk,andproximitytoclimateadaptationinitiatives.Analysiswas
Zoll, Adaptation in Austin—4
completedontwocategoriesofdependentvariables:exposuretofloodriskandproximity
tofloodadaptationinitiatives.13independentvariablesareused,fourofwhichrepresent
demographicdataattheblockgrouplevel.Asocialvulnerabilityindexandfiveassociated
themeswerecreatedusing2015ACSfive-yearestimatestoaddressissuesassociatedwith
compoundingsocialstressors.Thelastvariable,floodrisk,isusedasanindependent
variablewhenattemptingtounderstandtheprobabilityofclimateadaptationsiting.
Data
DataforthisprojectwerecompiledfromAugustthroughNovember2017fromthe
ACS2015five-yearestimates,FEMAdigitalfloodplainmaps,andCityofAustinGISdataon
existingfloodresilienceefforts.Table1providesvariablesummarystatistics.
Dependentvariables
ExposuretofloodriskwasanalyzedusingFEMA’s2016digitalfloodplainmapsfor
thegreaterAustinarea,whichweredownloadedfromtheCityofAustin’swebsiteand
importedintoArcGISPro.GISanalysiswasusedtocategorizecensusblockgroupsthat
intersectwithfloodplainstoclassifyfloodriskvariables.Initialcategorizationseparated
100-and500-yearfloodplains,butonlythreecensusblockgroupshadexposureto500-
yearfloodplainswithoutexposureto100-yearfloodplains.Formoreappropriatestatistical
analysis,100-and500-yearfloodplainswerecollapsedintoonecategoryandthosethree
censusblockgroupswerecodedas“1”intheflooddummyvariable.
Flood–Dummyvariable
0=Nofloodrisk–95-100%ofthecensusblockgroupareaisclassifiedasFEMADor
Xzones.
1=Floodrisk–6-100%ofthecensusblockgroupareaisclassifiedasFEMAAor
FEMAX500zonesindicatinga1%and.2%annualchanceoffloodingrespectively.
FloodRisk–Continuousvariable
Thepercentofthecensusblockgroupareathatisinthe100-and500-year
floodplains.
Zoll, Adaptation in Austin—5
Dataforfloodadaptationinitiativescaptureexistingeffortsbythecitytoimprove
floodresilience.Fivecitydocuments(AustinHazardMitigationPlan,ImagineAustin
ComprehensivePlan,AustinWatershedMasterPlan,TowardsaClimate-ResilientAustin,
andtheFloodMitigationTaskForceReport(CityofAustin,2014,2015,2016a,2016b,
2016c)wereusedtoidentifyexistingcityeffortstoaddressclimateadaptationorflood
resilience.AvailableGISshapefilesweredownloadedfromtheCityofAustin’sGISportal
andgroupedintothreecategories:floodbuyouts,small-scalegreeninfrastructure,and
plannedfuturetreecanopy.Whencensusblockgroupshadmultipleadaptationcategories
thedummyvariablewasassignedbasedonhierarchyoftreatmentwherebuyoutsoutrank
greeninfrastructurewhichoutranksfuturetree-canopydevelopment.GISanalysiswas
usedtocategorizecensusblockgroupsas:
Adapt_1–Dummyvariable
0=Nobuyouts
1=Censusblockgroupcontainsatleastonefloodbuyout
Adapt_2–Dummyvariable
0=Nosmallscalegreeninfrastructure
1=Censusblockgroupcontainsatleastonesmallscalegreeninfrastructure
project(thisincludesgreeninfrastructurelikebioswalesand
stormwaterpondsmaintainedbytheCityofAustin)
Adapt_3–Dummyvariable
0=Nohighpriorityareasforfuturetreecanopyexpansion
1=Censusblockgroupthatcontainshighpriorityareasforfuturetreecanopy
expansion
Actionsthatareappliedevenlyacrossthecity(e.g.curbandgutterrequirements)
wereexcludedfromanalysis.Hardinfrastructureprojects(tunnels,floodwalls,etc.)were
alsoexcludedduetodataavailability.Whilerelatedtoclimateadaptation,extensive
existinggreeninfrastructure(parks,wetlands,existingtreecanopy)wereexcludedfrom
analysistofocusonexplicateflood/climateresilienceprojects.Areasprioritizedforfuture
Zoll, Adaptation in Austin—6
treecanopyexpansionwereincludedbecausetheprioritizationdecisioncriteriausedby
thecityincludesmeasuresforclimateandfloodconcerns.
Independentvariables
Measuresofsocialvulnerabilitywerecompiledfromthe2015ACSfive-year
estimatesattheblockgrouplevel.13independentvariablesareused,fourofwhich
representcommondemographicmeasuresofincomeandrace/ethnicitythatareusedin
environmentaljusticeresearch.Theremainingninevariablesaremeasuresofsocial
vulnerability.Forthisanalysis,Idefinesocialvulnerabilityas:pre-existingcharacteristics
ofgroupsorconditionswithincommunitiesthatcreateunequalexposuretorisk,increased
hardshipimmediatelyfollowingevents,andunequallong-termrecoveryconcerns.Social
vulnerabilityvariableswereselectedbasedonprevioushazardandenvironmentaljustice
literaturesthathaveanalyzedinequalityinexposuretoriskandrecovery(Chakraborty&
Collins,2014;Maantay&Maroko,2009;Maldonadoetal.,2016;Rumbach&Kudva,2011;
vanZandtetal.,2012).
DemographicVariables
MedianHouseholdIncome-USD(2015)reportedoverthepast12months
Black/AfricanAmerican-%ofthepopulationthatisnon-HispanicBlack/African
American
Asian-%ofthepopulationthatisAsian
Latinx-%ofthepopulationthatisNon-WhiteHispanic
SocialVulnerabilityIndexandThemes
Asocialvulnerabilityindexvariablewascreatedfollowingindexmethodsthathave
beenusedinboththehazardandenvironmentaljusticeliteratures(CDC,2016;Ekstrom&
Moser,2013;Pastor,M.,Sadd,J.,Morello-frosch,2013;vanZandtetal.,2012;Vargo,Stone,
Habeeb,Liu,&Russell,2016).11variablesweregroupedintofivethemesandgivenatotal
scoreperthemeandthenallthemeswereaddedtogetheranddividedby10foratotal
scorebetween0-100.
Zoll, Adaptation in Austin—7
Themes
SocioeconomicTheme–NoInsurance+HouseholdsBelowPoverty
NoInsurance-%ofpopulationwithouthealthinsurance
HouseholdsBelowPoverty-%ofhouseholdsbelowpovertyinthepast12months
HouseholdTheme–Age+FemaleHeadedHousehold+Disability
Female-HeadedHousehold-%ofthepopulationthathasasinglefemaleheadofhousehold
withoneormoredependents
AgeUnder17andOver65-%ofthepopulationthatisunder17orover65
HouseholdswithaDisability-%ofhouseholdswhereatleastonememberhasadisability
MinorityTheme–Totalpopulationthatisnotnon-HispanicWhite
Education/LanguageTheme–Education+English
HouseholdswithlimitedEnglish-%ofhouseholdswhereallmembersovertheageof14
havelimitedEnglishskills
EducationLevelsBelowaHighSchoolDiploma-%ofthepopulationover25with
educationallevelsbelowaHighSchoolDiploma
Housing/TransportationTheme–Mobile+NoVehicle
MobileHome-%populationlivinginamobilehome.
HouseholdswithoutaVehicle-%ofhouseholdswithoutavehicle.
SocialVulnerabilityIndex
SocialVulnerabilityScore–(Socioeconomic+Household+Minority+
Education/Language+Housing/Transportation)/10
Dataquality
AvailabledataforallcensusblockgroupswithintheCityofAustinjurisdiction
(N=607)weregatheredfromACS.ACSdataarepopulationsamplescollectedovera5-year
Zoll, Adaptation in Austin—8
periodaspartofarollingdatacollectioneffort.FEMAdigitalfloodmapsareestimatesof
floodplains,whicharecommonlyusedinplanningandresearchefforts.Adaptationdata
aretrackedbytheCityofAustinandaresupposedtorepresentallcurrenteffortsbythe
city.ACSdatapresentsthreemajorissues.First,whiletheASCdataiscurrentandrolling,it
hassignificantmeasuresoferror.Second,duringdatagatheringefforts19blockgroups
wereremovedbecausetheyhadincompletedata.Third,removingtheseblockgroupsis
standardpractice,butitcausedtheremovalofonecensusblockgroupthathas145flood
buyoutproperties.AfixforincompleteACSdataistousetheDecennialCensus,however
point-in-timedatafrom2010istoodatedtouseinthisstudy.Outsideofusingcensus
populationdata,theanalysiswouldhavebenefitedfromdataonpropertyvalues,hard
infrastructureadaptationefforts,andimpactsfrompreviousfloodevents.Populationdata
woulddecreasebiasandincreaseprecision.Propertyvaluesarelikelyacofounding
variableforhardinfrastructureprojects,butcouldidentifyamajordriverincitydecision
makingforadaptationsiting.Hardinfrastructuretendstorequiresignificanturban
planningandpublicfinanceefforts,whichmakesitamoreimpactfuldependentvariable.
Giventhedatalimitationsandconstrainedfocusinthispaper,patternsshouldnotbe
generalizedoutsideoffloodriskandadaptationeffortsinAustin.
Zoll, Adaptation in Austin—9
Table1:Summarystatistics
Variable Mean SD Min MaxMedianHouseholdIncome($1,000) 68.7 36.7 5.8 232.8
White(%) 53.8 25.0 0.0 95.0Black/AfricanAmerican(%) 6.7 9.2 0.0 66.0
Asian(%) 5.7 7.8 0.0 63.0Latinx(%) 31.1 23.3 0.0 100.0
HHBelowPoverty(%) 5.8 4.9 0.0 39.0NoInsurance(%) 6.0 7.5 0.0 51.0
FemaleHeadedHousehold(%) 10.1 3.4 0.0 19.0Under17andOver65(%) 30.0 10.7 0.0 58.0
Householdsonememberwithdisability(%) 16.4 9.8 0.0 53.0HouseholdswithlimitedEnglish(%) 7.2 8.9 0.0 58.0LessthanaHighSchoolDiploma(%) 11.7 14.0 0.0 71.0
Mobilehomes(%) 3.4 10.9 0.0 86.0Householdswithoutavehicle(%) 6.0 7.5 0.0 51.0
SocioeconomicTheme 22.6 15.1 0.0 68.0HouseholdTheme 56.6 18.2 1.0 111.0MinorityTheme 46.2 25.0 5.0 100.0
EducationandLanguageTheme 19.0 19.6 0.0 101.0HousingandMobilityTheme 9.4 12.7 0.0 89.0
SocialVulnerabilityScore 15.4 6.9 5.0 36.5Floodrisk(%) 11.1 17.3 0.0 100
100-yearFloodrisk(dummy) 0.5 0.5 0.0 1.0Adaptation1Floodbuyout(dummy) 0.0 0.1 0.0 1.0
Adaptation2Greeninfrastructure(dummy) 0.3 0.5 0.0 1.0Adaptation3Futuretreecanopy(dummy) 0.2 0.4 0.0 1.0
Analysis
SpatialAnalysis
SpatialanalysiswasconductedinArcGISProtocalculatecensusblockvaluesfor
floodriskandadaptioninitiatives.AllGISfilesareprojectedtotheWGS84(DD)coordinate
systembeforeanyspatialanalysiswascompleted.ACSdatawasdownloadedforTravis,
Hays,andWilliamsCountiesandcompiledintooneexceldocument.19blockgroupswere
removedbecausetheyhadincompletedata.ACSdatawasnormalizedbydividingthe
variablecountbytheappropriatepopulationmeasuretogeta%valueforthecensusblock
group(e.g.5householdswithoutavehicle,300householdsintheblockgroup=1.7%).ACS
datawasthenjoinedtoCensusTIGER/LineshapefileswhichwereclippedtotheCityof
Austinjurisdictionalboundaries.FEMA’s2016digitalfloodplainmapsforthegreater
Zoll, Adaptation in Austin—10
AustinareaweredownloadedfromtheCityofAustin’sGISdataportalandusedtocreate
newshapefilesforeachofthefloodzonevariables.Adaptationshapefilesforwere
downloadedfromtheCityofAustin’sGISdataportalandusedtocreatenewshapefilesfor
eachoftheadaptationvariables.
Layerswereoverlaidtoproducemapsshowingrelationshipsbetweensocial
vulnerability,floodrisk,andadaptation.Thespatialanalysistool“tabulateintersection”
wasusedtocross-tabulatethetotalareaineachcensusblockgroupthatisimpactedby
eachfloodcategoryandthenumberofadaptationactionsineachcensusblockgroup.Pivot
tableswereusedtocreatecorrespondingdummyvariablesforeachcensusblockgroup.A
finaltablewasexportedfromArcGISProandthenimportedtoSTATA.
GISoverlayshowsaconcentrationofsocialvulnerability(Figure1),minorities
(Figure2),andlower-incomehouseholds(Figure3)ontheeastsideofAustin.Whileflood
riskisfairlywidespreadinthecity,therearepocketsofconcentrationontheeast,north-
west,andsouthernpartsofthecity.Adaptationeffortsareconcentratedalongthecentral
partofthecityinanorthtosouthtransect(Figure4)andtendtonotbelocatednearareas
ofgreaterfloodrisk,withtheexceptionofthefloodbuyoutprogram.Adaptationefforts
alsotendtoconcentrateonthewestsideofthecityinareaswithlowersocialvulnerability
(Figure5),lowerminorityconcentrations(Figure6),andhigherincomes(Figure7).
Zoll, Adaptation in Austin—11
Figure 1 Social vulnerability and flood risk, Austin
Zoll, Adaptation in Austin—12
Figure 2 Minorities and flood risk, Austin
Zoll, Adaptation in Austin—13
Figure 3 Income and flood risk, Austin
Zoll, Adaptation in Austin—14
Figure 4 Adaptation and flood risk, Austin
Zoll, Adaptation in Austin—15
Figure 5 Adaptation and social vulnerabilities, Austin
Zoll, Adaptation in Austin—16
Figure 6 Adaptation and minorities, Austin
Zoll, Adaptation in Austin—17
Figure 7 Adaptation and income, Austin
StatisticalAnalysis
Thefirststatisticalanalysiscomparesthemeansofindependentvariableswithtwo
categoriesof100-yearfloodrisk(noriskandrisk)andfourcategoriesofadaptation(no
adaptation,buyout,small-scalegreeninfrastructure,andfuturetreecanopy)usingtwo-tail
T-testsandANOVAtestsrespectively.Thesecondstatisticalanalysisuseslogistic
regressiontomodeltheoddsofexposuretofloodriskandproximitytoadaptation
initiativesbasedonindependentvariables.Threelogisticregressionmodelswererunon
floodplainsandadaptationprobability.Thefirsttestsdemographicindependentvariables.
Thesecondteststhesocialvulnerabilitythemes.Thethirdteststhesocialvulnerability
index.
Logisticregressioniscommonlyusedinenvironmentaljusticesitingresearch.It
assumesmutuallyexclusiveunitsinthedependentvariables,alargen,independent
Zoll, Adaptation in Austin—18
observations,nomulticollinearity,andnosignificantoutliers.Datausedinthispapermeet
mostoftheseassumptions.Thedependentvariablesarebinarydummyvariablesandthere
are588observations.Multicollinearitywastestedwiththresholdssetatatolerancescore
oflessthan.1oraVIFscoreofmorethan10,noneofthevariablesshowevidenceof
multicollinearity.Fiveoutlierswereinspectedandkeptinthemodelbecausetheydidnot
exhibitdataentryirregularities.
Whilelogisticregressioniscommonlyusedinenvironmentaljusticesiting
literature,otherstatisticaltechniquesmaybeappropriateforthestudyquestions.Logistic
regressiondoesnotaccountforthevariationinfloodriskoradaptationinitiatives.For
example,acensusblockgroupwith80%oftheblockareaina100-yearfloodplainis
treatedthesameasablockgroupwith10%oftheblockareaina100-yearfloodplain.OLS
mightprovideamorenuancedanalysisoftherelationshipsbetweensocialvulnerability,
floodrisk,andadaptationsiting.
Results
SummarystatisticsinTable1indicatevariableswithwiderangesandhighstandard
deviationswhichsuggesthighlevelsofvariability.Table2presentsvariablemeansper
floodriskgroup(no100-yearrisk,100-yearrisk)andt-testvalues.T-testsindicatethat
censusblockgroupswithexposureto100-yearfloodriskhavestatisticallysignificant
higheraveragesof:householdincome[(M=71.8and65.0)]peopleunder17andover65
[(M=30.8and29.1)],andmobilehomes[(M=4.6and1.9)].Censusblockgroupswith
exposuretofloodriskhaveastatisticallysignificantloweraverageofhouseholdswithouta
vehicle[(M=5.2and6.9)].
Zoll, Adaptation in Austin—19
Table2:FloodMeansandT-test
VariableNoRisk(N=270)
Risk(N=318) T-test
MedianHHIncome($1,000) 65.0 71.8 -2.3*White(%) 53.2 54.4 -.6
Black/AfricanAmerican(%) 6.8 6.7 .7Asian(%) 6.2 5.3 1.3Latinx(%) 31.1 31.0 .04
HHBelowPoverty(%) 6.4 5.3 2.6**
NoInsurance(%) 17.4 16.3 1.1FemaleHeadedHH(%) 10.1 10.1 .1
Under17andOver65(%) 29.1 30.8 -2*HHlimitedEng(%) 16.6 16.3 .4
HHonedisability(%) 7.0 7.4 -.5<HighSchoolDiploma(%) 11.8 11.7 .1
MobileHomes(%) 1.9 4.6 -3.1**HHw/oavehicle(%) 6.9 5.2 2.7**
SocioeconomicTheme 23.8 21.6 1.7
HouseholdTheme 55.8 57.2 -1.0MinorityTheme 46.8 45.2 .6
Ed/LanguageTheme 18.8 19.1 -.1House/MobilityTheme 8.8 9.8 -1.0
SocialVulnerabilityScore 15.4 15.3 .1
One-wayANOVAtestswereruntoexamineifproximitytoadaptationactionsis
differentfordemographicgroupsandsocialvulnerabilitythemes.Censusblockgroups
wereclassifiedintofouradaptationcategories:noadaptation(n=330),floodbuyouts(n=
8),small-scalegreeninfrastructure(n=167)andfuturetreecanopy(n=83).Table3
presentsANOVAtestresultsandshowsthatallindependentvariableshavestatistically
significantdifferencesbetweengroupsexceptforfemale-headedhouseholds,under17and
over65,andmobilehomes.Thisindicatesthattherearedifferencesbetweendemographic
andsocioeconomicthemesintermsofproximitytoadaptationactions.Resultspoint
towardscommonalitiesbetweenthedemographicaveragesfornoadaptationandsmall-
scalegreeninfrastructuregroups.Thosetwogroupsarewealthier(Mmedianhhincome=
70,900and74,800)andWhiter(M=58.4%and54.5%)whencomparedtotheflood
buyoutandfuturetreecanopygroups(Mmedianhhincome=54,200and48,700)(M%
White30.8and36.5).Thosetrendscontinuefortheremainingstatisticallysignificant
Zoll, Adaptation in Austin—20
variableswithindicatorofvulnerabilityhavinghighermeansforthefloodbuyoutand
futuretreecanopygroups.Table3:AdaptationMeansandANOVAF-test
Variable
NoAdaptation(N=330)
FloodBuyout(N=8)
GreenInfrastruc.(N=167)
FutureTreeCanopy(N=83) ANOVAF
MedianHHIncome($1,000) 70.9 54.2 74.8 48.7 11.1***White(%) 58.4 30.8 54.5 36.5 21.2***
Black/AfricanAmerican(%) 6.1 6.0 6.5 9.9 3.9**Asian(%) 5.6 0.6 6.9 4.1 3.7**Latinx(%) 27.2 60.6 29.3 47.2 23.5***
HHBelowPoverty(%) 6.1 5.5 4.7 7.0 4.9**
NoInsurance(%) 15.1 23.8 14.9 26.5 23.4***FemaleHeadedHH(%) 10.0 11.0 10.0 10.7 1.2
Under17andOver65(%) 29.4 33.1 31.5 29.5 1.7HHlimitedEng(%) 6.3 15.0 6.1 12.7 15.5***
HHonedisability(%) 15.0 23.4 16.2 21.8 12.6***<HighSchoolDiploma(%) 9.6 25.8 9.8 22.8 26.81***
MobileHomes(%) 3.0 9.6 3.9 3.3 1.2HHw/oavehicle(%) 6.2 1.5 4.7 8.1 5.1**
FloodRisk(%) 10.3 39.5 10.8 11.9 7.8***
SocioeconomicTheme 21.2 29.2 19.7 33.5 19.7***HouseholdTheme 54.5 67.5 57.7 62.0 5.24**MinorityTheme 41.6 69.3 45.5 63.5 21.3***
Ed/LanguageTheme 15.9 40.8 15.9 35.5 31.0***House/MobilityTheme 9.2 11.1 8.6 11.5 1.0
SocialVulnerabilityScore 14.2 21.8 14.7 20.6 24.5***p<0.05,**p<0.01,***p<0.000
LogisticRegression
Logisticregressiontechniqueswereusedtomodeltherelationshipbetweenflood
riskexposureagainstincomeandrace,andsocialvulnerabilitythemes.Thevariable
“percentofWhiteresidents”isremovedfromthelogisticmodeltoavoidissueswith
multicollinearity.ResultsarepresentedinTable4andindicatethatfordemographic
variables,aoneunitincrease($1,000)inmedianhouseholdincomemultipliestheoddsof
beinglocatedina100-yearfloodplainby1.008.Logisticregressionof100-yearriskagainst
socialvulnerabilitythemesandtheoverallsocialvulnerabilityindexscoreindicatethatthe
oddsoflivinginacensusblockgroupthatisexposedtofloodriskmultiplyby.977and
Zoll, Adaptation in Austin—21
1.013foreachonepointincreaseinthesocioeconomicandhousing/mobilitythemes.The
remainingindependentvariablesarenotstatisticallysignificant.
Table4:LogitResultsforFloodRiskandDemographicsandSVI
ModelOR Model2OR Model3OR
Variable 100-year 100-year 100-yearMedianHHIncome($1,000) 1.008*** Black/AfricanAmerican(%) 1.005
Asian(%) 0.986 Latinx(%) 1.004
SocioeconomicTheme 0.977**
HouseholdTheme 0.999 MinorityTheme 0.999 Ed/LangTheme 1.010
House/MobilityTheme 1.013*
SocialVulnerabilityScore .999
Constant 0.524* 1.569 1.201N 588 588 588
LRchi2(12) 9.10 9.47 0.01Prob>chi2 0.0587 0.0916 0.9128
*p<0.05,**p<0.01,***p<0.000
Logisticregressiontechniqueswereusedtomodeltherelationshipbetween
adaptationactions,floodriskexposure,incomeandrace,andsocialvulnerabilitythemes.
Again,the“percentofWhiteresidents”isremovedfromthelogisticmodeltoavoidissues
withmulticollinearity.Table5presentstheresultsofadaptationactionsagainstincome
andracevariables.Oddforacensusblockgrouphavingfloodbuyoutsmultiplyby1.035
and1.036foreachunitincreaseinfloodriskandpercentofLatinxresidents.Oddfora
censusblockgrouphavingsmall-scalegreeninfrastructuremultiplyby1.008and1.025for
eachoneunitincreaseinmedianhouseholdincomeandpercentofAsianresidents.Oddsof
livinginacensusblockgroupthatisprioritizedfortreecanopyexpansionmultiplyby.989,
1.032,and1.026andforeachoneunitincreaseinmedianhouseholdincomeand
percentageofBlack/AfricanAmericanandLatinxsresidents.
Zoll, Adaptation in Austin—22
Table6:LogitResults(OR)forAdaptationSitingandDemographics Model1OR
VariableFloodBuyout
(N=8)GreenInfrastruc.
(N=167)
FutureTreeCanopy(N=83)
Floodrisk(%) 1.035*** 0.999 1.001MedianHHIncome($1,000) 1.008 1.008** 0.989**Black/AfricanAmerican(%) 0.952 1.007 1.032***
Asian(%) 0.696 1.025** 1.021Latinx(%) 1.036* 1.005 1.026***
Constant 0.00216*** 0.167*** 0.126***
N 588 588 588LRchi2(12) 23.71 11.20 72.43Prob>chi2 0.0002 0.0476 0.0000
*p<0.05,**p<0.01,***p<0.000
Table7presentstheresultsoflogisticregressionofadaptationactionsagainstflood
riskexposure,socialvulnerabilitythemes,andtheoverallsocialvulnerabilityindexscore.
Again,resultsindicatethatoddsforacensusblockgrouphavingfloodbuyoutsmultiplyby
1.035foreachunitincreaseinfloodrisk.Oddsforacensusblockgrouphavingsmall-scale
greeninfrastructuremultiplyby.972,1.023,and.977foreveryunitincreasein
socioeconomic,minority,andeducation/languagethemes.Oddsoflivinginacensusblock
groupthatisprioritizedfortreecanopyexpansionmultiplyby1.023,1.016,1.021,and
.981foreachonepointincreaseinsocioeconomic,minority,education/languageand
house/mobilitythemes.
Zoll, Adaptation in Austin—23
Table7:LogitResults(OR)forAdaptationSitingandSVI
Model2OR Model3OR
Variable
FloodBuyout(N=8)
GreenInfrastruc.(N=167)
FutureTree
Canopy(N=83)
FloodBuyout(N=8)
GreenInfrastruc.(N=167)
FutureTree
Canopy(N=83)
Floodrisk(%) 1.035*** 1.000 0.999 1.036*** 0.999 0.999SocioeconomicTheme 0.978 0.972*** 1.023*
HouseholdTheme 1.002 1.004 0.998 MinorityTheme 1.028 1.023*** 1.016* Ed/LangTheme 1.021 0.977** 1.021**
House/MobilityTheme 0.980 1.004 0.981**
SVIScore 1.096** 0.982 1.122***
Constant 0.001*** 0.298*** 0.051*** 0.001*** 0.532*** 0.035***N 588 588 588 588 588 588
LRchi2(12) 17.31 23.00 81.87 14.75 1.86 61.96Prob>chi2 0.0082 0.0008 0.0000 0.0006 0.3945 0
*p<0.05,**p<0.01,***p<0.000
Conclusion
Thispaperattemptstoanswertwoquestions,first:arecensusblockgroupswith
higherpercentagesoflow-income,non-White,andsociallyvulnerablegroupsmorelikely
tobelocatedinfloodplains?Logisticregressiononjustracialandincomedemographic
variablesandsocialvulnerabilitythemesfindsthatmedianhouseholdincomeistheonly
variablewithastatisticallysignificantrelationshiptofloodrisk.Theprobabilityoflivingin
a100-yearfloodplainincreasesasmedianhouseholdincomeincreases.
TheoverallfindingsforfloodriskmaybecomplicatedbythefactthattheCityof
Austinhaswidespreadexposuretofloodrisk,withhalfoftheblockgroupshavingatleast
6%oftheblockgrouplocatedinafloodplain.Thefindingsregardingincomearecontrary
towhatenvironmentaljusticetheorywouldpredict,butmirrorresultsfromastudy
conductedinMiami(Chakraborty&Collins,2014),wheretheauthorshypothesizedthat
higherincomegroupshavinggreaterexposuretofloodriskbecauseofhighdemandfor
proximitytowater-basedamenities.FurtheranalysisforAustinshouldincludeproperty
Zoll, Adaptation in Austin—24
valuesandconsiderusing25-yearfloodplainmaptogetamorerefinedpictureoffloodrisk
andincome.
Mysecondquestionasks:istherearelationshipbetweenmeasuresofincome,race,
socialvulnerability,floodrisk,andtheprobabilitythattheCityofAustinwillsiteaclimate
adaptationinterventioninacensusblockgroup?Ifindthatincome,race,andsocial
vulnerabilitythemesallinfluencetheprobabilityofacensusblockgroupreceivinga
climateadaptationintervention.Interestingly,floodriskisonlystatisticallysignificantfor
thefloodbuyoutadaptationaction.Thisisaconcernifthecityisusingsmall-scalegreen
infrastructureandthefuturetreecanopytoreducefloodrisk.However,itcouldbeanissue
ofhydrologywherethecityistargetingactionsinareasthatcancaptureorslowrunoff
beforeitentersafloodplain.FurtherdiscussionwiththeCityofAustinWatershedteam
mayhelptoclarifythisfinding.
Thereisademographicandvulnerabilitydivisionbetweencensusblockgroups
withhigheroddsofreceivingsmall-scalegreeninfrastructureincomparisontobuyoutsor
futuretreecanopyexpansion.InAustin,theoddsofreceivingsmall-scalegreen
infrastructureincreasewithmedianhouseholdincomesandtheconcentrationofAsian
residents.Alowerprobabilityofreceivingsmall-scalegreeninfrastructureisassociated
withhighermeasuresofsocioeconomicandeducation/languagevulnerability.1These
findingmayprovideadditionalsupporttoconcernsthatadaptationprojectsmaycreate
“ecologicalenclaves”thatprotectmoreprivilegedcommunitieswhileignoringmore
disadvantagedcommunities(Anguelovskietal.,2016;Shietal.,2016).Incontrast,theodds
ofreceivingfloodbuyoutsmultiplywithincreasesinthepercentofLatinxresidents.The
oddsofacensusblockgroupbeingapriorityforfuturetreecanopyexpansionmultiply
withincreasesinthepercentagesofLatinxandBlack/AfricanAmericanresidentsalong
withhighermeasuresofsocioeconomic,minority,andeducation/languagethemes.
Therearefourthingstonoteinthesefindings.Thefirstisthatthefloodbuyouts
programinAustinbeganin1999aspartofajointprogrambetweentheCityofAustinand
theU.S.ArmyCorpsofEngineerstopurchase483propertiesthathadexperienced
1 Theincreasedoddsforsmall-scalegreeninfrastructureastheminoritythemeincreasesislikelyduetothepositiveandsignificantrelationshipbetweenAsianresidentsandgreeninfrastructurementionedaboveandthereforenotdiscussedintheconclusion.
Zoll, Adaptation in Austin—25
repetitiveandsignificantlossesduetoflooding.TheprojectwasexpandedbytheCityof
Austintopurchaseanadditional440properties(CityofAustin,2016a).Thedurationofthe
buyoutsandthecoststothecity,estimatedat$140million,makethebuyoutsripeforsite
specificlongitudinalanalysisinsteadofpoint-in-timeanalysisfor2015.Second,itmaybe
usefultoseparateArmyCorpsofEngineersmandatedbuyoutsfromCityofAustin
voluntarybuyoutstoisolatenon-mandatoryfloodresilienceefforts.Third,thefuturetree
canopyexpansioniscurrentlyonlyaplandevelopedbytheCityofAustin.Itwillbe
importanttotracktheimplementationoftheplantoseeifactionsfollowintention.Finally,
havingreviewedCityofAustindocumentsandGISshapefilesforadaptationactionsitis
evidentthatthefloodbuyoutandtreecanopyplansarerespondingtoananalysisofrisk,
andinthecaseofthetreecanopyadesiretoaddressexistingenvironmentaljusticeissues.
Thereisnodocumentedevidencethatthesmall-scalegreeninfrastructureactionsare
designedtoaddressenvironmentaljusticeissues.MyfindingssuggestthatwhentheCityof
Austinisintentionalaboutaddressingunequalvulnerablyduetoeitherenvironmental
risksorsocialvulnerabilities,adaptationprogramscanshowthepotentialfor
compensatoryjustice.
Thisstudyisaninitialattempttoquantitativelyanswerlargerquestionsabout
environmentaljusticeandclimateadaptation.Futureworkcanaddressanumberof
unansweredquestions.First,furtheranalysisshouldinclude25-yearfloodplainsand
propertyvaluestoanswermorenuancedquestionsabouttherelationshipbetweenflood
riskandincome.Second,longitudinaldatamightbeusefulinansweringquestionsabout
changingvulnerabilitiesespeciallyaroundfloodbuyouts.Third,spatialstatisticsshouldbe
usedinArcGISProtocomplementthelogisticregressionusedhere.Fourth,adaptation
sitinganalysisisuseful,butultimately,weneedtounderstandtheeffectivenessof
adaptationactions,especiallyasmorecitiesundertakeadaptationprograms.Usingmore
advancedmodelingtoolslikeHAZUS,whichcansimulatefloodeventsandpredict
associatedpropertydamagesandmortalities,couldprovidesomeinitialestimatesof
impactsofadaptationpractices.Finally,thisstudyonlyexaminedfloodriskandflood
adaptationactionsinAustin,Texas.Additionalclimaterisksandadaptationmeasures
shouldbeexaminedforthecityandinadditionalcitiesintheUnitedStates.
Zoll, Adaptation in Austin—26
Resultsinthisarticlecontributetothequantitativebodyofresearchthatexamines
environmentaljusticeissuesinregardstoexposuretofloodriskandproximityto
adaptationactions.ItfindsthatinAustin,measuresofraceandthemesofsocial
vulnerabilityarenotstatisticallyrelatedtofloodrisk.Itidentifiesspatialmismatches
betweenfloodriskandnon-floodbuyoutadaptationactions.Importantly,itprovides
evidencearoundthepossibilitiesforadaptationactionstoeitherprotectmoreprivileged
communitiesortotargetcommunitieswithhighermeasuresofsocialvulnerability.It
showsthatwhenadaptationactionsaredesignedtoaddressunequalvulnerablydueto
eitherenvironmentalrisksorsocialvulnerabilities,adaptationprogramscanhave
potentialforcompensatoryjustice.Thispointstowardstwomajorpolicy
recommendations.First,floodadaptioneffortsshouldbelocatedinareaswithgreater
floodrisk.Second,ifthecityisinterestedinpursuingadaptationeffortswithpotentialfor
compensatoryjustice,theycanlooktotheframeworktheydevelopedtoplanthefuture
treecanopyasagoodfoundationtobuildfrom.
Zoll, Adaptation in Austin—27
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