Shi, Zewei
A icle
Modeling he impac o emission c edi sys ems on au omo i e p oduc
po olios: A ma hema ical analysis o policy e ec s in Eu ope, China,
and he U.S. unde di e en demand scena ios
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Johannes Wi e ,P edic ing S ock Re u ns Wi h Machine
Lea ning: Global Ve sus Sec o Models
Robin Roskosch, Bewa e o Bullshi –A Quali a i e S udy on
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Me e Anna Gläse , Go e nmen In e en ions Du ing he
COVID-19 Pandemic, Cul u e, and Co po a e Cos
Beha iou
Zewei Shi, Modeling he Impac o Emission C edi Sys ems on
Au omo i e P oduc Po olios: A Ma hema ical
Analysis o Policy E ec s in Eu ope, China, and he
U.S. Unde Di e en Demand Scena ios
Hagen Alexande Höne loh, Nume ical S udies o he
Scheduling o Con inuous Annealing Lines
Lea Wedel, KPIs o Sus ainabili y: De ining he S a egy o a
Sus ainable Fu u e in he Insu ance Indus y
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Published by Junio Managemen Science e.V.
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ISSN: 2942-1861
Modeling he Impac o Emission C edi Sys ems on Au omo i e P oduc Po olios:
A Ma hema ical Analysis o Policy E ec s in Eu ope, China, and he U.S.
Unde Di e en Demand Scena ios
Zewei Shi
Technical Uni e si y o Munich
Abs ac
In he mids o he global clima e c isis, go e nmen s wo ldwide ha e implemen ed a ange o emission policies aimed a en-
cou aging mo e p oduc ion o he en i onmen ally iendly ehicle. Howe e , he exac impac o hese policies on au omake s’
p oduc ion po olios and p o i abili y emains unce ain and challenging o an icipa e. This pape p esen s a comp ehensi e
analysis o h ee majo emission egula ion policies enac ed by he Eu opean Union (EU), China, and he Uni ed S a es (U.S.),
e alua ing hei in luence on ca manu ac u e s. Le e aging a ma hema ical model, his pape adop he pe spec i e o in-
di idual manu ac u e s seeking o maximize e enue, del ing in o he in icacies o hese policies. Fu he mo e, his a icle
conduc sensi i i y and ac o ial analyses o assess he impac o policy pa ame e s. The indings e eal ha all h ee majo
emission policies con ibu e o an inc ease in he p oduc ion o low-emission ehicles. Howe e , China’s policy has he leas
impac on manu ac u e s’ p o i s and elies mo e on ma ke demand o educe he a e age ca bon lee emissions compa ed
o he policies in he EU and he U.S. In conclusion, his pape unde sco es ha di e en policy sys ems yield a ying p o i
ou comes o manu ac u e s, necessi a ing adjus men s o p oduc ion po olios o sus ained p o i abili y and he signi icance
o ma hema ical models in aiding manu ac u e s’ unde s anding o e ol ing policies and making in o med p edic ions in a
dynamic egula o y landscape.
Keywo ds: au omo i e p oduc ion; g een ansi ion; in e na ional emission policies; egula o y impac ; sus ainabili y
1. In oduc ion
As mode n indus ializa ion su ges o wa d, humani y
con on s he complex challenges o clima e change. This
encompasses he onse o ex eme wea he pa e ns and el-
e a ed empe a u es, bo h d i en by he incessan elease o
copious amoun s o g eenhouse gases in o he a mosphe e.
The excessi e emissions o g eenhouse gases, such as ca -
bon dioxide (CO2)and me hane (CH4), ins iga e he g een-
house e ec , culmina ing in he a - eaching issue o global
I would like o since ely hank my supe iso , Maximilian Kol e o his
hough ul guidance and suppo h oughou my hesis. He no only
helped me app oach he p oblem mo e e ec i ely bu also p o ided alu-
able eedback o imp o e he cla i y and eadabili y o my w i ing. I am
also g a e ul o P o esso Kolish o p o iding he da a and ini ial li e a-
u e ha laid he g oundwo k o my esea ch.
wa ming. This phenomenon poses a h ea o he exis ing
ecosys em, mani es ing in dis up i e wea he pa e ns and
ex eme clima ic e en s (Yo o & Al., 2020). No ably, he p i-
ma y sou ce o CO2emissions s ems no only om indus ial
p oduc ion bu also om ehicula exhaus (Huang e al.,
2015). T adi ional ehicles p edominan ly powe ed by gaso-
line and diesel gene a e subs an ial CO2emissions in day-
o-day usage. In esponse o his en i onmen al challenge,
he elec ic ehicle concep eme ges as a iable solu ion. By
u ilizing elec ici y as he p ima y powe sou ce, elec ic e-
hicles could educe ca bon emissions, posi ioning hem as a
mo e eco- iendly al e na i e (Cos a e al., 2021)
Cu en ly, he e a e ou main ypes o ehicles: In e nal
Combus ion Engine Vehicles (ICEV), Plug-in Hyb id Vehicles
(PHEV), Ba e y Elec ic Vehicles (BEV), and Fuel Cell Elec-
DOI: h ps://doi.o g/10.5282/jums/ 10i3pp748-780
© The Au ho (s) 2025. Published by Junio Managemen Science.
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(A ibu ion 4.0 In e na ional). Open Access unding p o ided by ZBW.
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 749
ic Vehicles (ICEV) (Vache a & Hino , 2019). ICEV ca s a e
he mos adi ional, powe ed by gasoline and emi ing a el-
a i ely high amoun o CO2. BEV and FCEV a e pu e elec ic
ehicles wi h ze o ca bon emissions, collec i ely e e ed o
as ZEV (Ze o Emission Vehicles). The dis inc ion be ween
hese wo ehicle ypes lies in hei powe sou ces: BEVs use
ba e ies cha ged wi h elec ici y, while FCEVs u ilize bio-
uels and hyd ogen-powe ed uel cells, which can also be
conside ed hyd ogen-powe ed ehicles (Pa ikh e al., 2023).
PHEVs ep esen a middle g ound, wi h lowe ca bon emis-
sions compa ed o ICEVs. They ope a e on a hyb id powe
sou ce, u ilizing bo h elec ici y and pe ol o gasoline simul-
aneously. These di e en ehicle ypes ha e signi ican a i-
a ions in p oduc ion cos s and sales e enue, depending on
ac o s such as p oduc ion yea , ype, and size (Lipman &
Delucchi, 2006).
The ZEV and PHEV ehicle ypes p oduce less CO2 wi h
lowe ailpipe emissions, which can help mi iga e he p ob-
lem o global wa ming. Howe e , despi e he inc easing pop-
ula i y o elec ic and low-ca bon emission ehicles, he cos
o new ene gy ehicles emains highe han adi ional ehi-
cles, leading o lowe p o i s o manu ac u e s (Cuenca e
al., 2000). Vehicle p oduce s need o main ain ma ke com-
pe i i eness and p io i ize p o i and inancial gains, mak-
ing i challenging o pe suade hem o p io i ize he p o-
duc ion o less p o i able bu en i onmen ally iendly ehi-
cles. To encou age manu ac u e s o educe lee emissions,
which ep esen he a e age amoun o CO2 p oduced ac oss
hei p oduc ion po olio, a ious go e nmen s ha e in o-
duced policies and egula ions. Di e en go e nmen s ha e
adop ed unique app oaches o es ablish coun y-speci ic poli-
cies (An e al., 2011). Howe e , he p ecise e ec i eness o
hese policy sys ems on manu ac u e s’ p oduc ion po olios
emains unclea .
This a icle ocuses on h ee p ima y ma ke s: Eu ope,
China, and he USA, each wi h i s own emission egula ions.
In Eu ope, he emission policy is e e ed o as he Supe
C edi Policy, in China, i is known as he Dual C edi Pol-
icy, and in he USA, i is named he US C edi Policy. Fu he
de ails abou hese egula ions can be ound in Sec ion 3.4.
In his scena io, wo s akeholde s exis : he manu ac u e
and he policymake . Fo he manu ac u e , hey need o de-
e mine quickly how di e en policies will a ec hei busi-
ness p o i and make quick esponses o he p oduc ion po -
olio o ensu e p o i abili y. Fo he policymake , hey need o
balance he p o i o he manu ac u e and he ca bon emis-
sion. As Dominioni and Fau e (2022) shows, an imbalanced
policy can lead o ei he a loss o ax income o he limi ed
e ec i eness o emission policies. Al hough he go e nmen
has spen a lo o ime discussing he de ails o he policy
and unde s ands ha e en small pa ame e changes can a -
ec policy e ec i eness, e alua ing he policy’s e ec is com-
plex. Policies mus p ecede ma ke eac ions, and pas esul s
a e un eliable p edic o s due o changing demand and p o-
duc ion si ua ions. Once a policy is published, i is di icul
o wi hd aw. Fo manu ac u e s, ega dless o how he pol-
icy pe o ms, hey need o unde s and how di e en policies
and changes in policy pa ame e s will a ec hei p oduc ion
po olio and e enue o adjus hei business s a egy. Di -
e en coun ies ha e hei own goals in se ing up policies,
and i is essen ial o manu ac u e s o analyze how di e -
en policies in di e en coun ies di e o di e en ia e hei
p oduc ion s a egy and ensu e be e p o i abili y.
O e all, his s udy aims o compa e a ious emission poli-
cies and assess hei impac on manu ac u ing po olios o
p oduce s ac oss di e en pa ame e scena ios in a quan i a-
i e manne . In his a icle, ope a ions esea ch me hods a e
used o simula e he e ec o di e en policies based on eal-
is ic es ins ances. By cons uc ing mixed-in ege linea op i-
miza ion models o di e en policies and demand scena ios,
policymake s as well as ehicle manu ac u e s can ha e a de-
ailed quan i a i e iew o assess policy e ec i eness (Thies
e al., 2022). The da a comes om la ge global ehicle man-
u ac u e , and he policy in o ma ion is based on he cu en
policy se up as o 2023. Mo eo e , since he ma hema ical
model is lexible in changing pa ame e s, he pa ame e s and
da a can be adjus ed o e lec he cu en si ua ion and make
mo e accu a e p edic ions and analyses.
The emainde o his s udy is s uc u ed as ollows: In
Sec ion 2, I discuss he li e a u e ela ed o his opic on ind-
ing ou he emission policy impac . In Sec ion 3, I p esen
he model wi h de ailed o mula ion and pa ame e s, along
wi h di e en models o he h ee emission policy sys ems
in Eu ope, China, and he US. In Sec ion 4, I show how he
model is sol ed o di e en policy sys ems, wi h he main
discussion ocusing on he Supe C edi Sys em in Eu ope.
In Sec ion 5, I lis ou he es ins ances pe o med and he
s uc u e o he design o he expe imen in e alua ing di e -
en policy sys ems. In Sec ions 6, I p esen he inal esul s,
including he de ailed po olio, as well as sensi i i y and ac-
o analysis o di e en demand scena ios and pa ame e
se ings. Finally, in Sec ion 7, I d aw conclusions ega ding
he di e en policy sys ems and p o ide an ou look o u -
he esea ch.
2. Li e a u e Re iew
Va ious app oaches ha e been p oposed o assess he e -
ec s o emission policies on ca manu ac u e s, and hey can
be classi ied in o i e p ima y ca ego ies. Empi ical s udies
and economic models ely on his o ical da a and economic
p inciples o conduc analyses on a b oad scale. Ma ke sce-
na io models c ea e hypo he ical ma ke condi ions o e al-
ua e policy impac s. A echnology s a egy model employs
ma hema ical modeling o assis manu ac u e s in making
decisions ega ding he adop ion o di e en ehicle mod-
els wi h a ious mo i e echnologies. Simula ion-based plan-
ning models use simula ions o p ojec long- e m e ec s o
he policy egula ion o he manu ac u e . Indi idual ehicle
manu ac u e ma hema ical models a e ailo ed o speci ic
manu ac u e s o in-dep h analysis wi h ou pu o de ailed
p oduc ion plan and lee emission end.
In he ca ego y empi ical s udies and economic models,
wi h he empi ical s udies ga he da a on emissions, p oduc-
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780750
ion, and ma ke beha io o analyze he impac o emission
policies and he economic model use economic p inciples o
p edic how policy changes may a ec ca manu ac u e s,
such as changes in cos s, p ices, and ma ke demand. Fo
he empi ical s udies Be gek and Be gg en (2014) e iewed
he empi ical s udies on en i onmen al policy and ound ha
policy ins umen s play a c ucial ole in d i ing en i onmen-
al inno a ion ac oss sec o s. Also, (Y. Wang e al., 2018)
analyzed compliance s a egies o ou di e en au omake s
unde dual-c edi egula ions, conside ing uel economy and
NEV (New Ene gy Vehicle) p oduc ion which includes he
ZEVs (Ze o Emission Vehicles) and PHEVs (Plug-in Hyb id
Elec ic Vehicle), compa ing hei app oaches and sugges ing
cos -e ec i e s a egies wi h egula o y imp o emen s.
Besides hese empi ical analyses, he economic model
and p icing model ha e been buil up o analyze how he
policy would a ec he ehicle manu ac u e in a b oad i-
sion. (Mo an e al., 2020) conduc ed mic o-le el s udies
wi h a mul i egional inpu -ou pu economic model o an-
alyze he consume -o ien ed policy and showed ha hese
policies would educe ca bon emissions by abou 25%. Addi-
ionally, he go e nmen p icing model o dual-c edi policy
published by (Yang e al., 2023), which compa ed wi h he
ma ke p icing model, shows ha he dual-c edi policy ben-
e i s ene gy sa ing and emission educ ion in he anspo
sec o . Mo eo e , in he s udy by (Ma e al., 2021), a supply
chain model includes wo s akeholde s, he engine supplie
and au omake s, o analyze he ca bon emission policy e ec
on he p oduc ion o he ICEV and NEV ehicle. (Michalek
e al., 2005) also conside ed he impac o he compe i ion
o o he manu ac u e in he pape and p oposed ma hema -
ical models o enginee ing pe o mance, consume demand,
and manu ac u ing cos s, combined wi h game heo y o he
ma ke segmen . Fo hese models, he end could be ob-
se ed om he economic pe spec i e, bu i could be oo
b oad in scope ha makes lack o some p ecision in explain-
ing some de ails in he impac o he emission policy on he
p oduc ion plans o he indi idual manu ac u e .
Besides he economic models, a ious ma ke scena io
models ha e been used o analysis he impac o he emis-
sion policy on he manu ac u e ’s po olio. These models
c ea ing a ious hypo he ical ma ke scena ios including
he anspo a ion sec o and hen e alua ing how di e -
en emission policies would impac ca manu ac u e s unde
hese scena ios. Fo example, Thiel e al. (2016) used a
TIMES-based ene gy sys em model o examines he impac
o s ic e EU CO2 ca legisla ion on anspo - ela ed emis-
sions, Elec ic ehicle up ake, oil consump ion, and ene gy
cos s. This model is a modeling pla o m ha conside ac o s
like ene gy p oduc ion, cos s, and en i onmen al impac and
could helps make in o med decisions abou emission policies
and esou ces. Hill e al. (2018) p o ided h ee models o
PRIMES (global ene gy-economic model)-TREMOVE ( ans-
po a ion policy), GEM-E3T (model wi h mac oeconomic,
ene gy, and en i onmen al policies) and he JRC DIONE
(model o assessing ene gy and en i onmen al policies)
o analysis he o e all ma ke si ua ion in conside ing en-
e gy, clima e, anspo a ion and he Eu ope emission policy.
These models all each simila conclusion ha he EU policy
a e e ec i e in educing GHG(G eenhouse Gas) emissions.
The ALTER-MOTIVE modelling me hod also been conduc ed
by Ajano ic and Haas (2017) o in eg a e he ene gy sys em
and anspo a ion showing ha GHG emissions could be
educed a leas by 33% in a selec ed policy scena io. O he
han hese p e- o med model, Ou e al. (2018) de elops he
New Ene gy and Oil Consump ion C edi s Model o quan i y
he impac s o his policy in scena io om 2016 o 2020 o
discuss he e ec o he dual c edi sys em in China on he
elec ic ehicle sales. While hese scena io models a e use-
ul o gene a ing con incing esul s, hey may o e simpli y
ma ke condi ions and he beha io s o indi idual manu-
ac u e s. These models can assess he e ec s o emission
policies on a b oad scale and om a ma ke pe spec i e,
bu hey may no p o ide a comp ehensi e unde s anding o
how indi idual ca manu ac u e s would be impac ed by o
espond o hese policies.
Speak o make analysis om indi idual le el, he e a e
some s udies modeled he p oblem o indi idual ca man-
u ac u e s o ind p o i maximizing echnology s a egies
conside ing emission policies. S. Wang e al. (2018) build a
mixed-in ege ma hema ical model wi h decision a iables
ep esen ing a ious mo o echnologies in a echnology
combina ion (TC) p oblem. This model is designed o de-
sc ibe an au omake ’s decision-making p ocess, and I u ilize
a gene ic algo i hm o assess he impac o China’s dual c edi
policy om 2020 o 2025. Mo eo e , Romejko and Nakano
(2017) inc ease he ange o he mo o echnology pa h o
he ehicle p ojec s and explo es a mo e di e se ange o
eigh al e na i e uel ehicles (AFVs), including EVs, FCVs,
CNG (Comp essed Na u al Gas) ehicles, and mo e, o p e-
dic an op imal AFV po olio o achie ing economic and
ene gy secu i y goals. Mo eo e Zhu e al. (2022) p oposed
a decision-making algo i hm o au omake s’ p oduc ion
s a egies unde he dual-c edi policy in China. This algo-
i hm e eals how au omake s’ choices ansi ion be ween
s a egies based on h esholds and go e nmen a ge s. Fu -
he mo e, o he han changing be ween he cons ain s and
pa ame e , Kellne e al. (2021) also use mul i-objec i e op i-
miza ion me hod analysis in he echnology selec ion o ind
he op imal powe ain echnology po olio. Fo hese mod-
els, some indi idual le el o he ehicle po olio planning
has been pe o med. Howe e , hese models only p o ide
he ou come o which mo o echnology o ini ialize, wi h-
ou o e ing in o ma ion on he ac ual quan i y p oduced
o di e en ehicle echnology ypes. Consequen ly, hey
do no o e a comp ehensi e iew o he esul ing a e age
lee emission alues o he de ailed cos s uc u e o ehicle
manu ac u e s and a e no capable o p o iding a e y clea
pic u e o he ehicle po olio and he impac o emission
policy on hese po olios.
Simula ion me hod is also a popula modeling me hod
o ob ain ehicle po olios. Kieckhä e e al. (2012) a yea
1970 used a hyb id ma ke simula ion app oach o s a egic
planning o au omo i e ehicle po olios o p edic powe -
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 751
ain ma ke sha es ac oss ehicle sizes based on po olio
o e ings, consume beha io , and ma ke condi ions. Also
Kieckhä e e al. (2009) c ea es a amewo k using sys em
dynamics and agen -based simula ion o analyze he p oduc
s a egies which aid manu ac u e s in e ec i e echnology
in oduc ions ac oss ehicle classes while conside ing egu-
la ions and ma ke s. Mo eo e he NW (Newman and Wa s)
’small wo ld’ ne wo k model also been used by Hu e al.
(2020) o explo e he dynamic e ec s o di e en policies on
he di usion o elec ic ehicles. Fo hese simula ion model,
hey could p o ide a close o eali y ou pu o he ehicle
po olio esul hus indica e he impac s o he emission pol-
icy, bu he model would ake long imes o be sol ed. I
could be ine icien in ime when implemen ing la ge numbe
o es ing scena ios in o hose simula ion model o ge ing
he solu ion insigh s.
Recen ly, Thies e al. (2022) p oposed a no el model ha
concen a es on he indi idual ehicle manu ac u ing po o-
lio. This a icle analysis how he EU emission policy e ec s
he ehicle po olio using op imiza ion model om he pe -
spec i e o an indi idual ehicle manu ac u e . This model
has p o ided a de ailed p oduc ion po olio, showing he op-
imal quan i y o each ehicle ype o be p oduced annually,
along wi h a comp ehensi e esou ce plan and accu a e a -
e age lee emissions o each yea ’s p oduc ion and p o ide
a basic amewo k o he op imiza ion model used in his a -
icle. In his a icle, he base model o Thies e al. (2022)
is ex ended by conside ing no only he EU policy bu also
he o he wo majo policy sys em in China and he US. Fu -
he mo e, he EU policy is conside ed in mo e de ail, as he
supe c edi elaxa ion is conside ed. This no el model o e s
a mo e comp ehensi e and ealis ic analysis o policy e ec s
om he pe spec i e o indi idual ca manu ac u e s and can
be sol ed in ew minu es. This model p o ides de ailed in-
o ma ion, including he ehicle ini ializa ion plan, p oduc-
ion quan i y o each ehicle ype in each yea , a e age lee
emissions in each yea , ma ke sha e o ehicle ypes, and
he de ailed cos s uc u e. Mo eo e , i allows o easy pa-
ame e adjus men s o cus omize he esul s as he policy
changes.
3. Me hod
This sec ion p esen s a ma hema ical model o p ojec
po olio planning conside ing di e en emission policies.
The o e all oad map o he model o mula ion is p esen ed
in Sec ion 3.1 and Sec ion 3.2 explains all he pa ame e
in o ma ion o he ma hema ical model. A e ha , a base
model wi hou emission policies is p esen ed in Sec ion 3.3,
be o e i is ex ended by he emission policies o Eu ope,
China and he U.S. in Sec ion 3.4.
3.1. Model Road Map
Figu e 1desc ibes he oadmap o he op imiza ion
model in his a icle. The planning ho izon is 10 yea s, om
2025 o 2035, du ing which he manu ac u e can change
i s po olio p oduc ion decisions. Fo he pe iod ou side he
planning pe iod, he se ings emain ixed.
Wi hin he planning ho izon, he manu ac u e can make
se e al decisions, wi h he p ima y one being he de e mi-
na ion o p oduc ion quan i ies (q ) o each ehicle ( )in
each pe iod ( ). To ensu e hese p oduc ion quan i ies, he
esou ce plan has also been es ablished, which includes he
capaci y (k ) o each esou ce ( )in each pe iod ( ). Ad-
di ionally, he esou ce adjus men plan con ains (kRampup
)
and (kRampdown
), ep esen ing he equi ed inc ease and de-
c ease amoun s o esou ce ( )in pe iod ( ). The objec-
i e unc ion aims o maximize he Ne P esen Value (NPV)
o he en i e p oduc ion po olio, which is discoun ed he
manu ac u e ’s p o i ob ained by sub ac ing all cos s om
he e enues. Re enues encompass sales e enue gene a ed
om ehicle sales h oughou he planning pe iod, as well
as end-o -pe iod capaci y cash-ou income. Cos s include
p oduc ion expenses, which consis o bo h ixed and a i-
able cos s ela ed o ehicle p oduc ion, expenses o inc eas-
ing o dec easing esou ces o main ain p oduc ion capaci y,
de elopmen cos s o ini ializing new ehicle p ojec s, and
penal y cos s o gains dependen on he speci ic policy sys em
in place.
Se e al cons ain s bind he decision-making p ocess o
ehicle p oduc ion and can be ca ego ized in o i e main ca -
ego ies. The ehicle p ojec cons ain s help de e mine he
ini ializa ion o di e en ehicle p ojec s each yea and limi
he maximum numbe o p ojec s ha can be s a ed. The
p oduc ion esou ce cons ain s a e used o ensu e su icien
esou ces a e a ailable o p oduc ion. The ehicle demand
cons ain is employed o p e en he sale o mo e ehicles
han he ma ke demand, and he p oduc ion olume con-
s ain is used o ensu e he minimum p oduc ion olume
each yea . The policy- ela ed cons ain s in di e en policy
sys ems impose penal ies o es ic ions on he a e age lee
emissions o he p oduc ion po olio.
The inpu s o modeling he p oduc ion p ocess con-
s ain s a e depic ed in Figu e 1. The ehicle p ojec s in-
clude se e al p e-de ined p ojec s ca ego ized by powe ain
echnology ype, size, p oduc ion yea , and powe ange
class. Some ehicle p ojec s a e al eady de e mined be o e
he planning ho izon. Each ehicle p ojec has i s own p o-
duc ion cos , sales e enue, and ailpipe line emission alue.
De elopmen cos s a e assumed o be he same o all ehicle
p ojec s, and he maximum li e cycle is equal o all p ojec s.
A e 2025, wi hin he planning pe iod, he manu ac u e
can ini ialize new ehicle p ojec s i sui able.Each ehicle
p ojec equi es p oduc ion esou ces. Be o e he planning
pe iod, he cu en on-hand esou ces a e p e-de ined. The
manu ac u e mus ensu e ha esou ces can mee he p o-
duc ion quan i y o ehicle p ojec s, which includes decisions
on amping up o down capaci y. Resou ce cos s include
ixed and a iable cos s, wi h he ixed cos pe p oduc ion
esou ce po en ially dec easing due o economies o scale.
Sales quan i ies mus no exceed he ma ke demand o a
gi en yea , segmen ed by ehicle ype, size, and powe ange
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780752
Figu e 1: Op imiza ion Model Road Map
(e.g., IC EV_medium_low). Mul iple ehicle p ojec s can be
conside ed o se e al yea s wi hin a ma ke demand seg-
men , bu each ehicle p ojec is associa ed wi h only one
ma ke segmen . Quan i ies below ma ke demand a e as-
sumed o be sold, and any un illed demand is conside ed o
be los wi h no capaci y payback. The gene al se ings de ine
he amewo k o gene al pa ame e s, such as he assumed
annual in e es a e o NPV calcula ion, u iliza ion loss due
o capaci y inc emen , and he minimum p oduc ion quan i y
equi ed o s ay on he ma ke , wi h ull de ails desc ibed in
sec ion 3.2.3.
In addi ion o he basic se up, one o he policies om Su-
pe C edi Policy in Eu ope, Dual C edi Policy in China, US
Emission Policy in he Uni ed S a es, o no Policy should be
chosen o o m he inal policy-speci ic op imiza ion model.
Each policy en ails speci ic pa ame e s and o mulas o be
conside ed. The pa ame e alues o di e en policies a e
based on cu en esea ch in 2023, combined wi h pe sonal
assump ions, and all a e ela ed o he CO2a e age lee
emissions, as desc ibed in sec ion 3.4.
Once hese se ups a e inco po a ed in o he model and
he model is sol ed, he inal objec i e alue becomes a ail-
able, along wi h de ailed alues o e enues and a ious
cos sec o s. Addi ionally, he lee emissions o each ehicle
p ojec ( )in each pe iod ( )can be de e mined by mul iply-
ing he ca bon cycle emissions (E )by he quan i y p oduced
(q )each yea .
All de ails abou he o mula ion o he op imiza ion
model a e p esen ed in sec ion 3.3.
3.2. Pa ame e In o ma ion o he Vehicle P ojec Po olio
Planning
This sec ion p o ides de ailed pa ame e in o ma ion, in-
cluding se s, indices, decision a iables, and desc ip ions o
all pa ame e s used in he model.
3.2.1. Se s & Indices
The model uses se e al se s and indices, which a e de-
sc ibed in Table 1. These se s and indices a e essen ial o
de ining he pa ame e s and decision a iables in he model.
3.2.2. Decision Va iables
The e a e 11 se s o decision a iables desc ibed in Ta-
ble 2. Th ee o hese a iables a e bina y a iables, while
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 753
Table 1: Se s and Indices
Se & Indices
Se Index Desc ip ion
VνVehicle p ojec s
X∈VVehicle p ojec s a be o e he
planning ho izon
T Pe iods
P∈TPe iods in planning ho izon
M m Ma ke segmen s
R P oduc ion Resou ces
E∈RExis ing p oduc ion esou ces
a he beginning o he
planning ho izon
he es a e con inuous a iables. These decision a iables
play a c i ical ole in he model, enabling he op imiza ion
o he ehicle po olio and he assessmen o a ious policy
impac s.
3.2.3. Pa ame e s
Table 3concludes all he pa ame e s used o he ma he-
ma ical model, including he basic model as well as he ad-
di ional emission policy model and sepa a ed by ca ego ies.
The pa ame e con ains ype cons an , a iable, and ec o .
3.3. Ma hema ical Fo mula ion o he Base Model
This sec ion desc ibes he model o he base model wi h-
ou he emission policy bu p o ides an explana ion o he
objec i e unc ion and he cons ain s in he con ex o a pu e
ehicle manu ac u e se ing.
3.3.1. Objec i e Func ion
Fo he objec i e unc ion, he goal is o maximize he
ne p esen alue (NPV) o he ehicle p ojec in en yea s
pe iod. The in e es a e is assume o be 5%, and he mone-
a y alue is calcula ed in each yea and e u n he inal NPV
alue base on yea 2025. The objec i e unc ion consis s o
h ee majo pa s which a e he ne p o i o he p oduc ion
po olio, he de elopmen cos and capaci y inc emen cos ,
and inally he capaci y cash back cos calcula ed and he end
o he p oduc ion planning phase a yea 2035.
Fo he ne p o i o each yea in he planning pe iod, i
adds up all sale e enue acco ding o he p oduc ion quan i y
and minus he p oduc ion a iable cos as well as he ixed
cos , also he penal y cos due o he emission policy would
be deduc ed acco ding o di e en policy ypes.
Fo he de elopmen cos and capaci y inc emen cos al-
hough he cos a e spen on speci ic yea in he p oduc ion
planning pe iod, bu i is assumed ha he paymen does
no due immedia ely. The paymen and deduc ion could be
e enly dis ibu ed in 7 yea s pe iod wi h each yea abou
14.3% o he o al cos paid. Vec RD
is he pa ame e used
o calcula e he dis ibu ion o he cos and could also be
coun ed a e he p oduc ion pe iod. So he ne NPV is sum-
ma ion o a all pe iods which is ∈T, om 2025 o 2050.
Fo he hi d pa o he capaci y back alue, i is calcu-
la ed a he end o he planning pe iod a yea 2035. This
e m used o coun back he capaci y alue o p e en he
model o e es ima ed he cos o he las ew yea s capaci y
inc ease. Fo he capaci y back cos , i is assumed ha he e-
sou ce on hand would e ain i ’s alue in 10 yea s pe iod and
o each yea , he alue would dep ecia e by 10%. Fo exam-
ple, o he esou ce in 2030, he esou ce would be capaci y
back wi h he es alue o 50%.
Objec i e Func ion:
Maximize NPV wi h:
maxNPV =
X
∈P
((T CSaleP od
−T C P odFixed
−T C Penal y
)·d )
−X
∈T
((T CRD
+T CCapaci y
)·d )+TRCapaBack·d Tmax
(1)
wi h
T CSaleP od
=X
∈V
((suni
−cUni a
)·q )(2)
T C P odFixed
=X
∈R
(cResou ce ·k )(3)
T C Penal y
=cPenal y
(4)
T CRD
=X
∈V
(cRD
·Vec RD
·y )(5)
T CCapaci y
=X
∈R
X
∈P
(cRampU pF ixed ·Vec PC
τ ·yRampupBin
+cRampU pVa ·Vec PC
τ ·kRampup
)(6)
TRCapaBack =X
∈R
zRes Value
(7)
3.3.2. Cons ain s
F om he baseline model, he e a e ou ca ego ies o con-
s ain se s lis ed below. The cons ain s o di e en emis-
sion policies a e in Sec ion 3.4.
Fo he ehicle p ojec s cons ain s se , i consis s o con-
s ain s ela ed o ehicle p ojec ini ializa ion. Cons ain
(8) se s up he ini ial s a o he ehicle p ojec be o e 2025,
he planning ho izon. Cons ain (9) uses a big numbe M o
swi ch he bina y a iable o he s a o he new ehicle
p ojec . Cons ain (10) limi s he allowed s a o he num-
be o ehicle p ojec s in e e y yea due o esou ce limi s,
and Cons ain (11) o ces he quan i y p oduced o 0 i he
ehicle exceeds he p ojec li e cycle o max yea s.
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780754
Table 2: Desc ip ion o Decision Va iables
Decision Va iables
Va iable Type Range Desc ip ion
y Bina y {0,1}Vehicle p ojec s a ing indica o wi h 1 meaning ehicle is ealized
and 0 o he wise
q Con inuous R+Numbe o ehicle p ojec p oduced a ime pe iod τ
k Con inuous R+Capaci y o esou ce in pe iod
kRampup
Con inuous R+Inc ease o p oduc ion esou ces a he beginning o pe iod
kRampdown
Con inuous R+Dec ease o p oduc ion esou ces a he beginning o pe iod
yRampup
Bina y {0,1}Resou ce inc ease indica o wi h 1 meaning he e is an inc ease in
esou ce a he beginning o pe iod , wi h 0 o he wise
zRes Value
Con inuous R+Residence Value o esou ce a he end o he planning ho izon
cPenal y
Con inuous R+Penal y cos paid o he excess CO2Emission in pe iod
P elax
Con inuous R+Supe c edi policy elaxed pe cen age
y1Bina y {0,1}Bina y a iable used o o m maximum o minimum cons ain
DDual
Con inuous R+Cos paid(+) o Re enue ea ned (-) o dual c edi policy
Fo he p oduc ion esou ce cons ain s, Cons ain (12)
desc ibes he esou ce usage cons ain , and Cons ain (13)
indica es ha in he amp-up pe iod, he esou ce would only
be a ailable a θmax pe cen age. Cons ain (14) adjus s he
on-hand esou ce a each yea o he planning pe iod a e
he p e ious amp-up and amp-down decisions ha would
be made. Cons ain (15) swi ches on he ixed cos o amp-
ing up he capaci y. Cons ain (16) calcula es he es alue
o he on-hand esou ces a he end o he planning yea
2035, wi h V es ep esen ing he emaining alue, es ima ed
as 5% o he o al cos paid o inc easing his amoun o e-
sou ce.
Cons ain (17) is he ehicle demand cons ain o en-
su e ha he p oduc ion olume, which is less han he de-
mand, would be sold o ea n p o i . Cons ain (18) gua -
an ees he minimum p oduc ion olume in each yea in he
planning pe iod.
Vehicle P ojec s
y =yini ial
∀ ∈X(8)
q ≤M·y ∀ ∈X,∀ ∈P(9)
X
∈V:SOP =
y ≤SOPmax ∀ ∈P(10)
q =0∀ ∈V,∀ ∈T: ≤SOP
∨ ≥SOP + max (11)
P oduc ion esou ces
X
∈V: =R
q ≤k ∀ ∈R,∀ ∈P(12)
X
∈V: =R
q ≤θmax ·k
∀ ∈R,∀ ∈P: = Rampup
(13)
k =kini ial
+X
τ∈P:τ≤
(kRampup
τ−kRampdown
τ)
∀ ∈R,∀ ∈P(14)
kRampup
≤M·yRampup
∀ ∈R,∀ ∈P(15)
zRes Value
≤V es ·X
∈U
((yRampup
·cRampU pF ix ed )
+ (cRampU pVa ·kRampUp
)) ∀ ∈R(16)
Vehicle Demand
X
∈V:m =m
q ≤dm ∀m∈M,∀ ∈P(17)
P oduc ion olume
X
∈V
q ≥qmin ∀ ∈P(18)
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 755
Table 3: De ailed Pa ame e In o ma ion
Pa ame e
Pa ame e
Ca ego y
Pa ame e Type Desc ip ion
Vehicle P ojec s SOP Vec o S a o p oduc ion ime o ehicle p ojec
cUni a
Cons an Uni a iable cos o ehicle p ojec
sUni
Cons an Uni sale e enue o ehicle p ojec
E Vec o CO2cycle emission o ehicle p ojec
m Vec o Ma ke segmen ype o ehicle p ojec
Vec o Resou ce ype needed o ehicle p ojec
max Cons an Maximum du a ion o he selling ime pe iod
Vec RD
Vec o Dis ibu ion o cash low: Pe cen age o o al de elopmen cos o ehicle
p ojec in pe iod
cRD
Cons an De elopmen cos o a new ehicle model
yini ial
Vec o P ese ehicle ealiza ion indica o o ehicle p ojec
P oduc ion
Resou ce
cRampUpFixed Cons an Fixed cos o amping up p oduc ion esou ces
cRampUpVa Cons an Uni a iable cos o he p oduc ion esou ces
Rampup
Vec o Ramp-up pe iod o p oduc ion esou ce
Vec PC
cu ampup Vec o Dis ibu ion o cash low: Pe cen age o o al p oduc ion cos o amp-up
pe iod cu dis ibu ed in cu en pe iod ampup
cResou ce Cons an Cons an cos o each uni o p oduc ion esou ce on hand
kini ial
Vec o Capaci y o p oduc ion esou ce be o e he planning ho izon
V es Cons an Residence alue o p oduc ion esou ce a he end o planning ho izon
Demand dm Vec o Numbe o ehicles demanded in ma ke segmen ma pe iod
Pmin Cons an Minimum pe cen age o o al demand needed o be ul illed
Emission
Regula ion
ELaw
Cons an To al CO2 lee emission h eshold in pe iod
σlow Cons an The maximum lee emission alue o he ca ego y o low emission ehicles
in he supe c edi policy sys em
γmu iplie Cons an PHEV mul iplie ac o in supe c edi policy sys em
ERelax Law
Va iable Relaxed h eshold o o al CO2 lee emission in pe iod
PEV h es
Va iable The egula ed elec ic ehicle pe cen age o mee he elaxa ion c i e ia in
pe iod
PSupe M ax Cons an Maximum allowance o elaxa ion in Supe C edi Sys em
cCO2Cons an Uni penal y paymen cos pe g/km o each ehicle sold
SCAFC
Va iable To al s anda d CAFC c edi p o ided in pe iod
ACAFC
Va iable Ac ual CAFC c edi consumed in pe iod
SFC
Va iable Fuel consump ion s anda d o ehicle p ojec in pe iod
AFC
Va iable Ac ual uel consump ion o ehicle p ojec in pe iod
WFC
Va iable Weigh ac o o low emission ehicle p ojec in CAFC c edi
SN EV
Va iable To al s anda d NEV c edi o new ene gy ehicles equi ed in pe iod
AN EV
Va iable Ac ual NEV c edi gained o new ene gy ehicles in pe iod
k Va iable Weigh ed NEV c edi a io ac o o ehicle p ojec
R Va iable Ta ge a io in pe iod discoun ed o NEV c edi
CDual Cons an Mone a y alue o one dual c edi in dual policy sys em
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780762
Fo he a ge NEV sco e calcula ion is ep esen in Equa-
ion (32). As o he a ge a io R , i can a y om yea o
yea . In 2025, he a ge a io is app oxima ely 20%, inc eas-
ing o 40% by 2030, and e en ually eaching 50% by he end
o 2035. The a ge a io can be adjus ed each yea , al hough
he e a e no o icial ules announced. In his model, he a -
ge a io is assumed o inc ease linea ly and e enly each yea ,
wi h speci ic pe cen ages de ailed in Table 7.
Table 7: NEV Ta ge Ra ios o he Yea s 2025-2035
NEV Ta ge Ra io (2025-2035) [%]
Yea Ta ge Ra io
2025 20%
2026 24%
2027 28%
2028 32%
2029 36%
2030 40%
2031 42%
2032 44%
2033 46%
2034 48%
2035 50%
The o mula o he calcula ion o he ac ual NEV sco e
is in Equa ion (31). The weigh ed ac o k o he calcula-
ion o he ac ual NEV sco e could be ha d o p edic since
he alue is di e en o speci ic ehicle model, o simply he
model, his model use he a e age sco e alue and shown in
able 8.
Table 8: NEV C edi s pe Vehicle Type
Weigh ed Fac o pe Type
Vehicle Type Es ima ed NEV C edi
PHEV 1.6
BEV 4
FCEV 4.8
Fo he mone a y alue CDualC edi , in Chen and He
(2022) a icle, he a e age exchange cos o one c edi
is be ween 2600 −2900 RMB, so in his model i is assumed
o be 330 Eu o a e he cu ency exchange.
U.S. Emission Policy
The de ailed emission h eshold alues Elaw
in Equa-
ion (34) used in he US emission model a e sou ced om
he s anda d eleased by he US En i onmen al P o ec ion
Agency (Regis e , 2023) and a e summa ized in Table 9. To
main ain consis ency and p ecision, he uni s in he policy,
o iginally gi en in uni s o g/mile, ha e been con e ed o
uni s o g/km, wi h alues ounded o one decimal digi .
5.2. Policy Compa ison Expe imen
This pape aims o employ ma hema ical models o ex-
plo e he di e ences among h ee dis inc emission policies
in Eu ope, China, and he US, while compa ing hem o he
baseline model wi h no emission policy in place. The esul s
will be e alua ed unde wo di e en demand scena ios: con-
se a i e and inno a i e demand. Each policy’s pa ame e s
ha e been calcula ed and es ima ed based on publicly a ail-
able in o ma ion.
Fo each policy scena io and demand ype, a comp ehen-
si e analysis will be conduc ed, including an examina ion
o he ehicle ini ializa ion schedule and p oduc ion quan-
i ies. Addi ionally, beyond he decision a iables, he objec-
i e unc ion will be ho oughly examined. This examina ion
will encompass cos s uc u es and a e age lee emissions
o gain insigh s in o he impac o a ious emission policies.
Table 10 summa izes he expe imen ’s ou line.
5.3. Sensi i i y and Fac o ial analysis o Di e en Emission
policies
In addi ion o compa ing di e en emission policies, his
pape will also conduc sensi i i y and ac o analyses o
each emission policy o assess how policy pa ame e s a ec
policy e ec i eness and iden i y he pa ame e s wi h he
mos signi ican impac on emission policies. Fo sensi i i y
analysis, each ac o will be ca ego ized in o h ee le els:
low, basic, and high, and es s will be conduc ed a hese
le els o analysis. In he ac o ial analysis, a 2-le el ac o ial
analysis will be pe o med, educing each ac o o wo le -
els: high and low. A 1/2 ac ion o he ull ac o ial design
me hod will be used o enhance he e iciency o he ac o ial
analysis.
Fo he analyzed ou pu s, due o he ex ensi e es ing e-
qui ed, only wo ypes o ou pu s will be compa ed: he ne
o al NPV (Ne P esen Value) and he pe cen age o low ca -
bon emission ehicles. NPV is he objec i e unc ion a o ed
by ca manu ac u e s, as hey seek o maximize NPV. How-
e e , his objec i e may con lic wi h he he educ ion o
ca bon emissions. The e o e, he pe cen age o low ca bon
emission ehicles will also be analyzed o assess he policy’s
impac on social bene i s om he go e nmen ’s pe spec i e
and e alua e policy e ec i eness. Table 11 summa izes he
Design o Expe imen (DOE) o he emission policy.
5.3.1. Supe C edi Sys em
Fo he Supe C edi sys em policy in Eu ope, ou ac o s
will be conside ed o he analysis. The i s ac o is he de-
mand ype, which includes wo scena ios: conse a i e and
inno a i e. The nex ac o is he h eshold pe cen age o
elaxa ion, which is de ined as he pe cen age o low emis-
sion ehicles wi h less han 50 g/km emissions. The h esh-
old is se in wo s ages, one be o e 2030 and one a e 2030.
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 763
Table 9: Fede al Vehicle Emissions S anda ds
Yea
2025 2026 2027 2028 2029 2030 2031 2032 and
la e
Emission s anda d
(g/km)149 152 134 116 99 91 82 73
Emission s anda d
(g/km)92.9 94.8 83.5 72.3 61.7 56.7 51.1 45.5
Table 10: Compa ison o Di e en Policy Expe imen s
Policy Compa ison Expe imen
Policy Type Demand Ou pu
Supe C edi Sys em Con/Inno •Vehicle ini ializa ion schedule
•Vehicle p oduc ion quan i y
•Cos s uc u e
•A e age lee emission esul
Dual C edi Sys em Con/Inno
US C edi Sys em Con/Inno
Baseline Model (No emission policy) Con/Inno
Fo he sensi i i y analysis, he ange o he h eshold be o e
2030 is om se a le el 5%, 15% and 25%, and a e 2030,
i is a he le el o 25%, 35% and 45%. The hi d ac o is he
maximum allowed h eshold pe cen age, which is a he le el
o 1%, 5% and 9%. Finally, he PHEV mul iplie , which p o-
ides a base pe cen age o PHEV- ype ehicles, is included
in he analysis. I can be se o ei he "on" o "o " o es i s
e ec on NPV alue and he ac ual quan i y o low ca bon
emission ehicles. These ac o s will be analyzed o unde -
s and hei impac on he NPV alue and he quan i y o low
ca bon emission ehicles.
5.3.2. Dual C edi Sys em
In he China dual c edi sys em, ou ac o s will be con-
side ed o analysis. The i s ac o is he demand ype,
which is simila o he supe c edi sys em. The emaining
h ee ac o s a e es ed by pe cen age changes, and hey in-
clude one impo an indica o o calcula ing he CAFC sco e,
one indica o o he NEV sco e, and one ac o conside ing
he exchange cos o c edi sco e ealiza ion. The le el o
change o hese ac o s is he same which a e -50%, 0% and
50%. These ac o s will be analyzed o unde s and hei im-
pac on he NPV alue and he quan i y o low ca bon emis-
sion ehicles.
5.3.3. US C edi Sys em
Fo he US c edi sys em, apa om he demand ype ac-
o , one mo e ac o which is he pe cen age change o egu-
la ed h eshold a e conside ed a le el o -10%, 0% and 10%.
Also s ic o non-s ic compliance would be es ed as an-
o he ac o in his scena io. Fo s ic compliance meaning
ha he h eshold could no be exceed and non-s ic com-
pliance i is assumed he penal y cos would be simila in EU
wi h 95 =
C/((g/km) ×yea ).
6. Resul
To assess he impac o a ious policy sys ems, Sec ion 6.1
includes an analysis o he op imal p oduc ion plan unde
ou di e en policy scena ios. Addi ionally, he conside a-
ion o a mul i ude o pa ame e s comes in o play, wi h each
ha ing he po en ial o in luence he inal ou comes o hese
egula ions. Fo his pu pose, Sec ion 6.2 conduc s sensi i i y
analysis a h ee di e en le els (low, basic, and high) o se-
lec ed pa ame e s unde di e en policies, while Sec ion 6.3
pe o ms a ac o analysis o hese pa ame e s.
6.1. Op imal P oduc ion Plan
The de ailed plan includes he ini ializa ion o ehicle
p ojec s o each yea om 2025 o 2035 , as well as he p o-
duc ion quan i y o ou di e en ypes o powe ain ech-
nologies.
In he ehicle p ojec ini ializa ion sec ion, he blocks a e
sepa a ed by powe ain echnology, ehicle p ojec size, and
yea . G ay blocks ep esen he s a o a powe ain echnol-
ogy in ha yea , while whi e blocks indica e ha he ehi-
cle p ojec will no be ini ia ed. Fo he p oduc ion quan i y,
each yea om 2025 o 2035 is ca ego ized by powe ain
echnology and illed wi h di e en pa e ns, as desc ibed in
he s acked ba cha .
Addi ionally, he a e age lee emissions o each yea
we e plo ed on a line cha , wi h he EU- egula ed lee emis-
sion h eshold as a e e ence o compa ison. The objec i e
alue, composed o six main componen s [Capaci y income
a he end o he pe iod, Capaci y inc ease cos , De elopmen
cos , P o i , Fixed cos , Penal y cos /Dual C edi alue], was
depic ed in a ba cha . The inal Ne P esen Value (NPV)
was shown on a line cha o u he analysis. These isu-
aliza ions p o ide insigh s in o he di e en policy scena ios
and hei e ec s on p oduc ion planning and emissions.
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780764
Table 11: Summa y o Design o Expe imen s (DOE) o Emission Policy
Summa y o DOE o Emission Policy
Policy Type Fac o s Le el Type Ou pu s
Supe C edi Sys em
Demand Type ConInno Op ion
NPV, EC pe cen age
Relaxed Th eshold Be o e 2030 [5% o 25%]
A e 2030 [25% o 45%]Pe cen age
Maximum Th eshold 1% o 9% Pe cen age
PHEV Mul iplie Y/N Op ion
Dual C edi Sys em
Demand Type Con/Inno Op ion
NPV, EC pe cen age
% change o S anda d
Fuel Consump ion -50% o 50% Pe cen age
% change o exchange
p ice -50% o 50% Pe cen age
% change o NEV
weigh ac o -50% o 50% Pe cen age
US C edi Sys em
Demand Type Con/Inno Op ion
NPV, EC pe cen age
% change o CO2
h eshold -10% o 10% Pe cen age
S ic compliance Y/N Op ion
Table 12: Pa ame e Changes o Dual C edi Sys em
Dual C edi Sys em Pa ame e Changes
S anda d Fuel Consump ion
(CAFC) Exchange P ice NEV Weigh ed Fac o (NEV)
Example Base
(small_PHEV_low_2025) 3.6 =
C330 1.6
-50% Pe cen age Change 1.8 =
C165 0.8
+50% Pe cen age Change 5.4 =
C495 2.4
Table 13: Pa ame e Changes o US C edi Sys em
US C edi Sys em Pa ame e Changes
Yea 2025 2026 2027 2028 2029 2030 2031 2032 and
la e
Emission s anda d
(g/km)92.9 94.8 83.5 72.3 61.7 56.7 51.1 45.5
-10% Pe cen age
Change 83.6 85.3 75.2 65.1 55.5 51.3 36.0 41.0
+10% Pe cen age
Change 102.2 104.3 91.9 79.5 67.9 62.4 56.2 50.1
6.1.1. Conse a i e Demand
The ollowing esul s we e calcula ed unde he assump-
ion o a conse a i e ma ke demand o low-emission e-
hicles, cha ac e ized by a lowe g ow h a e in low-emission
ehicle adop ion. This scena io ep esen s a mo e cau ious
ma ke app oach owa ds low-emission ehicles.
De ailed Po olio Analysis
Figu e 3p esen s in o ma ion o he ou di e en emis-
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 765
sion policies. In he scena io o no emission sys em, which
can be conside ed a e e ence poin o in e p e ing he o he
h ee policies, he e a e no penal ies o es ic ions on excess
lee emissions. In his scena io, only ehicle p ojec s ha
con ibu e posi i ely o he ne p esen alue o he po olio
a e ini ia ed. F om his scena io, i is e iden ha all ypes
o ICEV ehicles in each yea ha e a posi i e e ec on he
p o i ma gin. Howe e , o he small-sized PHEV, FCEV, and
BEV ehicles, he p o i ma gin is consis en ly nega i e, so
he e is no incen i e o manu ac u e s o p oduce hese e-
hicle p ojec s. Fo medium and la ge-sized ehicles in hese
powe ain echnologies, as p oduc ion cos s a e assumed
o dec ease due o echnological de elopmen , medium and
la ge-sized PHEV p ojec s a e ini ia ed a e 2029, medium-
sized FCEV p ojec s a e 2031, la ge-sized FCEV p ojec s a -
e 2029, and all medium and la ge-sized BEV p ojec s a e
ini ia ed due o hei posi i e p o i ma gins.
A e analyzing he base scena io wi h no impac om
emission policies, h ee di e en majo policies can be ana-
lyzed. Figu e 3shows ha se e al ehicle ypes wi h nega-
i e p o i ma gins a e ini ia ed in o de o balance penal y
cos s o mee he cons ain s on excess lee emissions. The
Dual C edi Policy esul s in he lowes numbe , wi h abou
70 ini ia ed low-emission ehicle p ojec s du ing he en-yea
planning pe iod. I is ollowed by 76 ehicle p ojec s in he
Supe C edi Sys em, while he US C edi Sys em leads wi h
he highes numbe , wi h abou 87 ini ia ed p ojec s. ICEV
ehicles domina e he po olio, bu in he US C edi Sys em,
due o s ic emission s anda ds a e 2029, small-sized ICEV
ehicles a e e mina ed because o lowe p o i ma gins com-
pa ed o medium and la ge-sized ICEV ehicles. Fo PHEV
ypes, in he US C edi Sys em, se e al small-sized PHEV
p ojec s a e ini ia ed o mee emission cons ain s. How-
e e , in he Supe C edi Sys em and Dual C edi Sys em,
no small-sized PHEV p ojec s a e ini ia ed. Fu he mo e, a
he end o 2035, in he Supe C edi Sys em and US Emis-
sion Sys em, he e is a sudden d op in he numbe o la ge-
sized PHEV p ojec s, likely due o he su icien p oduc ion o
ze o-emission ehicles. Fo hese p ojec s, he p o i ma gin
dec eases, po en ially alling below ha o ze o-emission e-
hicle ypes due o inc eased p oduc ion cos s. Fo FCEV and
BEV ypes o ehicles, medium and la ge-sized e sions a e
ini ia ed o balance he high CO2 emissions om ICEV ehi-
cles, and small-sized e sions a e ini ia ed as a las eso . In
compa ison, BEV p ojec s a e mo e a o able o manu ac-
u e s due o hei highe p o i ma gins.
Rega ding p oduc ion quan i ies, i is e iden ha manu-
ac u e s end o align wi h ma ke demand and p oduce as
many ICEV ehicles as possible. Compa ing he esul s o he
base model wi h no emission policy, i can be obse ed ha
all h ee emission policies would educe he p oduc ion quan-
i y o ICEV ehicles, wi h he US Emission Policy leading o
he la ges educ ion. In he Supe C edi Sys em, he e is a
sudden d op in he p oduc ion o ICEV ehicles a yea 2035.
This d op is a esul o he lee emission h eshold dec easing
om 60 g/km o 45 g/km, causing manu ac u e s o educe
p oduc ion o mee he s ic e s anda ds. Fo he Dual C edi
Policy, he ini ial p oduc ion quan i y in 2025 is lowe due o
lowe demand o low-emission ehicles and he lowe uel
e iciency o ICEV ehicles. Howe e , in he ollowing yea s,
p oduc ion quan i ies inc ease since he ICEV ehicle become
mo e uel e icien hus u ns ou o become mo e a o able
in he dual c edi sco e sys em.
Flee Emission and NPV Analysis
The lee emissions and objec i e unc ion composi ion
o he ou di e en policy scena ios a e depic ed in Fig-
u e 4. The ed line ep esen s he EU- egula ed h eshold
o a e age CO2 lee emissions and se es as a e e ence o
assess he impac o di e en emission policies. In he base
model, shown by he black line, he e is some dec ease in
lee emissions due o echnology de elopmen , bu he a -
e age CO2 lee emissions consis en ly emain abo e he EU
h eshold meaning some egula ion need o be pe o med as
an ex e nal o ce o con ol he lee ing emission. The h ee
di e en emission policies all ha e some e ec on educing
lee emissions in each yea . In he Supe C edi Sys em (blue
line), lee emissions ollow he EU- egula ed h eshold wi h
some elaxa ion in ce ain pe iods. In he Dual C edi Sys em
(yellow line), he end is simila o he line wi h no emis-
sion policy bu wi h smalle absolu e lee emissions. How-
e e , lee emissions a e s ill abo e he EU- egula ed h esh-
old. Las ly, in he US Emission Sys em (g een line), lee
emissions s ic ly ollow he US emission h eshold, which
becomes mo e s ingen a e 2028.
In e ms o ne p esen alue, he base scena io yields he
highes numbe . In he Dual C edi Policy, he NPV is also
high because manu ac u e s can ea n money o p oducing
low-emission ehicles. Unde conse a i e demand, man-
u ac u e s would decide o p oduce mo e low-emission e-
hicles o ea n hese new ehicle c edi s ha could be ade
o ea n some money. In he Supe C edi Sys em, a mino
amoun o penal y cos is incu ed, esul ing in a 12% de-
c ease in he ne p esen alue compa ed o he base model.
The US Emission Sys em yields he lowes ne p esen alue,
as a ge s mus be s ic ly me , leading o he p oduc ion o
se e al low-p o i -ma gin ehicle ypes and a 25% dec ease
in he objec i e alue.
6.1.2. Inno a i e Demand
In he Inno a i e demand scena io, he ma ke is mo e
ecep i e o low-emission ehicles, wi h a highe g ow h a e
in he low-emission ehicle ma ke .
De ailed Po olio Analysis
In his demand scena io, he ma ke in oduc ion o new
ehicle p ojec is simila bu wi h some mino di e ences,
as desc ibed in Figu e 5. In he baseline si ua ion, medium-
sized FCEV p ojec s in 2030 would also be ini ia ed due o
highe demand, and he la ge-sized BEV ehicle p ojec in
2025 would no be s a ed o p io i ize he p oduc ion o
ICEV ypes. Compa ed o he conse a i e demand scena io,
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780766
Figu e 3: Op imal Po olio o Di e en Policy in Conse a i e Demand
o all h ee policy scena ios, manu ac u e s would ini ial-
ize a lowe quan i y o low-emission ehicle ypes, p io i iz-
ing hose wi h la ge p o i ma gins o balance penal ies and
e enues. Fo example, small-sized PHEV ehicles would no
be ini ialized in he US emission sys em. Simila ly, he small-
sized FCEV p ojec would no s a in he Supe C edi sys em
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 767
(a) Flee Emission
(b) NPV S uc u e
Figu e 4: Policy Compa ison in Conse a i e Demand
and would ha e ewe yea s in he Dual C edi Sys em and US
emission sys em. The same end also applies o he small-
sized BEV ehicle p ojec s, wi h ewe p ojec s s a ed a e
2029 in he Supe C edi Sys em due o a la ge demand o
o he , mo e p o i able low o ze o-emission ehicle p ojec s
ha wo k o balance he CO2emissions. Compa ing he h ee
policies, he Dual C edi policy is much less sensi i e o he
demand scena io o ehicle p ojec ini ializa ion because o
i s c edi sys em policy. The ini ialized ehicle p ojec s in he
op imal p ojec po olios o he inno a e demand a e sim-
ila o he p ojec s o he conse a i e demand since he e
is no h eshold bu mone a y incen i es o manu ac u e s.
Fo he Supe C edi Sys em and US Emission Sys em, p o-
duce s choose he ehicle ype wi h he lowes emissions and
he highes p o i ma gin o balance he ex a emissions om
ICEV ypes and a oid penal y cos s. Once he h eshold is
me , he e is no incen i e o manu ac u e s o p oduce ad-
di ional low-emission ehicles. Howe e , o he Dual C edi
Sys em, i is always p o i able o p oduce mo e low-emission
ehicles because manu ac u e s can ea n mo e money o he
ex a c edi s ea ned.
The p oduc ion quan i y g aph in Figu e 5also shows a
simila end. In he Dual C edi Sys em, he o al p oduc ion
quan i y o FCEV and BEV ehicles consis en ly inc eases as
he p oduc ion o ICEV ypes dec eases. Howe e , o he
Supe C edi Sys em and US Emission Sys em, he o al p o-
duc ion quan i y is lowe due o educed demand o ICEV
ypes o ehicles. Addi ionally, in he inno a i e demand sce-
na io, he composi ion o p oduc ion quan i ies o di e en
powe ain ypes does no change signi ican ly. Howe e , in
he Dual C edi Policy scena io, he p oduc ion quan i y o
ze o-emission ehicles inc eases o ea n mo e money h ough
c edi s.
Flee Emission and NPV Analysis
In he Inno a i e demand scena io, he line plo and ba
plo we e used o analyze lee emissions and NPV alues,
as shown in Figu e 6. The black line ep esen s he base-
line model wi h no emission policy. A e 2031, due o in-
c eased ma ke demand o low emission ehicles, lee emis-
sions na u ally all below he EU egula ed h eshold. Fo
he Dual C edi Policy sys em (yellow line), lee emissions
a e much lowe , eaching hei lowes poin a e 2032. In
he Supe C edi Sys em (blue line), emissions ollow he EU
egula ed h eshold un il 2031, a e which hey d op u he
due o ma ke demand. The US Emission Sys em (g een line)
shows a simila end, bu a e 2032, he emission h eshold
is lowe han he EU egula ed h eshold.
In he NPV s uc u e g aph, i can be obse ed ha in
he Inno a i e demand scena io, compa ed wi h he conse -
a i e demand si ua ion, he di e ences in objec i e alues
be ween di e en policies a e smalle . The Dual C edi Pol-
icy has a highe NPV alue, abou 4% mo e compa ed o he
baseline model, due o he ex a c edi s ea ned. Fo he Su-
pe C edi Policy and US C edi Policy, he objec i e unc ion
alues a e simila , bo h abou 6% lowe . The majo educ-
ion occu s be o e 2031, as a e his yea , he ma ke i sel
becomes mo e a o able owa d low emission ehicles, and
he egula ions ha e less o no e ec on es ic ing manu ac-
u e s om p oducing mo e low emission ehicle ypes.
6.2. Sensi i i y Analysis
In he con ex o sensi i i y analysis o he policy ac o s,
his pape selec s up o h ee key ac o s o each emission
policy. These ac o s a e chosen based on hei p esumed sig-
ni icance on he policy ou comes and hei po en ial o being
eadily adjus ed by go e nmen al au ho i ies. Fo each ac o
analyzed, his s udy employs s acked ba cha s o compa e
he Ne P esen Value (NPV) s uc u e and he p oduc ion
quan i ies o di e en ehicle ypes. Addi ionally, i includes
objec i e alues and he pe cen age o low emission ehicles
in he o al p oduc ion as ep esen ed in he line on he ba
cha . The pa ame e s a e ca ego ized in o h ee le els wi h
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780768
Figu e 5: Op imal Po olio o Di e en Policy in Inno a i e Demand
same in e als: high, basic, and low. The a ionale behind
his ca ego iza ion is wo old: i s , o educe he expe imen-
a ion p ocess ime, as each ins ance ypically akes a ound
i e minu es o yield esul s, and second, he ac o s would
no in luence he ends. Fu he de ails ega ding he pa-
ame e adjus men ange can be ound in Sec ion 5.3.
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 769
(a) Flee Emission
(b) NPV S uc u e
Figu e 6: Policy Compa ison in Inno a i e Demand
6.2.1. Supe C edi Sys em
Unde he Supe C edi Sys em, his analysis ocuses on
h ee c ucial ac o s: he egula ed pe cen age o low emis-
sion ehicles, he maximum allowable elaxa ion pe cen age,
and he PHEV mul iplie . The egula ed low emission e-
hicle pe cen age ep esen s he minimum p opo ion o low
emission ehicles ( hose emi ing less han 50 g/km o CO2)
ha mus be me be o e elaxa ion o he emission h esh-
old is pe mi ed. The maximum allowed elaxa ion pe cen -
age se s he uppe limi o h eshold elaxa ion. Finally, he
PHEV mul iplie de e mines whe he PHEV- ype ehicles e-
cei e a mul iplie e ec , meaning ha when hei CO2 emis-
sions each 50 g/km, hey a e coun ed as app oxima ely 0.3
o a p oduc ion uni ins ead o 0. The ma hema ical o mu-
la ion o hese ac o s can be ound in equa ions 19.
Regula ed EV Pe cen age
In he objec i e alue diag am, I obse e ha as he e-
qui ed Regula ed Low Emission Vehicle Pe cen age inc eases,
he objec i e alue is sligh ly a ec ed, esul ing in a dec ease
in he inal NPV alue. This end is simila o bo h con-
se a i e and inno a i e demand scena ios, as shown in he
Objec i e Value g aph in Figu e 7. Howe e , he di e ences
a e smalle in he inno a i e demand scena io. The p ima y
ac o d i ing his cos di e ence is he penal y cos . As he
equi ed pe cen age inc eases, i becomes mo e challenging
o manu ac u e s o achie e he goal pe cen age needed o
bene i om he Supe C edi Policy. This esul s in a highe
penal y cos , which nega i ely impac s he NPV alue.
In he p oduc ion po olio o di e en ehicle ypes, I
obse e a signi ican di e ence in he pe cen age o low emis-
sion ehicles p oduced as he h eshold pe cen age is ad-
jus ed. When he h eshold pe cen age is inc eased by abou
10%, he pe cen age o low emission ehicles p oduced in-
c eases om 32.3% o 34.2%, ep esen ing a 2% inc ease.
This change is mainly d i en by he inc ease in he p oduc-
ion olume o PHEV ehicles. On he o he hand, when he
h eshold pe cen age is dec eased, he e is a sligh educ ion
in he p oduc ion o low emission ehicles, bu his educ ion
is only abou 0.4%, which is ela i ely small compa ed o he
impac o inc easing he h eshold. Fu he mo e, in he in-
no a i e demand scena io, he p oduc ion po olio shows
less a ia ion and emains a a le el o abou 40%. This pe -
cen age le el is highe han he pe cen age assumed o he
conse a i e demand scena io and indica es ha in he inno-
a i e demand scena io, a la ge p opo ion o low emission
ehicles is p oduced ega dless o he h eshold pe cen age.
Maximum Allowed Relaxed Th eshold
When I analyze he change in he maximum elaxed
h eshold allowed in he Supe C edi Sys em, I obse e ha
as he allowed pe cen age inc eases, he objec i e alue also
inc eases. The p ima y di e ence is mos no iceable in he
penal y cos s. This end is mo e e iden in he conse a-
i e demand scena io, as in he inno a i e demand scena io,
manu ac u e s ha e a s onge incen i e o p oduce low
emission ehicles. In conclusion, he elaxa ion o Supe
C edi h esholds has a smalle impac on he p oduc ion
po olio in he inno a i e demand scena io.
When examining he pe cen age o low emission ehicles
p oduced in bo h inno a i e and conse a i e demand sce-
na ios, I obse e a s able end wi h some sligh di e ences.
A allowed pe cen ages o abou 1% and 9%, he pe cen ages
a e simila , likely due o changes in PHEV p oduc ion. How-
e e , a highe o lowe allowed pe cen ages, manu ac u e s
end o p oduce mo e PHEV ehicles o inc ease hei p o i s
in conse a i e demand scena ios, while hey p oduce ewe
PHEV ehicles in inno a i e demand scena ios. The pe cen -
age o low emission ehicles is mo e s able in inno a i e de-
mand, wi h only a 0.2% change compa ed o abou 1% in
conse a i e demand.
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780770
(a) Composi ion o he Objec i e Value (b) Composi ion o he Vehicle Po olio
Figu e 7: Analysis o Change o Regula ed EV Pe cen age
(a) Composi ion o he Objec i e Value (b) Composi ion o he Vehicle Po olio
Figu e 8: Analysis o Change o Maximum Relaxa ion Pe cen age
PHEV Mul iplie
In he implemen a ion o he supe c edi policy, he in-
oduc ion o he PHEV mul iplie was in ended o incen i ize
manu ac u e s o p oduce mo e PHEV ehicles. Howe e , he
esul s o he sensi i i y analysis sugges ha he PHEV mul-
iplie may no signi ican ly in luence he beha io o manu-
ac u e s, possibly due o he ela i ely low ma ke demand
o PHEV ehicle ypes. The analysis shows ha he e a e no
signi ican di e ences in bo h he NPV g aph and he ehicle
po olio g aph. The composi ion and absolu e alues emain
la gely unchanged, wi h he conse a i e demand scena io
consis en ly ha ing a highe NPV objec i e alue and abou
10% ewe low emission ehicles p oduced.
6.2.2. Dual C edi Sys em
In he Dual C edi Sys em, calcula ing dual c edi s in-
ol es complexi y, and his pape simpli ies ce ain ac o s
by using a e age alues. The sys em comp ises h ee main
pa s, wi h one c ucial ac o selec ed om each pa . These
pa s encompass he s anda d uel consump ion in calcula -
ing he CAFC sco e o adi ional ICEVs, he c edi exchange
p ice, and he NEV weigh ac o in calcula ing he NEV sco e
o low-emission ehicles (PHEV, FCEV, BEV). To analyze he
impac o hese ac o s, adjus men s o app oxima ely 50%
compa ed o he cu en assump ions we e made.
S anda d Fuel Consump ion (CAFC)
The change in he s anda d uel consump ion c i e ia has
a no able impac on he manu ac u e ’s objec i e alue. Wi h
s ic e es ic ions, he objec i e alue dec eases because he
manu ac u e ’s abili y o ea n dual c edi s as ex a p o i di-
minishes. Con e sely, as he s anda d uel consump ion in-
dex becomes mo e elaxed, he objec i e alue inc eases.
Speci ically, a 50% dec ease in he index leads o a 27% de-
c ease in he objec i e alue in a conse a i e demand sce-
na io and an 18% dec ease in an inno a i e demand sce-
na io. Con e sely, when he index becomes mo e elaxed,
he objec i e alue inc eases by app oxima ely 16% in bo h
demand scena ios.
Rega ding he pe cen age o di e en ehicle ypes p o-
duced, in a conse a i e demand scena io, a s ic e S an-
da d Fuel Consump ion index leads o a signi ican inc ease
in he pe cen age o low emission ehicles p oduced, app ox-
ima ely 6%. Con e sely, a mo e elaxed index esul s in a
mino dec ease in he pe cen age o low emission ehicles
p oduced, abou 0.4%. In an inno a i e demand scena io,
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 771
(a) Composi ion o he Objec i e Value (b) Composi ion o he Vehicle Po olio
Figu e 9: Analysis o Change o PHEV Mu iplie
(a) Composi ion o he Objec i e Value (b) Composi ion o he Vehicle Po olio
Figu e 10: Analysis o Change o CAFC S anda d Fuel Consump ion
a s ic e index leads o a smalle inc ease o a ound 1% in
he p oduc ion o low emission ehicles, compa ed o a con-
se a i e demand scena io. A mo e elaxed index in he in-
no a i e demand scena io also esul s in a modes inc ease
o app oxima ely 0.6% in he pe cen age o low emission e-
hicles p oduced, along wi h an inc ease in o al p oduc ion
olume o ea n mo e dual c edi .
Exchange P ice
The change in he exchange p ice o he dual c edi pol-
icy a ec s bo h he objec i e alue and he p oduc ion po -
olio. When he exchange p ice inc eases, he objec i e alue
o he manu ac u e also inc eases, bu he ex en o he in-
c ease is smalle compa ed o changes in he S anda d Fuel
Consump ion index in CAFC c edi calcula ion. This end
and alue o inc emen a e simila in bo h conse a i e and
inno a i e demand scena ios, wi h abou a 50% inc ease in
he p ice esul ing in abou a 2% o 5% inc ease in NPV.
Rega ding he p oduc ion po olio, as he p ice inc eases,
manu ac u e s end o p oduce mo e low emission ehicles o
ea n he dual c edi alue. The demand scena io does no sig-
ni ican ly a ec he end, and he pe cen age inc ease ends
o ollow a loga i hmic pa e n a he han a linea one. Wi h
a la ge p ice, he e is a lowe inc ease a e in he p oduc ion
o low emission ehicles.
NEV Weigh ed Fac o (NEV)
The NEV weigh ac o index is a c i ical ac o in de-
e mining he NEV sco e in he dual c edi policy sys em.
This ac o de e mines how much low emission ehicles a e
coun ed in calcula ing he NEV sco e. A highe NEV weigh
ac o index sco e gi es low emission ehicles a highe sco e
in he NEV sco e calcula ion, which can help manu ac u e s
ea n mo e alue. As he NEV weigh ac o index inc eases,
he objec i e alue also inc eases. This end is consis en in
bo h demand scena ios and inc eases linea ly by abou 4.
Rega ding he pe cen age o he p oduc ion po olio, an
inc ease in he NEV weigh ac o index mo i a es ehicle
manu ac u e s o p oduce mo e low emission ehicles in bo h
demand scena ios. The inc ease is also linea , wi h a sligh ly
lowe a e o inc ease in he inno a i e demand scena io, bu
i esul s in abou a 1% inc ease in he absolu e alue in bo h
scena ios. The inc ease in low emission ehicle p oduc ion is
mo e p ominen o FCEV ehicles since FCEV has he highes
NEV weigh ac o compa ed o o he ehicle ypes.
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780778
(a) Main E ec Plo (b) In e ac ion Plo
Figu e 22: Fac o analysis in US C edi Sys em o NPV alue
(a) Main E ec Plo (b) In e ac ion Plo
Figu e 23: Fac o analysis in US C edi Sys em o EV pe cen age
7. Conclusion and Ou look
This a icle employs a ma hema ical model, p ima ily a
mixed-in ege linea model, o cha ac e ize ca manu ac u -
e s’ p oduc ion po olios unde di e en emission policies.
The aim is o gain a mo e quan i a i e unde s anding o a -
ious policy sys ems and hei e ec i eness. By in eg a ing
ac ual da ase s wi h di e en demand scena ios and policy
pa ame e s, he s udy simula es he impac o hese policies,
assuming ha manu ac u e s s i e o maximize hei p o i s.
The esul s indica e ha all h ee policy sys ems in Eu-
ope, China, and he Uni ed S a es con ibu e o inc eased
p oduc ion o low-emission ehicles compa ed o he base
model wi h no policy in place. Rega ding he ini ializa ion
o low-emission ehicles, he US emission policy leads o
he mos signi ican inc ease in he numbe o ehicle ini ia-
ions, while he Supe C edi Policy in Eu ope and he Dual
C edi Policy pe o m simila ly in his ega d. Howe e , om
a inancial pe spec i e, he Dual C edi Policy pe o ms he
bes in p ese ing he ca manu ac u e ’s p o i , while he US
emission policy has he mos de imen al e ec on he manu-
ac u e ’s p o i . Fo he a e age lee emissions, bo h he Su-
pe C edi Policy and he US emission policy e ec i ely ack
he end se by he egula ed emission h eshold. Howe e ,
he Dual C edi Policy does no ha e a ixed h eshold bu
a he ollows emissions acco ding o ma ke ends. As he
ma ke inc easingly a o s low-emission ehicles, esul ing
in a shi owa ds an inno a i e demand scena io whe e con-
sume s p e e such ehicles, he lee emissions a e lowe in
esponse o his end.
Fu he mo e, my expe imen s show ha , i is e iden ha
o he Supe C edi Policy and he US Emission Policy, he
demand le el has a signi ican impac on he p o i o he ca
manu ac u e and he pe cen age o low-emission ehicles
p oduced. In con as , he Dual C edi Policy exhibi s no able
di e ences, whe e he demand le el appea s o in luence p i-
ma ily he pe cen age o low-emission ehicles p oduced bu
no he objec i e alue.
Speak o he speci ic pa ame e ac o s, he Supe C edi
Z. Shi /Junio Managemen Science 10(3) (2025) 748-780 779
Sys em does no exe a subs an ial in luence on ei he o he
indica o s, namely NPV (Ne P esen Value) and EV (Elec-
ic Vehicle) pe cen age. Howe e , o he Dual C edi Policy
and he US Emission Policy, hese ac o s exhibi a ela i ely
highe e ec on bo h indica o s, wi h he CAFC index being
he mos signi ican ac o in he Dual C edi Policy. No ably,
he ma ke exchange p ice o he Dual C edi only appea s o
impac he ne p esen alue and no he pe cen age o low-
emission ehicle p oduc ion. In he con ex o he US emis-
sion policy, he only ac o , which is he ca bon lee emission
h eshold, plays a signi ican ole in de e mining he manu-
ac u e ’s p o i and he policy’s e ec i eness.
The e a e se e al limi a ions o his s udy. Fi s ly, ega d-
ing he model sol ing me hod, he Supe C edi Sys em pol-
icy is no o mula ed as a linea model and canno be eadily
ans o med in o a linea o m o op imiza ion. Ins ead, a
heu is ic me hod was employed o sol e his model in wo
s eps. While his app oach may no gua an ee an op imal
solu ion, howe e , my expe imen s showed ha he heu is-
ic gi es good quali y solu ions ha esul ing de ia ions a e
unlikely o ha e a signi ican impac .
Secondly, in his s udy, ma ke demand is based on sim-
pli ied assump ions and is no speci ic o indi idual ehicle
models. The o al demand amoun is assumed o be cons an
o each yea in he planning pe iod, and ehicle sales quan-
i ies may a y due o consume p e e ences. Addi ionally,
he ehicle ype segmen a ion used in his s udy is ela i ely
b oad, ca ego izing ehicles based on powe ain echnology,
size, yea , and powe ange. In eali y, he e is a much la ge
a ie y o ehicle models. To add ess his limi a ion, a mo e
accu a e demand p edic ion model could be in eg a ed in o
he analysis o enhance esul accu acy.
Thi dly, some o he policy-speci ic pa ame e s a e based
on assump ions. Fo ins ance, in he Dual C edi Policy sys-
em, ce ain pa ame e s such as uel consump ion s anda d in
CAFC sco e calcula ion o he weigh ed ac o o ac ual NEV
c edi calcula ion a e calcula ed based on de ailed desc ip-
ions o speci ic ehicle models and a e no included in he
da ase due o p i acy conce ns. Ob aining a mo e comp e-
hensi e da ase may equi e close collabo a ion wi h ehicle
manu ac u e s.
A las , in he eal ma ke scena io, he coope a ion be-
ween he di e en ehicle manu ac u e should also been
conside ed. In his s udy, i is assumed ha only one ehicle
manu ac u e exis s and he e would be any ma ke ading
ac i i y occu s. These ac i i ies is ha d o be modelled bu i
necessa y could be added o his model o a mo e accu a e
esul .
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