Population Growth and Local Air Pollution: Methods, Models, and Results
Author(s): James C. Cramer
Source: Population and Development Review, Vol. 28, Supplement: Population and
Environment: Methods of Analysis (2002), pp. 22-52
Published by: Population Council
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Population Growth and
Local Air Pollution:
Methods, Models,
and Results
JAMESC. CRAMER
Air pollutionis a seriousthreatto human and environmentalhealth in
virtuallyall ofthelargecitiesoftheworld(Brennan1996; Schneider1998;
WHOIUNEP 1992; WorldResourcesInstitute1996). Thereare manypollutants;the most common include oxides of sulphurand nitrogen(S°x
carbonmonoxide(CO), ozone (03), suspendedparand NOXrespectively),
ticulatematter(SPM), and lead (Pb). A recentsurveyof20 of the world's
largestmegacities(projectedpopulationover 10 millionin the year2000)
guidelineswere exceededbyat least
foundthatWorldHealthOrganization
one of thesepollutantsin everymegacity(WHO/UNEP1992). Highlevels
cardioofpollutionhave been shownto have adverseimpactson respiratory,
on
vascular,and neurologicalsystemsin humans,as well as adverseeffects
and damagetobuildingsurfaces.
crops,and forests
agricultural
vegetation,
Mostresearchon urbanairpollutionhas focusedon thesourcesrather
sourcesincludefuelcomthanthe causes ofpollution.The mostimportant
bustionfordomesticcookingand heating,power generation,motorvehicles, industrialprocesses,and disposal of solid wastes by incineration
(WHOIUNEP 1992). The mostimportantsourcesofpollutionand typesof
pollutantsvaryfromcityto city.Domesticfuelcombustiontendsto be more
importantin citiesin lower-incomecountries,while motorvehiclestend
countries.Researchon the
to be moreimportantin citiesin higher-income
forexample,
sourcesofpollutiontendstobe quitespecificand quantitative;
motorvehicle emissionsaccountfor44 percentof SPM and 73 percentof
NOXin Jakarta(WorldResourcesInstitute1996: 6).
Discussionofthe causes ofairpollution,in contrast,tendsto be quite
generaland vague. It is widelyassumedthatpopulationgrowth,industrialization,and socioeconomicdevelopmentall contributeto air pollution,
are rarelyquantified.These relationships,
but theirrelativecontributions
22
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JAME S
C
.
C
RAMER
23
furthermore,
are complex.Withsocioeconomicdevelopment,forexample,
pollutionfromdomesticfuelcombustionoftendiminisheswhilepollution
frommotorvehiclesincreases.Pollutiontendsto risewithincomebecause
ofincreasedproductionand consumption,
butat highlevelsofincomepollutionmaydeclinebecause ofinvestments
in pollution-reducing
technologies (Dietz and Rosa 1997). In addition,it is widelyacknowledgedthat
governmentpoliciesand programsaimed at reducingair pollutioncan be
quite effective;
thusanotherfundamentalcause ofair pollutionis the absence or insufficiency
ofpollutioncontrolsand regulations.
Populationgrowthand socioeconomicdevelopmentare general,ultimate causes ofairpollution.More proximateand specificcauses linkthese
ultimatecauses to thesourcesofpollution.Proximatedeterminants
include
the natureof the transportation
system(e.g., whetherpublictransportationis available), the age ofthe vehiclefleet,typesoffuelsavailable (e.g.,
dependenceon coal,use ofunleadedgasoline),industrial
composition(some
industriespollute more than others),and urbanspatialstructure(degree
of sprawland congestion).Finally,meteorologyand topographyare importantdeterminants
of the impactof atmosphericemissionson ambient
air quality.Causal modelsthatincorporateultimatecauses,proximatedeterminants,
sources,and emissionsor ambientair qualityare rareindeed.
Even rarerare modelsthatexplicitly
recognizemultiplecausal directions.As I have noted,populationgrowthis widelyregardedas an importantcause ofair pollution,and air pollutionhas been shown to have serious adverse impactson human health. This suggestsa simplemodel of
reciprocalcausalitywithnegativefeedback:populationgrowthcauses air
pollution,and air pollutionreducesthe rateofpopulationgrowth.In this
instancethe feedbackprobablyis ratherweak, since severalstudiessuggestthaturban air pollutionmay accountforabout 2 percentof all local
deaths (WHO/UNEP1992; WorldResourcesInstitute1996). Othertypes
of feedbackmay be more important.In the United States,forexample,
much migrationis guidedby the searchforimprovedqualityof life,not
just a betterjob; urbanairpollutionmayhave a muchlargerimpacton net
migrationthan on mortality(Hunter1998). In addition,increasinglevels
ofpollutionmay stimulateor provokegovernmentpollutioncontrolprograms(at least,one would hope so); thisalso would providenegativefeedback,dampingthe growthofairpollution.
Thischapterhas two aims.The firstaim is to surveythemethodological approachesthathave been used to studytherelationship
betweenpopulationgrowthand local airpollution.The secondaim is to describea statistical modelingapproachand to illustrateit withan empiricalcase study.
The case studyutilizesdata fromCalifornia
and incorporates
multiplecauses
ofpollutionas well as feedbackloops. I concludewitha discussionof the
applicability
oftheproposedapproachto othersettings.
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24
POPULATION GROWTH AND LO CAL AIR POLLUTION
Researchapproachesin thestudyofpopulation
and pollution
Two fundamentally
different
approacheshave been used in researchon
populationgrowthand airpollution:simulationmodelsand statistical
models. In both approachesmost researchhas been at the national or global
level,not local; researchershave consideredonlythe impactofpopulation
growthon pollutionand have ignoredthe possibility
of reciprocalcausalityand feedback.Simulationis the more commonapproach,so I will review thesemodelsfirst.
Simulationmodels
The simplestsimulationmodelsuse populationprojectionsand data on per
capita emissionsof pollutants.Prospectiveprojectionscomparethe emissions thatwould occur withno populationgrowthto the emissionsthat
are expectedto occur withpopulationgrowth(Birdsall1992; Bongaarts
1992; Heilig 1994; Meyerson1998). Resultsoftenare expressedin terms
of the percentageof futureemissionsattributable
to populationgrowth.
Such simulationssometimesuse constantper capita emissionsand sometimesincorporateprojectionsof the trendin per capita emissions.Ofter
thesesimulationsare disaggregated
intoat leasttwo populationcategories,
forexample,the moredevelopedand the less developedcountries.An especiallysophisticated
prospectiveprojection(O'Neill,MacKellartand Lutz
2001) examiIlesgreenhousegas emissionsin termsofdetailedprojections
ofpopulationand ofper capitaemissions.The model is disaggregated
and
takesintoaccountindirecteffects
ofpopulationgrowth,wherebyreduced
ratesofpopulationgrowthstimulatemorerapidincreasesin affluence,
partiallyoffsetting
thebeneficialdirecteffects
ofslowedpopulationgrowth.
Essentiallyidenticalmethodshave been used withhistoricaldata and
retrospective
projections(e.g., Harrison1993; Moomaw and Tullis 1999).
Usuallythese efforts
have been based on the slightlymore sophisticated
IPAT model,in whichthe trendin emissions(I for"impact")is a function
of populationgrowth(P), the trendin consumptionper capita (A for"affluence"),and thetrendin emissionsperunitofconsumption(T for"technology"). Again,resultsusuallyare expressedin termsof the percentage
of emissionsgrowththat is attributable
to populationgrowth.The IPAT
model has been widely criticized(Demeny 1991; Dietz and Rosa 1994;
O'Neill,MacKellar,and Lutz 2001), both forbeinga tautologicalidentity
and forits assumptionthatthe components(population,affluence,and
technology)are independent.These criticisms
apply equally to the prospectiveprojectionsbased on populationand emissionsper capita.
More sophisticated
projectionmodelsdisaggregate
thepopulationinto
a numberofcategoriesand utilizecomplexmethodsforprojecting
percapita
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JAME S
C
.
C
RAMER
25
emissions.
Ridker(1972), forexample,used a varietyofage groupsand
householdcomposition
categories
in thepopulationprojection.
fIe used
econometric
modelsto estimatehouseholdconsumption
functions
fora
variety
ofgoods,an elaborate
input-output
modeltoconvert
end-useconsumption
intonecessary
resource
inputs,
andadditional
econometric
models
to predicttheemissions
producedin providing
theseinputs.Ridkerthen
comparedtheconsumption
streamsfortwodifferent
populationprojections,representing
slowand moderate
population
growth,
and estimated
theemissionsofcriteria
pollutants
(e.g.,CO, SPM,NOX)impliedbyeach
consumption
stream.Comparison
oftherelative
ratesofemissions
to the
relativeratesofpopulation
growth
yieldedestimates
ofthepollutionelasticity
withrespectto population:
forexample,0.46 forSPM,0.26 forCO,
and 0.29 forNOX.
The mostelaborateprojection
modelsutilizecomputersimulation
modelsbasedon numeroussimultaneous
equationsdepicting
theinterrelationsamongmanypertinent
factors.
Thesemodelsare widelyused in
urbanplanningto predictthe emissionsimpliedby specificprojectsor
changesin landuse; untilrecently
theyhavenotbeenusedoftenbydemographers
tostudytheimpacts
ofpopulation
growth.
De la Barra(1989)
presentsthe theoretical
underpinnings
ofintegrated
land use and transportation
models,andWegener(1994) compares
theleadingmodelscurrently
in use.Thesemodelssimulate
spatialdistribution,
landuse changes,
and patterns
oftransportation
thataccompany
populationgrowth.They
can be used to predictemissions
fromtransportation,
industry,
and residentialactivities.
Thesemodels,however,aremostusefulin studying
the
impactsofspecific
projectsor plans,suchas construction
ofa new highwayorimposition
ofnewzoningordinances.
In usingthemodelstosimulatedifferent
scenarios
ofpopulation
growth,
numerous
assumptions
about
transportation
infrastructure
arerequired,
and results
maybe sensitive
to
theassumptions.
Statistical
models
Thesecondapproachtoresearch
onpopulation
andpollution
involvesstatisticalanalysisofdataon pollutionand thefactors
thatcausepollution.
Thisrequiresidentifying
all relevant
variables
andthefunctional
forms
by
whichtheyare linkedto pollution.
Verysophisticated
modelshave been
developedto linkpollution
toitssourcesandto climateandmeteorology,
butmodelslinking
pollution
toitsanthropogenic
causesarerelatively
primitive.Mostsuchmodelseitherexaminea singlecausalfactor
ormakehighly
restrictive
assumptions
aboutfunctional
form.
Oneofthebeststudiesisby
Dietzand Rosa (1997), who transform
theIPATmodelintoa stochastic
statistical
model.Theyutilizedatafrom111 countries
in 1989 to regress
CO2emissions
onpopulation
andaffluence
(percapitagrossdomestic
prod-
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26
POPULATION GROWTH AND LOCAL AIR POLLUTION
uct).Theyfindthatbothpopulation
andaffluence
havenon-proportional
(i.e.,nonlinear)
effects
on CO2emissions.
A seriousdeficiency
intheirspecification
oftheIPATmodelis theirlackofdataon technology.
A moreambitious
modelhasbeendevelopedrecently
and appliedto
local-leveldata on pollutionin California(Cramer1998; Cramerand
Cheney2000). Thismodelalso is a stochastic
transformation
oftheIPAT
model,butitincludesmeasuresoftechnology
as wellas populationand
affluence;
ithas been usedto examinea variety
oftypesofpollutants.
It
warrants
detaileddescription.
Airpollution
isa serious
problem
inCalifornia,
andstringent
airquality
standards
have beenimposedbystateand federalauthorities.
An elaborateregulatory
system
has beendevelopedto implement
mitigation
measuresand to monitor
progress
towardattainment
oftheair qualitystandards.Because of theseregulations,
air qualityin California
actuallyis
improving
despiterapidpopulation
and economicgrowth.
Theregulatory
system
generates
twotypesofdataonairpollution:
actualambient
airqualityis measuredathundreds
ofmonitoring
stations
throughout
thestateto
testforattainment
oftheairqualitystandards;
and emissionsofspecific
pollutants
fromspecific
sourcesare estimated
in eachcountyofthestate,
toaid in thedevelopment
ofnewregulations
and to monitor
progress
towardattainment
ofthestandards.
Bothtypesofdata-observedambient
airqualityandestimated
emissions wereexaminedin connection
withpopulation
growth,
usingsimilarmodels.In bothcases,airqualitywas measuredas a trend,defined
as
theratioofpollution
levelsintwosuccessive
timeperiods.Population
also
wasmeasuredas a trend(i.e.,ratio)overthesametimeperiod.Trends
in
airqualityandpopulation
werecompared
acrossdifferent
geographic
units,
usually
countiesbutalsocitiesandplaces.Trendsin regulatory
effortl
and
trends
in percapitaincomealso wereincludedin themodel.The former
wasusedas an indicator
oftechnologyr
sincemosttechnological
changes
thatimproveairqualityin California
havebeenmandated
bygovernment
regulations;
thelatterwas usedas an indicator
oftrendsin overallconsumption
andproduction
percapita.Otherpossiblecausalfactorsr
suchas
cultural
beliefsandsocialinstitutions,
wereassumednotto varyacrossareaswithinCalifornia.
Researchon populationgrowthand observedtrendsin ambientair
quality
is complicated
bythepervasive
windcurrents
in California.
Pollution
measuredata specific
sitemayhavebeenproduced
bythepopulation
atthatsite,2oritmayhaveblownin fromupwind.Carbonmonoxide
is a
primary
pollutant
andisreadily
measured
as soonas itisproduced,
so wind
currents
are largelyirrelevant
in themodelofatmospheric
CO. The research
showeda strong
relationship
betweenlocalpopulation
growth
and
trends
in atmospheric
CO at a site.Ozone,in contrast,
is theproductofa
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JAMES C. CRAMER
27
chemicalreaction
involving
twoprecursor
primary
pollutants
(reactive
organicgases3and oxidesofnitrogen)
and sunlight.
Thischemicalreaction
maytakeplacelongaftertheprimary
pollutants
wereemitted,
so levelsof
ozoneat a siteare strongly
affected
bywindcurrents.
Theresearchfailed
to demonstrate
anyrelationship
betweenpopulation
growth
andtrendsin
ozone,despiteefforts
to identify
majorwindcurrents
and to incorporate
trends
in upwindpopulation
andupwindregulatory
effort
intothemodels
(Cramerand Cheney2000).
Researchon population
growth
and trendsin emissions
is,in many
ways,morestraightforward.
Windcurrents
areirrelevant,
so upwindcausal
factors
neednotbe identified.
Minorerrors
inestimating
emissions
should
be similarin successive
yearsand canceloutin thecalculation
oftrends.
Major,systematic
errorsin estimating
emissions
shouldbe duplicated
in
estimates
ofregulatory
effort,
sincethemethodsforestimating
emissions
and regulatory
effort
are similar;
hencetheseerrorsshouldbe controlled
byincluding
regulatory
effort
inthemodel.
Researchon population
growth
andtrends
in emissions
in California
countiesfrom1980to 1990did,infact,demonstrate
largeeffects
ofpopulationonpollution,
controlling
fortrends
inregulatory
effort
andpercapita
income(Cramer1998).Thecomplexity
thatarosehereis thatpopulation
growth
had a greater
effect
on somesourcesofemissions
thanon others.
Emissions
wereaggregated
into13broadsourcecategories
in orderto obtainreliableestimates
ofemissions
forcounties.Forsourcecategories
associatedmainlywithconsumption
orlifestyle
activities,
theeffects
ofpopulationgrowth
werelarge(elasticities
near1.0,withpercapitaincomeand
regulatory
effort
controlled);
examplesofthesesourcecategories
areresidentialand commercial
sources,solventuse,passenger
vehicles,and offroadvehicles.Forsourcecategories
associated
mainlywithproduction
for
a nationalor globalmarket,
theeffects
oflocalpopulationgrowthwere
small;examplesofthesesourcecategories
are industrial
and agricultural
processesand off-road
mobileequipment.
The overallimpactofpopulationgrowth
dependedon therelative
balanceoftypesofsourcecategories.
Thestatistical
analysesofairpollution
andpopulation
growth
inCalifornia
aredeficient
in severalrespects.
Windcurrents
introduce
enormous
complexity
intomodelsofambientairquality,
so at thisearlystageofdemographic
research
itispremature
toutilizethesedata;atpresent,
research
shouldfocuson emissions.
In themodelsreviewed
above,emissions
were
aggregated
into13 broadcategories
ofsources,butlittlejustification
was
provided
fortheseparticular
groupings.
Morespecific
sourcesofemissions
shouldbe examinedin detailbeforesourcesare combinedin categories.
Finally,
andmostimportantly,
thepossibility
offeedback
effects
andreciprocalcausalitywas noted(Cramer1998) butcouldnotbe examinedbecauseoflimitations
ofthedata.Theempirical
casestudybelow,alsousing
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28
POPULATION GROWTH AND LOCAL AIR POLLUTION
data fromCalifornia,
is intendedto replicatethe earliermodelsand rectify
these deficiencies.
Populationandpollutionin California
The aim of thisempiricalcase studyis to describethe statisticalapproach
to researchon populationgrowthand airpollutionin detail,illustrating
its
implementation,
strengths,
and weaknesseswithrealdata. I investigatethe
relationship
betweenpopulationgrowthand pollution,takingintoaccount
multiplecauses ofpollutionand possibilities
offeedbackor reciprocalcausality.I look specifically
at populationgrowthand atmosphericemissions
in Californiafortheperiod1975-95. Availabledata do notpermitthe constructionof a completecausal model. Insteadrthe researchreportedhere
shouldbe considereda modestfirststepin thisdirection.I use longitudinal
data and laggedvariablesto representalternativecausal directionsin separatemodels.In one modelI estimatetheeffect
ofpopulationgrowthon air
quality,withstatistical
controlsforotherplausiblecausal factors.In a second set ofmodelsI explorethe effects
ofair qualityon populationgrowth
and theothercausalfactors.
The resultsconfirm
theneed forcomplexcausal
modelsand providea starting
pointforfurther
work.
The setting
Californiais a highlyurbanizedand industrialized
statewitha large capitalistagricultural
sector.The stateis ecologicallydiverse,witha longcoastline, large inland desert,rich farmland,extensiveforests,and imposing
mountains.In partbecause of itsenvironmental
diversity
and beautyand
the abundanceofresources,California's
populationhas grownrapidly.The
populationquadrupledin size from1940 to 1990, fromunder7 millionto
over 30 millionpersons;itis projectedto doubleagain in the next 50 years
(CaliforniaDepartmentof Finance 1998b). Much of thisgrowthis due to
migration,both fromotherstatesand fromothercountries,but natural
increasealso contributessignificantly
(CaliforniaDepartmentof Finance
1998a). The economyhas grownrapidlyas well, leading to a veryhigh
average standardof living.Real per capita income,however,has grown
littlesincethemid-1970s.
Populationand economicgrowthhave put severestresseson the environment,
and thisin turnhas spawneda veryactiveenvironmental
movement. Amongthe more seriousenvironmental
problemsin the stateare
waterscarcityand pollution;diminishedbiodiversity;
loss ofwetlands;soil
and forestdegradation;toxic,radioactiver
and solidwaste disposal;and urban and suburbansprawl(Palmer1993). But the mostnotoriousand possiblymostseriousenvironmental
problemis airpollution.Smogin Los An-
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JAMES C. CRAMER
29
gelesis legendary,
butotherareassuffer
as well.Ofthe20 mostpolluted
metropolitan
areasin theUnitedStatesin 1995,eightwerein California
(UnitedStatesEnvironmental
Protection
Agency1996:TablesA-18,A-19).4
Despiterapidpopulationand economicgrowth,
air qualityactually
has improved
in mostareasofCalifornia
sincethelate 1970s.Thisis because the vigorousenvironmental
movement
has succeededin enacting
legislation
creating
an elaborateregulatory
structure.
Thereis a statewide
AirResourcesBoardand 14 regional(i.e.,airbasin)airqualitymanagementdistricts;
manycountiesalso have air qualitymanagement
offices.
Responsibility
forcontrolling
and reducing
airpollutionis sharedamong
thedifferent
regulatory
layers.In general,
pointsourcesare regulated
by
counties,
areasourcesbyregional
districts,
andmobilesourcesbythestate
agency.5Mostemissions
arecausedbymobilesources,especially
automobiles,and enormousreductions
in emissions
havebeenachievedbystatewide legislation
mandating
cleanercarsand cleanerfuels.Regionaland
countyregulations
increasingly
are attacking
othersourcesofemissions.
Regulations
generally
aredirected
atspecific
pollutants
from
specific
sources.
Successin reducing
airpollution
is due as muchtostringent
airqualitystandards
as to theelaborateregulatory
structure.
Arnbient
airquality
standards
aresetbyboththeCalifornia
andfederal
governments,
although
California's
own standards
oftenare morerestrictive.
The statestandard
forozone,forexample,
limits
themaximum
concentration
ofozoneto0.09
partspermillionforanyone-houraverage;noncompliance
is measuredas
the numberof daysin whichthepeak one-hourconcentration
exceeds
thisstandard.
Standards
alsoexistforcarbonmonoxide,
oxidesofsulphur,
and severalotherpollutants.
Attainment
ofthesestandards
is determined
by measurements
takenat hundredsof monitoring
stationsscattered
throughout
thestate.
Ifan areaisnotincompliance
withtheambient
airquality
standards,
it
(i.e.,a countyoran airbasin)mustdevelopa planforattaining
compliance
andformonitoring
progress
toward
attainment.
Theseplansarebasedonestimatesofemissions
ofspecific
pollutants
fromspecific
sources.
Emissions
are
estimated
bylocal,regional,
orstateregulatory
agenciesdepending
on the
typeofsource;forexample,emissions
from
mobileandmanyareasources
areestimated
bythestate,andemissions
fromsomeareasourcesandfrom
pointsourcesare estimated
bytheregionsandcounties.In general,plans
and regulations
are directed
at themostimportant
sourcesof emissions
first,
althoughconcepts
ofefficiency
alsoplaya rolein decisionmaking.6
Theimprovement
in airqualitysince1975is impressive
butuneven.
Improvement
is seenbothforactualconcentrations
ofozone,CO,andother
pollutants
(California
AirResources
Board1995b;UnitedStatesEnvironmentalProtection
Agency1996) and forestimated
emissionsofreactive
organicgases (ROG),CO, and SOX(butnotNOXor SPM) (California
Air
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POPULATION GROWTH AND LOCAL AIR POLLUTION
30
ResourcesBoard1993).Muchoftheimprovement
is due to dramatic
reductionsin emissions
fromautomobiles
and frompoint-source
industrial
processes.Even amongother(i.e.,area) sourcesof emissions,
however,
improvements
havebeenmadein ROGand SOxemissions.
Forthesetwo
pollutants,
statewideemissionsfromarea sourceswereaboutone-third
lowerin 1995thanin 1975.7
Trendsinemissions
vary,however.
Fortheotherthreepollutants
consideredhere CO, NOx,and PM10 (smallparticulate
matter)--emissions
fromarea sourcesincreased
byabouthalffrom1975to 1995.Trendsalso
varybetweencounties.WhileROGemissions
(fromthesourcesexamined
here)declinedbya thirdstatewide
from1975 to 1995,theydeclinedby
overhalfirla number
ofcounties,
increased
somewhat
inmanyothercounties,andmorethandoubledintwocounties.Similar
variation
occurswith
otherpollutants.
The challengeconfronting
us is to explainthesetrends.PopulatIon
growthoftenis citedas thecauseofenvironmental
degradation,
butthis
explanation
is problematic.
Population
growth,
ofcourse,cannotexplain
whysomeemissions
increased
whileothersdeclined.
Furthermore,
itturns
outthatdeclinesin emissions
oftenoccurred
inthelarger,
moreurbanized
countieswherepopulationgrowthwas greatest,
whilelargerelativeincreasesin emissionsoccurred
in small,ruralcounties.In othercontexts,
observations
of similardiscrepancies
betweentrendsin populationand
trendsinenvironmental
degradation
haveledtotheconclusion
eitherthat
populationis irrelevant
or thattheeffects
ofpopulationare conditional
(i.e.,thattheeffect
ofpopulation
growthdependsupontheinstitutional
and socialstructure,
levelofdevelopment,
typeofeconomy,
culturalvalues,andthelike).Suchconclusions,
however,
maybe premature.
Statistical
models
Theapproachtakenhereis thatenvironmental
trendsand theseemingly
anomalouseffects
ofpopulation
canonlybe understood
clearly
withinthe
contextofa properly
specified
causalmodel.Onekeycharacteristic
ofsuch
a modelis thatitis multivariate.
Othercausalfactors
mayaccountfordivergent
trendsin degradation;
aftertheseotherfactors
are takenintoaccount,theeffects
ofpopulation
mayappearconsistent
and systematic.
A
secondcharacteristic
ofsucha modelis thatall variablesshouldbe measuredas relative
trends.
Anabsoluteincreaseinpopulation
mayhavevery
different
absoluteimpactson theenvironment
in different
economicand
institutional
settings
(e.g.,at low andhighstandards
ofliving),whereasa
specific
percentage
increasein population
mayhavethesamerelative impactin quitediversesettings.8
Thesetwomethodological
ploys specificationofrelativetrendswithina multivariate
model-are essentialforcor-
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JAMES C. CRAMER
31
rectestimation
oftheeffects
ofpopulation
growth
on environmental
degradation.
Fullunderstanding
oftheseeffects
requires
thatpopulation
growth
and environmental
degradation
be examinedwithina completecausal
modelthatallowsforindirect
effects
andfeedback
effects.
The researchproceedsin twosteps:analysisoftheeffects
ofcausal
factors
on trendsinemissions,
andanalysisoffeedback
effects
ofemissions
on thecausalfactors.
Forthefirst
partoftheresearch,
I usethesamemodel
as in pastresearch(described
aboveand in Cramer1998).The standard
IPATmodel(I=PAT)suggests
thatemissions
areproduced
bythreefactors:
population,
affluence,
andtechnology.
I modify
thismodelin fourways:I
useunconventional
definitions
ofthethreecausalfactors
(particularly
technology);I measureeach causalfactor
as a trendfromone timeperiodto
therlext;I takethelogarithm
ofeachsideoftheequation,makingitadditiverather
thanmultiplicative;
andI adda residual
term,
making
themodel
stochastic.
The residualtermincorporates
bothrandommeasurement
errorsin theestimates
ofemissions
andtheeffects
ofnumerous
unobserved
variablessuch as local values,cultures,institutions,
and technological
changesnotmandated
bytheregulatory
system.
Themodel,then,isa type
ofproduction
function:
ln(I') = bo+ blln(P') + b2ln(A')+ b3ln(R')+ e
wherethetrendin countyemissions
(I') is regressed
on countytrendsin
population(P'), percapitaincome(A'), and regulatory
effort
(R', representing
mandatedtechnology).
Becauseallvariables
aremeasured
in logarithmic
transformation,
thecoefficients
represent
elasticities.
Themeasurement
oftrendin regulatory
effort
is crucial.Airquality
has improved
becauseofregulations,
so withoutpropercontrols
forthis
factor
thedetrimental
effects
ofpopulation
growth
cannotbe detected.
Becauseregulations
arecostly,
theyareimposedonlyaftercareful
analysisof
expectedbenefits.
Estimates
ofemissions
reductions
due toanyregulation
are based on laboratory
and fieldexperiments,
simulations,
and experienceselsewhere.Forplanning
purposes,
theseestimates
are usedto produce projections
of futureemissionsrelativeto a baselinelevel,all else
remaining
constant.
Estimates
ofemission
reductions
arecalledcontrol
factors.Forexample,a controlfactorof0.8 forCO emissions
in 1995indicatesthattheexpectedcumulative
effect
ofall CO regulations
is toreduce
CO emissions
by20 percent
fromthebaselineyear(e.g.,1975)to 1995.A
control
factor
of1.0indicates
thatno regulations
havebeenimposed.9
Controlfactors
are computed
bystateandregionaltechnical
staff
foreachcriterionpollutant
separately
bysource(forover100specific
sources)foreach
countyand eachyear.In theresearch
on population
growth
andtrends
in
airquality,
control
factors
areusedtoconstruct
measures
oftrends
inregulatoryeffort
fromonetimeperiodtothenext.
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32
POPULATION GROWTH AND LOCAL AIR POLLUTION
I estimatethemodified
IPATmodelrepeatedly
forvariouscriterion
pollutants
and sourcesofemissions.
Data forall variables
areavaIlablefor
theyears1975,1980,1985,1987,1988,1990,and 1995 (thoughforonly
eightcountiesin 1988).Trendsareestimated
fromoneyeartothenext,so
intervals
rangefromone to fiveyearsin duration.
With56 countiesand
sixtimeintervals,
thedatacomprise
a cross-section
oftimeseries.Because
therearemanymorecountiesthantimeperiodsandtheentirepopulation
ofrelevant
countiesis included,
thecorrect
statistical
modelforthesedata
isa fixed-effects
panelmodel(Baltagi1995;Greene1993;Judgeetal. l 985).
In effect,
thisis an ordinary
leastsquaresregression
witha dummyvariableforeachcounty.A logtransform
is usedforall trendvariables,
so that
regression
coefficients
areinterpreted
as elasticities.l°
The analysisoftrendsin emissions
servesto replicate
pastresearch
on theeffects
ofpopulation
growth
on airquality.Perhapsthemainnoveltyofthecurrent
research,
however,
is theeffort
totranscend
sucha narrowconceptualization
ofpopulation-environment
dynamics.
In the secondhalfoftheresearch,
eachofthecausalfactorstrendsin population,
regulatory
effort,
and percapitaincome is itselfspecified
as an outcome
variable.Theanalysisis restricted
tothesamesetoffourvariables;
no new
variablesareintroduced,
as wouldbe required
in a full-scale
causalanalysisofanyoftheoutcomes(e.g.,population
growth
surelyis determined
by
otherfactors
besidespollution,
environmental
regulations,
and percapita
income!) .
Whileitishighly
likelythatthetrendinemissions
is causedbytrends
in thecausalfactors,
thereverseis unlikely.
Short-term
trendsin population,regulations,
and percapitaincomemostlikelyare causedbyinitial
levels
oftheothercausalfactors,
nottheirtrends.
Forexample,population
growth
overa one-tofive-year
periodprobably
is influenced
bytheinitial
levelofpollution(ifatall),notbythetrendinpollution.
Bydistinguishing
levelsfromtrends,
theanalysesofemissions
and itscausalfactors
can be
disaggregated
intoseparate
models;nonrecursive
modelsforreciprocal
causalityarenotneeded.Usinglongitudinal
datato represent
a dynamic
processbyrecursive
equationsis fairly
common(Cramer1980).The specific
modelsused in theanalysesofthecausalfactors
are described
in a later
section.Each modelincludesinitiallevelof emissionsas a predictor,
so
separateregressions
are estimated
fordistinct
combinations
ofpollutants
and sourcesofemissions.
Fised-effects
modelsareusedhereas well.
Dataqualityaxldsamplerestrictions
Observations
havebeenincludedin thesampleonlyiftheyweredeemed
reliable
andrelevant.
Forexample,datafrom
twoofCalifornia's
58 counties
havebeen excludedbecausethesetwocounties(Alpineand Sierra)have
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JAME S
C
.
C
RAMER
33
verysmallpopulationssparsely
distributed
acrossvastmountainous
terrain.Trivial
sourcesofemissions
alsohavebeenexcluded;thesearesources
thatproduce,on average,lessthan100tonsofemissions
ofa specific
pollutantpercountyperyear.Emissions
fromnaturalcausesand fromunplannedwildfires
havebeenexcludedon theassumption
thattheyareunrelated
tolocalpopulation
growth.
Emissions
from
severaladditional
sources
(e.g.,utility
equipment,
solvents
usedindrycleaning)havebeenexcluded
becausetheseemissions
wereestimated
fromdataon countypopulation;
onlyemissions
estimates
madewithout
reference
to thecausalfactors
are
usedhere(California
AirResources
Board1995a).
Estimates
ofcontrolfactors
(i.e.,regulatory
effort)
are notavailable
forcertainsourcesofemissions,
so thesesourcesalso havebeenexcluded
fromtheanalysis.Mostindustrial
sourcesofemissions
are excluded,becausecontrolfactors
arenotavailableforspecific
pointsources.Emissions
fromautomobiles
andtrucks
alsohavebeenexcluded,
becausecontrol
factorsarecalculatedforspecific
modelsofvehiclesand arenotavailablefor
county-wide
fleets.Theseexclusions
are notas seriousas theymayfirst
appear.Mostindustrial
sources,
whiletheoretically
interesting,
wouldhave
been excludedanyway,becausemostindustries
in California
todayemit
trivialamountsofpollution(lessthan100 tonsper countyperyearon
average,according
to theofficial
emissions
inventory).
Automobiles
and
trucksare veryimportant
sourcesof emissions,
butthe earlierresearch
alreadyshowedclearlythattheseemissions
are strongly
associatedwith
localpopulation
growth;
replication
isnotessential
here.
A smallnumberofobservations
havebeenomitted
fromthesample
becauseofapparenterrorsofmeasurement
thatcausedundueinfluence
orextreme
outliers
in dataanalysis.l2
Separateregressions
wereestimated
foreach pollutant
and each sourceincludedin theanalysis.Each regressionwas carefully
analyzedforinfluential
casesor outliers.
Forexample,
fromthe regression
of emissions
ofreactiveorganicgasesfromsolvents
usedin commercial
and industrial
degreasing
operations,
theplotofstandardizedresidualsagainstpredicted
valuesis shownin Figure1. Twoobservations,
from
county
24 (Merced)in 1988 and 1990,appearas extremely
largeresiduals(onepositive,
onenegative).
Thesametwoextreme
residuals are seenin thepartialregression
(i.e.,addedvariable)plotoftrendin
emissionson trendin population,
shownin Figure2. Theseextremeresidualsare causedbyan abnormally
smallvolumeofemissions
recorded
in 1988; emissions
ofROGfrom
degreasing
inMercedCountyweregreater
than70 tonsperyearin 1985 and 1987 andgreater
than60 tonsperyear
in 1990 and 1995,butwererecorded
as only21 tonsin 1988. Thisappears
tobe an errorin thedatafile;emissions
of71 tonsin 1988 wouldbe consistent
withthegeneraltrendin MercedCounty.Theverylow figure
for
1988 createsan extremely
steeptrenddownward
in emissions
from1987
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-
1
5t0
POPULATION GROWTH AND LO CAL AIR POLLUTION
34
to 1988,and an extremely
steeptrendupwardin emissions
from1988to
1990(hencethelargenegative
andpositive
residuals)
. Ratherthanassume
thatthecorrect
figurefor1988is 71 tons,thetwoextremeobserarations
(trends1987-88and 1988-90)havebeenexcludedfromanalysis.A total
of126suchoutlying
orinfluential
observations
wereexcluded.
Another,
relatedproblemcan be detectedin Figures1 and 2: 1980
datapoints(i.e.,trendsfrom1975to 1980) tendto clusterin theupperrightquadrantoftheresiduals-by-predicted
plotandthepartialregression
plot.These data pointshave highpredicted
valuesbecausepopulation
growthwas particularly
rapidduringthe 1975-80period.The systematicallylargepositiveresidualsin 1980,however,are troubling.
Theselarge
positiveresidualsseemtobe associated
witha changein methodsusedto
estimate
emissions.
Thechangewasimplementedbetween
1975and 1979
(California
AirResources
Board1996),andthenewmethodseemstoproducelargerestimates
ofemissions
thantheoldmethod.Withsourcesfor
whichestimates
ofemissions
wereproducedbythestateagency,thebias
FIGURE 1 Plot of residuals by predictedvalues, froma regressionof
trendin ROG emissionsfromdegreasirlgsolventson trendsin
population, regulatoryeffort,and per capita irlcome:Fixed-effects
panel model forCaliforniacounties,1975-95
.508925-
2.
-
3@S
SM5
S
-.520602- z
-.57571
l
,
,
_
Fittedvalues
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All use subject to JSTOR Terms and Conditions
l
.859348
31940
330
F0
JAMES C. CRAMER
e
3S
FIGURE 2 Partial regressionplot of trendin ROG emissionsfromdegreasing
solventson trendin population, controllingfortrendsin regulatoryeffort
and per capita income: Fixed-effectspanel model forCaliforniacounties,
1975-95
coef- .79525114,se = .08010512,t = 9.93
_
.514271
-
-
24-
290
30
x
-
o
_,
S@S
117
_
3
3rt
-.590739
-
2488
.244795
-.131557
e( logpop I x )
due to thechangein methodsshouldbe similaracrosscounties,
and the
biascanbe controlled
bya dummy
variablefor198013(notincludedinthe
regression
shownin Figures1 and2). Withsourcesforwhichestimates
of
emissions
wereproduced
byregional
orcounty
agencies,
theimpactofthe
centrally
mandatedchangemightvaryacrosscounties;in thesecases,the
1980 data (i.e., the distorted
1975-80trend)have been excludedfrom
analysis.
The specific
regressions
ofemissions
trendsthatare estimated
here
areshownin Table1,alongwiththenumberofobservations
availablefor
eachregression
afterall thevariousexclusions.
Fivecriteria
pollutants
are
examined:carbonmonoxide(CO),reactive
organic
gases(ROG),oxidesof
nitrogen
andofsulphur
(NOX
andSOx),andsmallparticulate
matter
(PM10).
Twenty-nine
regressions
areestimated:
7 sourcesofCO, 3 sourcesofNOX,
12 sourcesofROG,2 sourcesofSOx,and 5 sourcesofPM10.In themajorityofregressions
thereare over200 observations
(up to 56 countiesand
up to six timeintervals
each),whilein tworegressions
thereare fewer
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36
POPULATION GROWTH AND LOCAL AIR POLLUTION
TABLE 1 Data source and numberof observationsused to estimate
fixed-effects
regressionmodels ofemissions,by typeand source of
emission
Data
sourcea
Emissions source
Fuel combustion
Agricultural
Residential
Waste burning
AgIiculturaldebris
Forestmanagement
Range management
Solvent use
Asphaltpaving
Degreasing
Printing
Misc. procedures
Petroleummarketing
Construction,demolition
Farmingoperations
Pesticideapplication
Waste disposal
Othermobile
Mobile equipment
Off-roadvehicles
Number of observations
CO
N°x
ROG
S°x
PM10
D
A
83
288b
288
288
287C
288
D
D
D
195b
137b
174bC
180C
l83b c
1 1 6b
1 l 3b
D
A
D
230C
286C
118C
A
A
D
A
D
286C
A
A
288
2 l8b c
64c
284C
125C
288
286C
230C
286C
268C
286C
221C
aEmissionsdata proYidedby air managementdistnctsare indicatedby D; emissionsestimatedby the California
AirResources Board are indicatedby A.
bBecause of lack of regulations,controlfactorsforthistypeand source of emissiondo not vary (all or virtually
all are equal to unity),so controlfactoris not includedin the emissionsmodel.
COneor more outliershave been omitted.
than 100 observations
(becausecertainsourcesare notpresentin every
county,
notall countieshavedataforall sixtimeintervals,
andthesample
restrictions
affect
somecountiesmorethanothers).Table1 also indicates
thepollutants
and sourcesforwhichtherewereinfluential
cases or extremeoutliers(i.e.,casesexcludedbecauseofdataerrors),
andthesources
forwhich1975-80trends
wereexcluded(datasourceD).
Modelsoftrends
inemissions
Thepurposeofthisanalysisis to replicate
pastresearch,
in whichpopulationgrowth
had a largeimpacton emissions
fromsourcesassociatedwith
consumption
and lifestyle,
but onlya smallimpacton emissionsfrom
sourcesassociatedwithproduction
fora globaleconomy.Severalofthe
specificsourcesof emissionsexaminedhereclearlybelongto the "consumption
andlifestyle"
category;
theseincluderesidential
fuelcombustion,
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JAMESC. CRAMER
37
andoff-roadveasphaltpaving,
(i.e.,gasstations),
petroleummarketing
a largeregresfind
to
expect
we
Withthesesourcesofemissions,
hicles.
belongtothe
clearly
Othersources
growth.
forpopulation
coefficient
sion
small
relatively
forwhichwe expecttofinda
category,
production"
"global
fuel
theseincludeagricultural
growth;
forpopulation
coefficient
regression
associatedwith
wasteburning
debris,
ofagricultural
burning
combustion,
and offoperations,
farming
miscellaneous
andrangemanagement,
forest
mobileequipment.
road
listedin Table 1 do not
sourcesofemissions
Severalofthespecific
or,more
belongto one ortheotherofthesetwobroadcategories;
clearly
thattake
activities
include
These
theybelongtobothcategories.
accurately,
sector,
sectorand thelargerindustrial
placein boththelocalcommercial
a priori:solventuse in printing
withneithersectorclearlypredominant
wastedisand unspecified
and demolition,
construction
anddegreasing,
extenused
are
pesticides
usealsoisincludedherebecause
posal.Pesticide
agrias wellas in large-scale
settings
and commercial
in residential
sively
the
culture.For thesesources,we cannotpredictin advancewhether
most
small;
or
large
be
will
forpopulationgrowth
coefficient
regression
intermediate.
be
will
it
likely,
and sourceshownin Table1, thetrendin emisForeach pollutant
percapitaincome,and regulaon trendsin population,
sionsis regressed
coefficients
Theregression
in logtransform).
(withall variables
toryeffort
2. These
Table
in
fromtheseequationsareshown
fortrendin population
fromtheregresForexampletthecoefficient
are elasticities.
coefficients
0.611 indicates
of
combustion
fuel
fromresidential
sionofCO emissions
abouta 6 perwith
is associated
thata 10 percentincreasein population
centmcreasem emlsslons.
bythereareweaklysupported
hypotheses
Nearlyall ofthespecific
(largerthan0.6)
largecoefficients
relatively
sultsin Table2. As expected,
vehicles,
off-road
fuelcombustion,
residential
from
arefoundforemissions
0.4) are
than
(smaller
smallcoefficients
andasphaltpaving;andrelatively
mobileequipment,
debrisburning,
fuelcombustion,
foundforagricultural
manfromforest
and
ROG)
not
but
(withPM10
operations
and farming
coefficient
small
the
are
tothehypotheses
Themajorexceptions
agement.
found
largecoefficient
andthesomewhat
marketing
foundforpetroleum
are
out of20 coefficients
one or twoexceptions
forrangemanagement;
withinthenormalrangeoferrorandrequireno specialexplanation.
are conwiththehypotheses,
consistent
whilelargely
Theseresults,
largeas
as
not
are
sideredweak supportbecausethe"large"coefficients
In past
arenotas smallas expected.
and the"small"coefficients
expected,
and
for"consumption
coefficients
withbroadersourcecategories,
research
coeffiwhile
unity,
near
often
and
than0.8
sourcesweregreater
lifestyle"
lessthan0.2 andoftennearzero.I have
were
sources
production
for
cients
results.
moremoderate
forthecurrent,
no explanation
.
.
.
.
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POPULATION GROWTH AND LOCAL AIR POLLUTION
38
TABLE 2 Partial regression coefficients for population trend in
fixed-effects panel models of trends in emissions, by type and source
OI emlSSlOIl
>
*
.
Emissions source
Fuel combustion
Agricultural
Residential
Waste burning
Agricultllraldebris
Forestmanagement
Range management
Solvent use
Asphaltpaving
Degreasing
Printing
Misc. procedllres
Petroleummarketing
Construction,demolition
Farmlngoperations
Pesticideapplication
Waste disposal
Othermobile
Mobile equipment
Off-roadvehicles
Type of
modela
Regression coefficients
ROG
N°x
CO
sOx
PM10
D
A
0.306
0.611*
0.573*
0.610*
D
D
D
0.316*
0.087
0.466*
0.592*
0.616*
D
A
D
0.601*
0.456*
1.080*
A
A
D
0.248*
0.663*
0.334*
0.756*
-0.449*
0.678*
A
D
A
A
0.342*
0.065
0.389t
0.178
0.377* -0.008
0.720* 0.676*
0.343*
0.762*
0.400*
1980 data; model D simplyexcludes 1980 data. Both models
aModel A includes a dummyvanable identifying
include controlsfortrendsin per capita income and controlfactor(when the latteris not constant-see Table 1) .
*p < .05.
Ap < . 10.
With the sources forwhich hypothesesare ambiguous,resultsare
neitherlargenorsmall;
is intermediate,
fordegreasing
mixed.The coefficient
and wastedisand demolition,
construction
forprinting,
but the coefficients
are associatedprimarily
the latteractivities
posal clearlyare large.Evidently
fornationalorglobalmarkets.
production
notvvith
withconsumption,
Whilepopulationgrowthis ourmainconcernhere,otherresultsfrom
these equations should be mentionedbriefly.For those pollutantsand
variesand could
effort
exist,so thatregulatory
sourcesforwhichregulations
in mostcases
effort
regulatory
for
coefficient
the
model,
the
in
be included
effort"
"regulatory
the
if
is near unity.Thisis the resultthatshould occur
to the data and
so it adds credibility
variable was properlyconstructed,
forper capitaincomeis small
approach.In mostequations,the coefficient
is
the coefficient
in those cases whereit is significant,
and not significant;
the
in
China
example
for
countries,
some
In
positive.
as
negative
as often
maybe a majorfactorin environmen1980s, rapidlyincreasingprosperity
tal degradation.In the Californiacontextofveryhighstandardsofliving,a
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39
JAMESC. CRAMER
consciousness,
andhighenvironmental
movement,
environmental
strong
friendly
environmentally
for
used
be
inincomecanas easily
improvements
forenas
appliances)
home
(e.g.,cleanercars,moreefficient
consumption
unimporThe
1997).
(DietzandRosa
consumption
harmful
vironmentally
theseequationsmaybe due to the
in
income
capita
oftrendin per
tance
confriendly
environmentally
butsometimes
ofincreasing
effects
offsetting
surprising.
butis not
thus,itwasunexpected
sumption;
effects
offeedback
Models
sectionis causally
Asnotedearlier,themodelanalyzedin thepreceding
thedycausalmodeldepicting
A morecomplete
and deficient.
incomplete
hereis displayedin
namiclinkagesamongthefourvariablesconsidered
portionofthismodelshowsthelinkagesexam3. The right-hand
Figure
regulatory
oftrendsin population,
section:effects
inedin thepreceding
now
Whatis ofinterest is the
and percapitaincomeon emissions.
effort,
may
portionofthemodel,whichshowsthatthecausalfactors
left-hand
timelags)andthateachin turn
belinkedto each other(withappropriate
bythelevelofpollution.
maybe affected
FIGURE 3 Dynamic recursivecausal model of emissions
Level
To
Change
To-Tl
p
CFi
E
dCFi
I
Where:
P = population
fromsourcei
foremissions
CFi= controlfactor
fromall sources
E = emissions
fromsourcei
Ei = emissions
I = percapitaincome
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>
dEi
40
POPULATION GROWTH AND LOCAL AIR POLLUTION
Eachofthecausalfactors
istheoutcomeofa complexprocessinvolvingnumerousdeterminants.
Localpopulation
growth,
forexample,is the
sumofnaturalincreaseandnetmigration;
in theCalifornia
context,
these
dependuponsuchthingsas relative
wagesand thedemandforlabor,the
costofliving,racialand ethniccomposition
ofthepopulation
and theexistenceofimmigrant
communities
to serveas network
magnets,
and such
qualityoflifefactors
as crimerate,qualityofschools,recreation
opportunities,and climate(Cadwallader1992; Hunter1998). Introduction
and
implementation
ofenvironmental
regulations
and changesin countyper
capitaincomeare at leastequailycomplexphenomena.I have madeno
effort
to specify
comprehensive
modelsofthecausalfactors.
Instead,I restrict
myattention
to thefourvariablesin theemissions
modeland empirically
explorerelationships
amongthem,as shownin Figure3. Using
thesevariables,
I construct
and estimate
a plausiblemodelofeach causal
factor
inturn.Asa further
simplification
I examinethemodelsofthecausal
factors
independently,
undertheimplausible
assumption
thattheresidualsin thethreemodelsareuncorrelated.
Theresults
thatfollow,
therefore,
shouldbe considered
exploratory,
incomplete,
and suggestive
only.
Population
growth
The modelin Figure3 indicatesthattheshort-term
trendin population
maybe affected
byall threeofthevariablesconsidered
here:regulatory
effort,
emissions,
andpercapitaincome.Regulations
in placein a county
at thebeginning
ofa timeinterval
couldaffect
population
growthduring
thatinterval
inseveralways,bothofwhichwouldseemtoimpedegrowth:
regulations
couldincreasethecostofliving;andregulations
couldsymbolize an alienating
bureaucratic
political
system
thatstifles
individual
behavior (possiblyrelevantin the California
"frontier"
context).l4
Thelevelof
emissions
canaffect
subsequent
population
growth
intwoways:byreducingnaturalincrease(presumably
mainlybyraisingmortality),
or by discouraging
in-migration
or encouraging
out-migration
(Hunter1998).The
healtheffects
ofairpollution
areveryrealbutdemographically
relatively
small
(Anderson
etal. 1996;Morgan
etal. 1998;Schwartz
1993),somigration
probablyis theimportant
factor
here.Airpollution
mainlyaffects
in-migration,
notout-migration
(Cadwallader
1992).Finally,
theiIiitiallevelofpercapita
incomecouldaffect
subsequent
population
growth
in severalways,withdiverseconsequences:
forexample,
wealthmayserveas a magnet,
attracting
migrants;
high-income
areastendto havehighhousingprices,
discouraging
in-migration;
andhigher-income
areasaremorelikely
tosupport
growth-controlmeasures.
Becauseofthesediverse
consequences,
one cannotpredict
a
priori
thedirection
ofeffect
oflevelofpercapitaincomeonpopulation
growth.
Estimating
theeffects
ofinitiallevelsofregulatory
effort,
emissions,
andpercapitaincomeon subsequent
population
growth
is complicated
by
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JAMES C. CRAMER
41
theproblemthatregulatory
effort
and emissions
aremeasuredseparately
foreach of the 29 combinations
ofpollutants
and sourcesof emissions
shownin Table1,whilepercapitaincomeandpopulation
growth
arethe
sameforall 29 categories.
Estimating
29 separateequationsforpopulation
growth
is redundant
andinefficient,
butaggregating
emissions
andregulatoryeffort
intosinglemeasuresis difficult.
Themeasureofemissions
that
is empirically
feasible
yetrelevant
hereis emissions
ofa particular
pollutant fromall sources;l5thisyieldsfivemeasuresand requiresa separate
regression
equationforeach.
An analogousmeasureofregulatory
effort
(i.e.,overallregulation
of
a particular
pollutant,
regardless
ofsource)wouldbe relevant
butis not
feasible.Initiallevelofregulatory
effort
is defined
as thecumulative
controlfactor
fora specific
pollutant
and sourcefrom1975to theinitialyear
in question;thisis a decimalnumberlessthanorequaltounity,
andthese
numbers
cannotmeaningfully
be summed(as is donewithemissions),
averaged,multiplied,
orotherwise
combined
withthedataavailable.Thesolutionadoptedhereis to pool all observations
fora particular
pollutant
intoone sample,andto estimate
a separateregression
foreachofthefive
pooledsamples.Within
eachpooledsample,mostcountiesina givenyear
appearin the samplemultiple
times(onceforeach sourceofemissions,
exceptincasesofmissing
ordeleteddata).Thevaluesofpopulation
growth
and initiallevelsofemissions
fromall sourcesand percapitaincomeare
identical
foreachofthesemultiple
appearances
ofa countyina givenyear,
whilethevalue ofregulatory
effort
differs
and is specific
to theparticular
sourceofemissions.l6
In thisspecification
thecoefficient
for"control
factor"represents
theaverageimpactofregulations
ofparticular
sources,not
theimpactofoverallregulatory
effort.
Forunregulated
sources,
all control
factors
areunity;a dummy
variableis usedtoidentify
thesesources.
Estimates
ofcoefficients
fromthefiveregressions
oftrendinpopulationare presented
in Table3. Thecoefficient
forinitiallevelofemissions
fromall sourcesis consistently
negativeand significant
in thefiveregressions,althoughin severalcasesitis relatively
smallin magnitude.
Neverthelessitis clearthatpopulation
growth
is slowerin thosecountieswith
higherlevelsofairpollution.
Thisis strong
evidenceforan equilibrating
feedbackeffect:
populationgrowthcontributes
to air pollution,
and this
pollution
in turnretards
further
growth.
Thecoefficient
forpercapitaincomealsois consistently
negative
and
significant
inthefiveequations,
indicating
thatpopulation
growth
isslower
in higher-income
counties.Mostpopulation
growthin California
during
thistimeperiodwas due eitherto immigration
or to thehighfertility
of
earlierimmigrants.
Apparently
immigrants
arenotdrawntohigh-income
counties,perhapsbecauseofthe costofhousingor lack of demandfor
theirlaborskills.Finally,
thecoefficient
forinitialregulatory
effort
generallyis smalland notsignificant.
Thismaybe an artifact
ofinappropriate
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42
POPULATION GROWTH AND LOCAL AIR POLLIJTION
TABLE3 Regressions
oftrendin populationon initiallevelsof
emissionsfromall sources,source-specific
controlfactor,
and per
capitaincomea:Fixed-effects
panelmodels,bytypeofpollutant
Regression
Predictors,
initial levels
Eniissions,all sources
Ifinitialyearis 1975
Controlfactor
If source is not regulated
Per capita income
Constant
2 within
Pooled N
coefficients
CO
N°x
ROG
sOx
PM10
-0.203**
-0.003
0.301*
-0.001
-0.078**
2.748**
0.377**
1451
-0.020*
0.051**
0.028
-0.042**
0.061**
0.002
_0.003
-0.185**
2.170**
0.329**
2537
-0.035**
0.048**
-0.003
-0.184
-0.007
-0.267
b
-0.164**
1.774**
0.352**
804
b
-0.224**
2.377**
0.385**
508
0.001
-0.032
1.885
0.345
1090
aAllvanables are in log transforrn,
so coeffiQentsrepresentelasticities.
bDummyvanable omitted;all sourceswere regulatedat least somewhat.
**p < .0 1
*p< .05
measurement,
oritmayindicatethatairpollution
regulations
haveno effecton population
growth.
Regulatory
effort
Regulatory
effort
is measuredforspecific
pollutants
and sourcesofemissions,so itis examinedinseparateregressions
foreachofthe29 combinationsofpollutants
and sourcesshowninTable1. Thecausalmodelin Figure 3 showsregulatory
effort
responding
to fourfactors:
initiallevel of
population,
percapitaincome,emissions
fromall sources,and emissions
fromthe specific
sourcebeingregulated.
The importance
ofemissionsis
self-evident:
regulations
wouldnotbe imposed
unlesspollution
werea problem.Demonstrating
thislinkageis important,
however,becausethereis
alwaysthepossibility
thatregulations
arenotimposedevenwhenpollutionis a problem.Therealso is thequestionofwhetherregulations
are
imposedin responseto theoveralllevelofpollution,
regardless
ofwhich
sourcescontribute
mostto theproblem;or whetherspecific
sourcesare
regulated
whenever
theyproduceemissions,
withoutregardto theaggregateproblem
oflevelofemissions
from
allsourcescombined.
In eachequationofregulatory
effort
duringa timeinterval,
whereregulatory
effort
is
fora particular
pollutant
andsource,twomeasuresofinitiallevelofemissionsare specified:
emissions
fromthatparticular
source,and emissions
fromall other
sourcescombined
(netofthatparticular
source).
Population
sizecouldaffect
regulatory
effort
in severalways.In countieswithlargepopulations,
problems
ofinterdependence
and externalities
maybe moreapparent
andpublictolerance
ofregulations
maybe greater.
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JAME S
C
.
C
RAME R
43
Countieswithlargepopulations
mayhavelargerandbetter-trained
planningdepartments
thatcancorrectly
assessairquality
andtheneedforregulations.Finally,publicsupportforenvironmental
protection
is strongin
California,
butso is opposition
bywell-organized
interest
groups;largelocal populations
maybe moredifficult
to mobilizein favorofregulation.
The overallimpactofpopulationsizeis difficult
to predict.
The effects
of
percapitaincomeare moreclearcut,
at leastjudgingfromthebroadhistorical
andcross-national
record:
higher-income
peoplearemoreconcerned
withqualityoflifeissues(likecleanair),andarebetter
abletoescapeonerousaspectsofregulations.
Thus,I expectthatregulatory
effort
is greater
in
higher-income
counties.
Empirical
problems
againmustbe confronted.
Themajority
ofpollutantsand sourcesareunregulated;
with19 ofthe29 combinations
ofpollutantand source,thereis toolittlevariation
in regulatory
effort
between
countiesforreliableanalysis.In theremaining
tencases,initiallevelsof
populationand emissions
fromall sourcesareveryhighlycorrelated
(r >
0.85),so coefficients
estimated
forthesevariables
areunreliable.
Othercorrelationsamongthepredictors
are modestin size,so collinearity
should
notaffect
othercoefficients.l7
Becauseoftheseempirical
problems,
I willdescribe
theregression
resultsbut not presentthemin detail.In all tenregressions
ofregulatory
effort
duringa timeinterval,
thecoefficients
forinitiallevelofemissions
fromtheparticular
sourceunderconsideration
arelargeinmagnitude,
significant
at the.01 level,andnegative.
Control
factors
aremeasuredas expectedreductions
in emissions,
so a smallercontrolfactor(e.g.,0.6 comparedto0.8) represents
a greater
regulatory
effort.
Thenegative
coefficients,
thus,indicatethatcountieswithmoreemissions
froma particular
source
made greaterregulatory
efforts
to reducethoseemissions.
Thisis highly
reassuring;
planning
andregulation
arenotalwaysso rational.
Coefficients
fornetemissionsfromall othersources,whileunreliable
becauseofcollinearity,
generally
arequitesmallas wellas notsignificant,
and theyare
as likelyto be positiveas negative.
Thissuggests
thatregulations
are imposedin responseto specific
emissions,
notin responseto overalllevelsof
emissions
(i.e.,regulations
target
theactualoffenders).
Coefficients
forinitiallevelofpopulationare large,significant,
and
positivein sixofthetenregressions
ofregulatory
effort;
theyare smallor
negativein theremaining
fourregressions.
Giventheinconsistency
in resultsand theproblemofseverecollinearity,
theseresultsat bestare suggestive;whattheysuggest
isthatregulatory
efforts
generally
areweakerin
countieswithlargerpopulations.
Coefficients
forinitiallevelofpercapitaincomegenerally
are small,
notsignificant,
and inconsistent
irldirection.
Percapitaincomedoes not
seemtobe an important
determinant
ofcountyregulatory
effort.
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44
POPULATION GROWTH AND LOCAL AIR POLLUTION
Trendirlpercapitaincome
Thecausalmodelin Figure3 showsthetrendirlcountypercapitaincome
responding
tothreefactors:
irlitial
levelsofpopulation,
emissions
fromall
sources,and regulatory
effort.
Thefirst
twoofthesefactors
are included
largelyforcompleteness
and symmetry,
rlotbecauseofstrong
hypotheses
ortheory.
In agricultural
orforest
settings,
airpollutioncan reduceyields
and diminish
incomes.Thismaybe a problemin certainlocalsettings
in
California,
butitis unlikely
to be an important
consideratiorl
in statewide
comparisons
ofcounties.
In anycase,initiallevelsofpopulation
andemissionsfromall sourcesare highlycorrelated,
precluding
reliableempirical
estimates
ofrelationships.
Thereis,however,
considerable
irlterest
inregulatory
effort.
Thegeneralissueiswhether
environmental
regulation
obstructs
ordiminishes
economicprosperity
(Repetto
etal. 1996);herethespecific
questioniswhether
greater
initialregulatory
effort
isassociated
withslowergrowth
inpercapita
income.As was thecase withpopulation
growth,
thetrendin percapita
incomeis thesameirla countyregardless
ofsourceofemissions,
butregulatoryeffort
is measuredwithrespectto each sourceofemissions.
As before,I pooledobservatiorls
intofivesamples,orleforeachpollutant.
In all
fiveregressions
oftrendinpercapitaincome,thecoefficient
forregulatory
effort
is smallandnotsignificant.
Clearlythedeterminants
oftrendin per
capitaincomearecomplexandmanyrelevant
factors
arenotspecified
in
theseequationst
so theseresultsare at bestsuggestive.
Nevertheless,
the
resultsarenotconsistent
witha hypothesis
thatregulatory
effort
seriously
diminishes
prosperity.
Summary
Thiscase studyofpopulationgrowthand airpollutionhas proceededin
twosteps.Thefirst
stepwas an analysisofthedirecteffects
ofthreecausal
factorspopulation,
percapitaincome,
andregulatory
effort-on
emissions.
In thesecondstepI exploredhowthethreecausalfactors
areinterrelated
andhow each,inturn,isaffected
byemissions.
Thepurposeofthissecond
stepwas to identify
possibleindirect
causaleffects
on emissions
and feedbackeffects
fromemissions.
Thetwostepscarlbe conducted
separately
by
takingadvantageoftimelagsinlongitudinal
data.
Two typesoffeedback
aresignificant.
One is seemingly
obvious:initiallevelsofemissions
influence
subsequent
regulatory
effort.
Thisshows
thatCalifornia's
regulatory
structure
works.Stateandfederal
lawsrequire
thatifambientairqualitystandards
areviolated,
mitigation
plansmustbe
developedand implemented.
Theresultspresented
hereshowthatareas
withhighlevelsofemissions
do indeedenactregulations
to reducethose
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JAMES C. CRAMER
45
emissions;
theregulations
aredirected
specifically
at theoffending
sources
ofemissions.
Thesecondtypeoffeedback
islessobviousandmoreintriguing:highinitiallevelsofemissions
detersubsequent
populationgrowth.
Moredetailedresearch
is neededtodetermine
whether
pollution
impedes
in-migration
orencourages
out-migration
(Hunter1998),andwhether
pollutionmatters
becauseofmarket
prices(e.g.,forhousing)orperceptions
of qualityoflife.In any case,bothtypesoffeedback
are negative:high
initial
levelsofemissions
produceresponses
thatdampensubsequent
growth
in pollution.Becauseofthedifficulty
ofcomparing
levelsand trendsof
variables,
itis notclearhowstrong
thedampening
feedback
is; statistically
and theoretically,
itis significant.
Two typesofindirect
effect
on emissions
also are significant.
Pirst,
size ofpopulationis associatedwithregulatory
effort.
Otherthingsbeing
equal (suchas initiallevelofpollution),
areaswithlargerpopulations
are
lesslikely
toimplement
additional
regulations.
Indirectly,
then,a largepopulationis associated
witha greater
increasein emissions
(orsmallerdecline
in emissions);
thisindirect
effect
is consistent
withandamplifies
thedirect
effect
ofpopulationgrowthon emissions.
Secondly,
percapitaincomeis
associatedwithpopulationgrowth.Areaswithhighlevelsofper capita
incomeexperience
slowerpopulation
growth;
as a consequence,
indirectly
theseareasexperience
lesspollution.
Additional
research
is neededtoilluminatethemechanisms
andmotivations
involved
here.
Severalnon-significant
(non)findings
arenoteworthy.
In thedataexaminedhere,initialregulatory
effort
appearstobe unrelated
bothto subsequentpopulationgrowthand to thetrendin percapitaincome(other
thingsbeingequal). Othertypesofgovernment
regulation
maybe offensivelyintrusive
andburdensome,
butefforts
tomaintain
cleanairseemto
be inconsequential
in thesetworespects.
Theconverseis notnecessarily
true.Whileinitialsizeofpopulation
is associated
withsubsequent
regulatoryeffort,
as described
above,initiallevelofpercapitaincomeisnotassociatedwithsubsequent
regulatory
effort.
Surprisingly,
whileaffluent
areas
prevent
oravoidpopulation
growth,
theyneither
promote
noropposeclean
airregulations
(otherthings
beingequal).
Theindirect
andfeedback
effects
described
abovecomefrom
veryincompletely
specified
causalmodelsin whichmanyrelevant
causalfactors
are omitted.
Theymust,therefore,
be considered
suggestive
hypotheses,
notconclusive
results.
Applicability
oftheapproachelsewhere
The statistical
approachto studying
populationgrowthand airpollution
producesusefulandinsightful
results
in California.
Theobviousquestion,
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46
POPULATION GROWTH AND LOCAL AIR POLLUTION
then,is whethertheapproachcan be appliedelsewhere.The answer,of
course,hingesupon theinstitutional
contextand dataavailability
"elsewhere."In different
institutional
settings
thespecific
modeldescribed
here
mayrequireminormodifications
ortalternatively,
thesamemodelmaybe
usedbutitwillproducedifferent
results
requiring
different
interpretations.
The issueofinterpretation
arisesbecauseoftheproblemofunitsof
analysis.In theanalysisofemissions
(thefirst
stageofthemodel),thedifferential
effects
ofpopulation
growth
occurbecauseofboundaries.
Populationandemissions
aremeasured
withinspecific
political
jurisdictions,
for
examplecountiesin California.
Localpopulation
haslargeeffects
on emissionsfromsourcesthatareassociated
withactivities
thattakeplacewithin
theselocal boundaries,
and has smalleffects
orlemissionsfromsources
thatareheavilyinvolved
intransportation
ortradeacrosstheboundaries.l8
Different
resultsmightbe obtainedfordifferent
jurisdictions
or setsof
boundaries
andin areaswithdifferent
patterns
ofinternally
versusexternallygenerated
activities.
Thisis a standard
problemin statistical
geography;itnecessitates
careininterpreting
results,
nota different
model.
Animportant
boundary
problem
thatwasoverlooked
inthecasestudy
presented
here (becauseoflackofdata)irlvolves
emissions
frommobile
sourcessuchas automobiles,
trucks,
buses,ships,andairplanes.
Emissions
fromsuchsourceswillbe measuredforspecific
jurisdictions
or sites,but
thepeopleusingthesemobilesourcesmaybe transients,
notresidents
of
thatjurisdictiorl
orsite.Thisproblem
willbe moreacutethesmaller
thejurisdiction,
solarger
arealunitsshouldbe used.Itmaybenecessary,
nevertheless,
tomodify
thebasicmodeltotakeaccount
oftransient
populations.
Regulatory
effort
is specified
as one ofthethreecausalfactors
in the
modelforCalifornia
becausetheenvironmental
regulatory
system
ishighly
developedand mostchangesin technology
thathave important
impacts
on emissions
aremandated
bythissystem.
In othersettings
theregulatory
systemmaybe less effective
or absententirely,
and modifications
ofthe
modelmaybe warranted.
Forexample,regulatory
effort
maybe omitted
as a causalfactor,
ora measureofmarket-driverl
technological
changemight
be addedto themodel.Suchchangeswouldrequiremodification
ofboth
themodelofemissions
andthemodelsoffeedback.
The modelofemissions
is estimated
separately
fordifferent
sources
ofemissions.
Thespecific
sourcesusedforanalysis
undoubtedly
wouldvary
acrosssettings,
andtheresults
obtained
maydependon thespecific
sources
used.Relatively
detailedsourceswereusedhere,comparedto thebroad
categories
used earlier(Cramer1998); even here,however,the sources
are aggregated,
and thisaggregation
can poseproblems.
In California
the
mostimportant
instanceofaggregation
is thesourcecategory
ofautomobiles.Automobile
emissions
varydramatically
depending
on themodeland
age ofthecar,so changesin fleetcomposition
can havelargeimpactson
thetrendinautomobile
emissions.
Somechangesinfleetcomposition
may
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C CRAMER
JAMES
47
in the
beassociatedwithpercapitaincomeandthuswouldbe controlled
emissions);
automobile
of
analysis
(haddatabeenavailabletopermit
model
withpopumaybe correlated
however,
infleetcomposition,
changes
other
of
examples
Other
maybe confounded.
and theireffects
lationgrowth,
which
fuelcombustion,
includeresidential
sourcecategories
aggregated
heatwater
heating,
space
cooking,
enduses(e.g.,
different
together
lumps
use
solvent
and
fuels(wood,naturalgas,electricity);
ing)and different
commerand
ofindustrial
thecomposition
whichreflects
degreasing,
from
ofsources
ofaggregation
levels
different
settings,
In other
cialactivities.
in source
changes
of
problem
the
maybe desirablein orderto minimize
changes
(e.g.,
sources
onpolicy-relevant
andtofocusattention
composition
fuelsormodesoftransportation).
inresidential
areproducedbymanysources,althoughauemissions
In California
in lowerespecially
In somesettings,
are themostimportant.
tomobiles
or two
one
only
by
maybe produced
mostemissions
incomecountries,
agriculor
offorests
burning
fuelcombustion,
sources,suchas domestic
casesresuch
In
trucks.
and
automobiles
or
turalwastes,a localindustry,
be
should
model
the
and
sources
searchshouldfocusonjusttheimportant
source.
ofeachspecific
adaptedtothecharacteristics
hereis
pollutiondescribed
air
and
growth
population
The modelof
feasibleonlybecausetimeseriesdataon fourkeyvariablesare available
Ifa longtimeseriesofdatais availfora largesampleoflocaljurisdictions.
thesameconceptualmodel
able but foronlyone or a fewjurisdictions,
time
(e.g.,an autoregressive
estimator
can be used butwitha different
1993;
Greene
see
model;
panel
seriesmodel insteadof a fixed-effects
but
Kmenta1986).Ifdataareavailablefora largesampleofjurisdictions
model
foronlyone or twotimeperiods,thena simultaneous-equations
thiswillrequire
willbe necessary;
(endogeneity)
causality
withreciprocal
dataon populacase,
any
In
variablesforproperidentification.
additional
are readily
probably
tionand somemeasureofincomeor consumption
and controlfactors
ofemissions
estimates
available,butin manysettings
maynotbe available.Ifmonieffort)
regulatory
(thebasisformeasuring
areusedin placeofestiquality
dataon actualambientair
toring-station
thenseveralchangesin themodelmaybe necessary.
matedemissions,
Measuresof ambientair quality,of course,combinepollutionfromall
sourcescouldnotbe estimated
sources,so separatemodelsfordifferent
wouldbe lost.Seasonaldataon
ofthemodelofemissions
andtherichness
seasonswhenwindcurrents
ambientairqualityshouldbe used,selecting
pollutshouldbe focusedon primary
attention
are minimal.In addition,
suchas CO andsuspended
emission,
after
immediately
antsthataredetected
accrueovertimesuch
that
pollutants
ratherthansecondary
particulates,
addedto themodel
be
could
upwindpopulation
as ozone.Alternatively,
a modelis not
such
with
althoughexperience
as anothercausal factor,
(CramerandCheney2000).
promising
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48
POPULATION GROWTH AND LOCAL AIR POLLUTION
Futuredirections
Asidefromreplicating
thisresearch
in settings
otherthanCalifornia,
what
needstobe done?Certainly
onenecessary
stepis toimprove
themodelby
including
additional
causalfactors.
Irlthemodelofemissions,
ifsourcecategoriescannotbe disaggregated
satisfactorily
becauseofdatalimitations,
thenmeasuresofcomposition
shouldbe addedtothemodel.Forexample,
Cramer(1998) combinedall industrial
sourcesintoone sourcecategory
becausetheemissions
fromanyone industry
are so smalland unreliably
estimated;
in thiscase itwouldhavebeendesirable
to includea measure
ofindustrial
cornposition
in themodelto controlfortheeffects
ofchangingcomposition
(e.g.,declineinpetrochemicalst
growth
in electronics
and
textiles)
on thetrendinemissions.
Another
desirable
addition
tothemodel
wouldbe a measureofcultureorlifestyle.
Atpresent
theonlyindicator
of
whatpeopledo (as distinct
from
howmanypeoplearedoingit)ispercapita
income.Incomesurelyis important,
butitis nottheonly determinant
of
automobile
use,sizeofhouse(a keyfactor
inresidential
fuelcombustion),
disposalofwaste,or demandforpollution-intensive
commodities
(Stern,
Young,andDruckman
1992).
The modelsoffeedback
presented
hereare evenmoredeficient,
as
explicitly
notedabove. Local populationgrowthdependsupon relative
wages,demandforlabor,costofliving,
proximity
to sourcesofmigration
and immigration,
racialandethniccomposition
ofthepopulation,
quality
of schools,local amenities(entertainment
and recreation
facilities,
local
university,
andthelike),andclimateas wellas thefactors
specified
hereinitiallevelsofincome,pollution,
andregulatory
effort
(Cadwallader
1992;
Gober1993).Theimposition
andimplementation
ofenvironmental
regulationsdependupon thelocal industrial
structure,
strength
of local and
regional
environmental
organizations,
existence
ofgovernmental
coalitions
andcredibility
oftheregulatory
agency,andtheeducational
leveland environmental
attitudes
of thepublicas well as the factors
specified
here
(Marzotto,
Burnor,
andBonham2000;SabatierandJenkins-Smith
1993).
Theseadditional
variables
shouldbe includedinthemodelsoffeedback
in
ordertogetgoodestimates
oftheeffects
ofthevariables
ofinterest.
Ifmostemissions
areproducedbyonlya fewsources,thenthegeneralmodelshouldbe modified
to suitthespecific
characteristics
ofthose
sources.Heremanysourceswereexamined,
and thesamegeneralmodel
was appliedto eachforthesakeofcomparability;
thisignoredtheunique
features
ofeachsource.Residential
fuelcombustion,
forexample,depends
uponhouseholdsizearldcomposition
as wellas numberofhouseholds;
it
is not a function
merelyoftotalnumberofpersonsin a jurisdiction.
In
California
mostresidential
fuelcombustion
is forspaceheatingand cooling;emissions
fromresidential
fuelcombustion
dependuponthelocalclimateand vegetation
(e.g.,treeshade),daytimeoccupancyofthehome
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JAMES C. CRAMER
49
(whichdependsuponage structure
ofthehouseholdandemployment
and
schoolenrollment),
and thermalcomfort
thresholds
of the inhabitants
(Crameretal. 1984,1985).Clearly,
different
modelswouldbe appropriate
in different
settings.
In manysettings
themajority
ofemissions
areproducedbyautomobiles.Oftenmorethanone-half
oftheemissions
from
automobiles
areproducedduringthefirst
minuteaftera coldstart,
so a modelofautomobile
emissions
wouldhavetoaccountfornumberoftripsas wellas duration
of
eachtrip(Cameron1991).Numberoftripsis relatedto thedensity
ofautomobiles(e.g.,numberofcarsanddrivers
perhousehold),
whichin turn
is relatedtoaffluence.
Numberoftripsalsois relatedtotheactivity
schedulesofhouseholdmembers
andtothespatiallocations
ofresidence,
work,
schools,commercial
shops,and recreation.
Durationoftripsdependson
urbanspatialstructure
andon congestion
(oraveragespeedoftrips).
Models
ofemissions
fromautomobiles
(andothermodesoftransportation),
therefore,mayshiftthefocusawayfromlocalpopulation
sizetowardthespatialdistribution
ofpopulation
andlanduses.Thismaybe a desirable
directionforresearch,
as localjurisdictions
mayhavemorepolicyleverageover
landuse andpopulation
distribution
thanoverpopulation
size.
Notes
This researchwas partiallysupportedby a
grantfromthe Committee
on Research,Universityof California,
Davis.Data werekindly
provided by the CaliforniaAir Resources
Board;theadviceand assistance
ofCARBstaff,
especiallyMartinJohnsonand AndyDelao,
are greatlyappreciated.
RobinCheneyhelped
construct
thedatafilewithgreatcare,dedication,
andhumor.
1 The quantitative
measureofregulatory
effort
is describedbelowin thecase study.
2 Partofthe"population
atthatsite"may
be transient
and notmeasuredwellin official
statistics.
In California,
motorvehiclesare a
major sourceof atmospheric
emissions,and
much motorvehicleusage is forcommuting
and long-distancetravelor hauling. Thus
manydrivers
maynotbe residents
of"thesite"
and not countedas partofthe local population.Thisproblemshouldaffect
themodelof
CO as well as the model of ozone; sincethe
former"works"well, the problemdoes not
seemto be serious.
3 These also are called volatileorganic
compounds(VOC).
4 Five ofthe six mostpollutedareas are
in California:
Riverside-San
Bemardino,Los
Angeles,Bakersfield,
VenturaCounty,and
Fresno.(ThesixthsiteisHouston.)OtherCaliforniasitesinthe"worst20" areOakland,Sacramento,
and San Diego.
5 Point sources of emissionsare large
manufacturingplants. Area sources are
smaller,dispersedstationary
sourcessuch as
drycleaningestablishments,
farms,and residentialhomes.Mobile sourcesincludeautomobiles,trucks,
otherformsoftransportation,
equipment(fromlargetractors
to forklifts
and
lawnmowers),and recreational
vehicles(e.g.,
boats,snowmobiles).
6 That is, the main considerationis to
achievethelargestpossiblereduction
in emissions.Theeffort
orcostrequiredtoachievethis
reduction
is a secondary
consideration,
mainly
because historicalexperienceindicatesthat
costsoreffort
tendto be greatly
exaggerated
a
prioriand because the healthand economic
benefitsof reducingpollutionexceed almost
anyplausiblecost,butareproportional
to the
amountofreducedemissions.
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so
POPULATION GROWTH AND LOCAL AIR POLLUTION
7 This and subsequentstatements
about showedup as largeresiduals)or wereunduly
(i.e.,associatedwithlargevaluesof
trendsin emissions
arebasedon estimates
from influential
mydata setand thusreferonlyto thespecific Cook'sDistance).For guidelineson assessirlg
see BollenandJackman(1990), Cook
sourcesofemissionsincludedin thisresearch/ outliers/
and Weisberg( 1999),and Fox (1991).
mostofwhicharearea sources.
13 In the regressions
of emissionstrend,
8 Forexample,Modoc Countyis a small,
ruralcountywithnearlypristineair quality. the dummyvariablecontrolsforsystematic
Populationgrowthin Modoc Countycauses bias in the 1975-80 trendrelativeto other
In theregressions
oftrendsin
farlesspollution(intonsofemissions
peryear) trendintervals.
effort,
and per capita
than comparable growth in Los Angeles population,regulatory
County,butthepercentaSe increase in pollution income,thedummyvariablecontrolsforsystematicbiasin the 1975 levelofemissions.
maybe thesamein thetwocounties.
14 Consider,forexample,thatin some
9 A controlfactor
of1.0couldindicatethat
regulationsare expectedto be completely
in- countiesin recenttimeperiods (but not in
effective,
butthisis unlikely.
In principle,
con- othercountiesorearliertimeperiods)outdoor
trolfactorscan exceed 1.0. Thiswould occur barbecues,lawn mowers,household appliifa regulation
werewithdrawn,
causingan in- ances, and commutingpatterns(e.g., rideparkingavailability)
have been regucreasein emissions;orifa regulation
designed sharing,
to reduceemissionsofone criterion
pollutant lated.
inadvertently
caused increasedemissionsof
15 This,ofcourse,is a massivesimplificaanotherpollutant.
tionnecessitated
bythedata.Therelevant
conis air pollutionthatis vis10 The coefficient
forpopulationgrowth, ceptformigration
forexample,indicatesthepercentagechange ible, either to the eye or to the nose (or
demonstratedand
in emissionsdue to a one percentincreasein suspectedor statistically
widelypublicized,even ifnotdirectly
detectpopulation(otherthingsbeingequal).
ablebyindividuals).
We havedataonlyforspe11 The29 equationsoftrendsinemissions
cificemissionssuch as CO and ROG (which
(fordifferent
combinations
ofpollutantsand
cannotreadilybe aggregated,
e.g.! totaltons
sourcesofemissions)are notindependent,
so
of 'Xjunk"),
not forpollutantslike ozone. Fia "seemingly
unrelatedequations"(SUE) apnally, our measure of "total" emissions is
proachmightseem warranted(Greene1993;
summedover availablesourcecategories;it
I(menta1986). The samecouldbe saidforthe
does notincludeemissionsfromautomobiles
equationsfortrendsin thecausalfactors.
Acor trucks,which conttibutethe majorityof
cordingto Greene( 1993:488489), however,
emissionsofmostpollutants.
SUE has no advantageiftheexplanatory
vari16 Becauseofthesubstantial
redundancy
ables are identicalin the equations,and little
of
multiple
observations
that
are
identicalin
advantage if the explanatoryvariablesare
thesamplesizesin effect
areinhighlycorrelated.
Thisis thecase here,so SUE mostrespects,
flated;to compensate,a morestringent
level
is notwarranted.
ofsignificance
is utilizedhere.
12 Outliersand influential
cases wereex17 Collineanty
was not a problemin reaminedcarefully
and conservatively,
andwere
gressions
of
trend
in
emissionsor trendin
omittedfromthesampleonlyiftheyappeared
to be theresultoferrorsin thedata.Observa- population.
18 In the lattercase, "external"populationswere not excludedsimplybecausethey
thanlocalpopulation.
were incongruent
withthe model (i.e., they tionis moreimportant
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