Climate Adaptation and Social Vulnerability in Austin, Texas · Climate Adaptation and Social...

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


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


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


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.


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


0=Nosmallscalegreeninfrastructure

1=Censusblockgroupcontainsatleastonesmallscalegreeninfrastructure

project(thisincludesgreeninfrastructurelikebioswalesand

stormwaterpondsmaintainedbytheCityofAustin)


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


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.


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


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.


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


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).


Figure 1 Social vulnerability and flood risk, Austin


Figure 2 Minorities and flood risk, Austin


Figure 3 Income and flood risk, Austin


Figure 4 Adaptation and flood risk, Austin


Figure 5 Adaptation and social vulnerabilities, Austin


Figure 6 Adaptation and minorities, Austin


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


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)].


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


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


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.


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.


Table7:LogitResults(OR)forAdaptationSitingandSVI

Model2OR Model3OR

Variable

FloodBuyout(N=8)


FutureTree

Canopy(N=83)

FloodBuyout(N=8)


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


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.


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.


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.


References

Anguelovski,I.,Shi,L.,Chu,E.,Gallagher,D.,Goh,K.,Lamb,Z.,…Teicher,H.(2016).Equity

ImpactsofUrbanLandUsePlanningforClimateAdaptation:CriticalPerspectivesfrom

theGlobalNorthandSouth.JournalofPlanningEducationandResearch.

http://doi.org/10.1177/0739456X16645166

Araos,M.,Berrang-Ford,L.,Ford,J.D.,Austin,S.E.,Biesbroek,R.,&Lesnikowski,A.(2016).

Climatechangeadaptationplanninginlargecities:Asystematicglobalassessment,4–

11.http://doi.org/10.1016/j.envsci.2016.06.009

Bautista,E.,Hanhardt,E.,Osorio,J.C.,&Dwyer,N.(2014).NewYorkCityEnvironmental

JusticeAllianceWaterfrontJusticeProject.LocalEnvironment,20(6),664–682.

http://doi.org/10.1080/13549839.2014.949644

Berke,P.,Newman,G.,Lee,J.,Combs,T.,Kolosna,C.,&Salvesen,D.(2015).Evaluationof

NetworksofPlansandVulnerabilitytoHazardsandClimateChange:AResilience

Scorecard.JournaloftheAmericanPlanningAssociation,81(4),287–302.

http://doi.org/10.1080/01944363.2015.1093954

Bierbaum,R.,Smith,J.B.,Lee,A.,Blair,M.,Carter,L.,Chapin,F.S.,…Verduzco,L.(2013).A

comprehensivereviewofclimateadaptationintheUnitedStates:Morethanbefore,

butlessthanneeded.MitigationandAdaptationStrategiesforGlobalChange,18(3),

361–406.http://doi.org/10.1007/s11027-012-9423-1

Bowen,W.(2002a).Ananalyticalreviewofenvironmentaljusticeresearch:Whatdowe

reallyknow?EnvironmentalManagement,29(1),3–15.

http://doi.org/10.1007/s00267-001-0037-8

Bowen,W.(2002b).Environmentaljusticethroughresearch-baseddecision-making.

Routledge.Routledge.

Broto,V.C.,&Bulkeley,H.(2013).Asurveyofurbanclimatechangeexperimentsin100

cities.GlobalEnvironmentalChange,23(1),92–102.

http://doi.org/10.1016/j.gloenvcha.2012.07.005

CDC.(2016).SVI2014Documentation.

Chakraborty,J.,&Collins,T.(2014).Socialandspatialinequitiesinexposuretofloodrisk:A

casestudyinMiami,Florida.NaturalHazards…,15(3),1–10.

http://doi.org/10.1061/(ASCE)NH.1527-6996.0000140.


Chakraborty,J.,Collins,T.W.,&Grineski,S.E.(2016).Environmentaljusticeresearch:

Contemporaryissuesandemergingtopics.InternationalJournalofEnvironmental

ResearchandPublicHealth,13(11),11–15.http://doi.org/10.3390/ijerph13111072

CityofAustin.(2014).TowardaClimate-ResilientAustin.

CityofAustin.(2015).WatershedMasterPlan.

CityofAustin.(2016a).FloodMitigationTaskForce :FinalReporttoAustinCityCouncil.

CityofAustin.(2016b).HazardMitigationPlanUpdate(Vol.1).

CityofAustin.(2016c).ImagineAustinComprehensivePlan.

Cutter,S.L.,Boruff,B.J.,&Shirley,W.L.(2003).Socialvulnerabilitytoenvironmental

hazards.SocialScienceQuarterly,84(2),242–261.http://doi.org/10.1111/1540-

6237.8402002

Ekstrom,J.A.,&Moser,S.C.(2013).Sea-LevelRiseImpactsandFloodingRiskintheContext

ofSocialVulnerability:AnassessmentfortheCityofLosAngeles.

English,P.,Richardson,M.,Morello-frosh,R.,Pastor,M.,Sadd,J.,King,G.,&Jerrett,M.

(2013).RacialandIncomeDisparitiesinRelationtoAProposedClimateChange

VulnerabilityScreeningMethodforCalifornia.InternationalJournalofClimateChange:

ImpactsandResponses,4(2).http://doi.org/10.18848/1835-

7156/CGP/v04i02/37156

Ford,J.D.,Berrang-Ford,L.,&Paterson,J.(2011).Asystematicreviewofobservedclimate

changeadaptationindevelopednations.ClimaticChange,106(2),327–336.

http://doi.org/10.1007/s10584-011-0045-5

Graham,L.,Debucquoy,W.,&Anguelovski,I.(2016).Theinfluenceofurbandevelopment

dynamicsoncommunityresiliencepracticeinNewYorkCityafterSuperstormSandy:

ExperiencesfromtheLowerEastSideandtheRockaways.GlobalEnvironmental

Change,40,112–124.http://doi.org/10.1016/j.gloenvcha.2016.07.001

H.JohnHeinzIIICenterforScience,&theE.(2000).Thehiddencostsofcoastalhazards:

Implicationsforriskassessmentandmitigation.IslandPress.

Hayhoe,K.(2014).ClimateChangeProjectionsfortheCityofAustin.

Jesdale,B.M.,Morello-Frosch,R.,&Cushing,L.(2013).Theracial/ethnicdistributionof

heatrisk-relatedlandcoverinrelationtoresidentialsegregation.Environmental

HealthPerspectives,121(7),811–817.http://doi.org/10.1289/ehp.1205919


Lyles,W.,Berke,P.,&HeimanOverstreet,K.(2017).JournalofEnvironmentalPlanningand

ManagementWheretobeginmunicipalclimateadaptationplanning?Evaluatingtwo

localchoicesWheretobeginmunicipalclimateadaptationplanning?Evaluatingtwo

localchoices,568(October).http://doi.org/10.1080/09640568.2017.1379958

Maantay,J.,&Maroko,A.(2009).Mappingurbanrisk:Floodhazards,race,&environmental

justiceinNewYork.AppliedGeography,29(1),111–124.

http://doi.org/10.1016/j.apgeog.2008.08.002

Maldonado,A.,Collins,T.W.,Grineski,S.E.,&Chakraborty,J.(2016).Exposuretoflood

hazardsinMiamiandHouston:Arehispanicimmigrantsatgreaterriskthanother

socialgroups?InternationalJournalofEnvironmentalResearchandPublicHealth,

13(8).http://doi.org/10.3390/ijerph13080775

Muñoz,C.E.,&Tate,E.(2016).Unequalrecovery?Federalresourcedistributionaftera

Midwestflooddisaster.InternationalJournalofEnvironmentalResearchandPublic

Health,13(5).http://doi.org/10.3390/ijerph13050507

Pastor,M.,Sadd,J.,Morello-frosch,R.(2013).ScreeningforJustice,(august),14–17.

Pulido,L.(2015).Geographiesofraceandethnicity1.ProgressinHumanGeography,39(6),

809–817.http://doi.org/10.1177/0309132514563008

Reckien,D.,Creutzig,F.,Fernandez,B.,Lwasa,S.,Tovar-restrepo,M.,&Satterthwaite,D.

(2017).Climatechange,equityandtheSustainableDevelopmentGoals :anurban

perspective.Environment&Urbanization,29(1),159–182.

http://doi.org/10.1177/0956247816677778

Rumbach,A.J.,&Kudva,N.(2011).PuttingPeopleattheCenterofClimateChange

AdaptationPlans:AVulnerabilityApproach.Risk,Hazards&CrisisinPublicPolicy,

2(4),1–23.http://doi.org/10.2202/1944-4079.1096

Sadd,J.,Morello-Frosch,R.,Pastor,M.,Matsuoka,M.,Prichard,M.,&Carter,V.(2013).The

Truth,theWholeTruth,andNothingbuttheGround-Truth:MethodstoAdvance

EnvironmentalJusticeandResearcher-CommunityPartnerships.HealthEducation&

Behavior :TheOfficialPublicationoftheSocietyforPublicHealthEducation,41(3),

281–290.http://doi.org/10.1177/1090198113511816

Schrock,G.,Bassett,E.M.,&Green,J.(2015).PursuingEquityandJusticeinaChanging

Climate:AssessingEquityinLocalClimateandSustainabilityPlansinU.S.Cities.


JournalofPlanningEducationandResearch,35(3),282–295.

http://doi.org/10.1177/0739456X15580022

Schweitzer,L.,&Stephenson,M.(2007).RightAnswers,WrongQuestions:Environmental

JusticeasUrbanResearch.UrbanStudies,44(2),319–337.

http://doi.org/10.1080/00420980601074961

Shi,L.,Chu,E.,Anguelovski,I.,Aylett,A.,Debats,J.,Goh,K.,…VanDeveer,S.D.(2016).

Roadmaptowardsjusticeinurbanclimateadaptationresearch.NatureClimate

Change,6(2),131–137.http://doi.org/10.1038/nclimate2841

Shonkoff,S.B.,Morello-Frosch,R.,Pastor,M.,&Sadd,J.(2011).Theclimategap:

Environmentalhealthandequityimplicationsofclimatechangeandmitigation

policiesinCalifornia-areviewoftheliterature.ClimaticChange,109(SUPPL.1),485–

503.http://doi.org/10.1007/s10584-011-0310-7

Tate,E.,Strong,A.,Kraus,T.,&Xiong,H.(2016).Floodrecoveryandpropertyacquisitionin

CedarRapids,Iowa.NaturalHazards,80(3),2055–2079.

http://doi.org/10.1007/s11069-015-2060-8

Thomas,D.S.,Phillips,B.D.,Lovekamp,W.E.,&Fothergill,A.(2013).Socialvulnerabilityto

disasters.(D.S.Thomas,B.D.Phillips,W.E.Lovekamp,&A.Fothergill,Eds.).CRCPress.

U.S.CensusBureau.(2017).“AmericanCommunitySurvey2010-20155-YearEstimates.”

AmericanFactFinder,U.S.Dept.ofCommerce,Washington,DC,

⟨https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml⟩(August,2017).

vanZandt,S.,Peacock,W.G.,Henry,D.W.,Grover,H.,Highfield,W.E.,&Brody,S.D.(2012).

Mappingsocialvulnerabilitytoenhancehousingandneighborhoodresilience.Housing

PolicyDebate,22(1),29–55.http://doi.org/10.1080/10511482.2011.624528

Vargo,J.,Stone,B.,Habeeb,D.,Liu,P.,&Russell,A.(2016).Thesocialandspatial

distributionoftemperature-relatedhealthimpactsfromurbanheatislandreduction

policies.EnvironmentalScience&Policy,66,366–374.

http://doi.org/10.1016/j.envsci.2016.08.012

Vogel,B.,&Henstra,D.(2015).Studyinglocalclimateadaptation:Aheuristicresearch

frameworkforcomparativepolicyanalysis.GlobalEnvironmentalChange,31,110–

120.http://doi.org/10.1016/j.gloenvcha.2015.01.001

Wheeler,S.M.(2008).StateandMunicipalClimateChangePlans:TheFirstGeneration.


JournaloftheAmericanPlanningAssociation,74(4),481–496.

http://doi.org/10.1080/01944360802377973

Whittemore,A.H.(2017).TheExperienceofRacialandEthnicMinoritieswithZoningin

theUnitedStates.JournalofPlanningLiterature,32(1),16–27.

http://doi.org/10.1177/0885412216683671

Woodruff,S.C.,&Stults,M.(2016).Numerousstrategiesbutlimitedimplementation

guidanceinUSlocaladaptationplans.NatureClimateChange,6(August).

http://doi.org/10.1038/nclimate3012

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