Unlocking ci cula economy policies in in eg a ed assessmen models
Le icia Magala
a,b,c,*
, Elena Ve dolini
d,a,b
, Alexand e Szklo
c
a
RFF-CMCC, Eu opean Ins i u e on Economics and he En i onmen , c/o BASE Via Be gognone 34, Milano 20144, I aly
b
CMCC, Eu o-Medi e anean Cen e on Clima e Change, c/o BASE Via Be gognone 34, Milano 20144, I aly
c
Ene gy Planning P og am (PPE), Uni e sidade Fede al do Rio de Janei o (UFRJ), Cen o de Tecnologia, Bloco C, Sala 211, Cidade Uni e si ´a ia, Ilha do Fundao, Rio
de Janei o 21941-914, B azil
d
Depa men o Law, Uni e si y o B escia, Via S. Faus ino 41, B escia 25121, I aly
ARTICLE INFO
Keywo ds:
Ci cula economy (CE)
In eg a ed assessmen models (IAM)
ci cula economy policies
clima e change mi iga ion
ABSTRACT
The ci cula economy (CE) has eme ged as a p omising s a egy o simul aneously add ess clima e change and
he o e -exploi a ion o Ea h’s esou ces. Ye , mos In eg a ed Assessmen Models (IAMs) lack he capaci y o
ully assess he po en ial bene i s and d awbacks o CE policies in he con ex o clima e change. This pape
p o ides a s uc u ed app oach o imp o e he ep esen a ion o CE in IAMs and guides hei applica ion in
clima e policy assessmen s. To his end, we i s p opose a amewo k o o ganize he mul iple laye s and policy
dimensions in ol ed in CE. We hen e iew he cu en s a e o CE modeling in IAMs and iden i y c i ical gaps,
including limi ed a en ion o policy mechanisms, lack o ma e ial-le el g anula i y, and insu icien co e age o
downs eam and ups eam supply chain sec o s. Las ly, we iden i y p io i y a eas o imp o emen , such as
coupling IAMs wi h ma e ial low and sec o al models, e ining da a and s uc u al assump ions, and de eloping
mo e cohe en CE policy na a i es. Toge he , hese s eps es ablish pa hways o he scien i ic communi y o
be e in eg a e CE in o IAMs and s eng hen unde s anding o i s ole in global clima e mi iga ion s a egies.
1. In oduc ion
The ci cula economy (CE) has eme ged as a p omising s a egy o
join ly ackle issues o clima e change and he o e -exploi a ion o
Ea h’s esou ces (Can zle e al., 2020), wo in e connec ed key chal-
lenges ha ou socie ies a e aced wi h. In eg a ed in o he b oade
agenda o sus ainable de elopmen , CE policies aim o educe pollu ion
and esou ce o e exploi a ion while inc easing p oduc s’ alue wi hin
he economy (Ghisellini e al., 2016). By p omo ing a ange o s a egies
– such as educe, euse, ecycle, and eco e – CE impac s supply chains
in a a ie y o ways and o e s subs an ial po en ial o educing GHG
emissions (Ma e ial Economics 2018). Howe e , i may also gene a e
unin ended e ec s ha could unde mine deca boniza ion e o s.
In eg a ed Assessmen Models (IAMs) - which in o m and shape
clima e policy (IPCC 2023) - a e c ucial ools o explo e he in e sec ion
o CE and clima e change. These models p esen e y he e ogeneous
amewo ks, using di e se me hodologies o explo e low-ca bon pa h-
ways compa ible wi h clima e a ge s, such as hose ou lined in he Pa is
Ag eemen (K ey 2014; Nikas e al. 2019; Weyan 2017). De ailed p o-
cess (DP) IAMs, which o e mo e g anula p ojec ions o clima e mi i-
ga ion s a egies ac oss di e en echnologies, egions, and sec o s
(Weyan 2017; Bose i 2021), ha e only ecen ly begun inco po a ing
CE s a egies (F agkos 2022; S egmann e al. 2022). S ill, impo an
challenges mus be o e come o ensu e e ec i e e alua ion o CE
policies.
Fi s , assessing he bene i s o CE wi hin he clima e con ex lies in
he undamen al complexi y and mul idimensional na u e o CE i sel .
The concep o ci cula economy is no ably complex, and no unique
de ini ion is es ablished in he li e a u e (Deu z 2024; Ki chhe e al.
2023). In his pape , ci cula economy e e s o he implemen a ion o a
se o p ac ices ha lowe s he quan i y o p ima y ma e ial esou ces
necessa y a any poin in ime o achie e a ce ain le el o se ices in he
economy. These p ac ices a e labelled “ci cula economy s a egies” and
a e classi ied acco ding o Po ing e al. (2017) using he 10R hie a -
chical amewo k as ollows: Re use (R0), Re hink (R1), Reduce (R2),
Reuse (R3), Repai (R4), Re u bish (R5), Remanu ac u e (R6), Repu -
pose (R7), Recycle (R8), and Reco e (R9). Impo an ly, he 10Rs can be
g ouped in o h ee main CE objec i es each ela ing o loops— he p o-
cess o cycling ma e ials wi hin he economy. These objec i es a e: (i)
Reducing p ima y ma e ials’ demand o na ow he loop; (ii) Inc easing
p oduc use phase o slow he loop; and (iii) ein oducing ma e ials
back o he economy o close he loop (Bocken e al. 2016).
* Co esponding au ho .
E-mail add esses: [email p o ec ed] (L. Magala ), [email p o ec ed] (E. Ve dolini), [email p o ec ed] (A. Szklo).
Con en s lis s a ailable a ScienceDi ec
Resou ces, Conse a ion & Recycling
jou nal homepage: www.sciencedi ec .com/jou nal/ esou ces-conse a ion-and- ecycling
h ps://doi.o g/10.1016/j. escon ec.2025.108614
Recei ed 18 No embe 2024; Recei ed in e ised o m 22 Sep embe 2025; Accep ed 29 Sep embe 2025
Resou ces, Conse a ion & Recycling 225 (2026) 108614
0921-3449/© 2025 The Au ho s. Published by Else ie B.V. This is an open access a icle unde he CC BY license ( h p://c ea i ecommons.o g/licenses/by/4.0/ ).
Second, unin ended e ec s – such as hose linked o nega i e spill-
o e s, ebound e ec s, ade-o s and eedback loops - may a ise om
he implemen a ion o CE s a egies, po en ially hinde ing he join
achie emen o mi iga ion and ci cula i y objec i es
(Aguila -He nandez e al. 2021; Fi ch-Roy e al. 2021; Rizos e al. 2019).
Such consequences a e go e ned by dis inc mechanisms, many o which
emain insu icien ly unde s ood - making i ha d o IAMs o cap u e all
o hem (Jol eau e al., 2025). Fo ins ance, ecycling may inad e en ly
cause nega i e spillo e s, such as bu den shi ing, by eloca ing ca bon
emissions o o he s ages o he p oduc li e cycle, po en ially o se ing
he in ended en i onmen al bene i s. Also, possible ebound e ec s a e
usually no accoun ed o as, o example, lowe p oduc ion cos s om
ecycled ma e ials o ma e ial e iciency measu es can s imula e highe
consump ion o he same p oduc (di ec ebound) (Cas o e al. 2022;
Me ic and Pigosso 2022; Zink and Geye 2017). In addi ion, inc easing
ecycling can also boos economic ac i i y and lead o mo e ene gy
consump ion and anspo a ion, which can ul ima ely inc ease local
CO₂ emissions (Boonman 2023), as well as, sa ings gene a ed om
ci cula p ac ices (e.g., buying second-hand p oduc s) may be edi ec ed
o o he goods and se ices wi h high en i onmen al impac s, such as ai
a el (indi ec ebound) (Cas o e al. 2022). Mo eo e , la ge-scale
ecycling policies can educe ma e ial cos s ac oss mul iple indus ies,
s imula ing economic g ow h and p oduc ion in o he sec o s, which
inc eases o e all esou ce use and emissions a he mac oeconomic le el
(economy-wide ebound) (Fe an e e al. 2024; Zink and Geye 2017).
T ade-o s may eme ge when ci cula s a egies compe e wi h clima e
mi iga ion goals. Fo ins ance, in cases whe e ecycling ce ain ma e ials
educes hei po en ial o s o e ca bon du ing hei (longe ) use phase, a
s a egy ha could help a oid ca bon emission o e shoo ing (de Oli-
ei a e al. 2021). Along simila lines, ex ending appliance li e imes
educes ma e ial demand, bu keeping less e icien p oduc s in use may
inc ease ene gy consump ion and emissions (Zink and Geye 2017).
Finally, eedback loops a e c ucial o unde s anding he dynamic
beha io o CE s a egies (i.e., ci cula cause-and-e ec ela ionships
whe e changes in one subsys em igge cascading e ec s in o he s,
which can ei he ampli y o coun e ac he o iginal CE s a egy). Fo
example, a balancing loop may happen when inc easing p oduc euse
lowe s demand o new p oduc s, educing p oduc ion and esou ce
ex ac ion, which dec eases en i onmen al p essu es and, o e ime,
diminishes he u gency and oppo uni ies o addi ional euse, g adually
s abilizing he sys em (Bassi e al. 2021).The e o e, e o s o p omo e
he up ake o CE s a egies by economic agen s equi e accompanying
policies ha add ess he nega i e consequences and ex e nali ies asso-
cia ed wi h esou ce ex ac ion, use and disposal.
Thi d, he ep esen a ion o mul iple policies is no s aigh o wa d
(Pe˜
nasco e al. 2021), pa icula ly i applied a mul iple scales along
supply chains, while accoun ing o economic and biophysical limi s,
and espec ing he modynamics law. These policies – o en implemen ed
as pa o po olios - p o ide economic and egula o y incen i es o
na ow, slow and close he loop, and boos in es men and inno a ion o
o e come echnical, economic, ins i u ional, and cul u al challenges
(G a s ¨
om and Aasma 2021). Ye , mos IAMs ely on a simpli ied policy
ep esen a ion: changing p ices o ep esen axes and subsidies, o
limi ing model pa ame e s (Roel sema e al. 2020).
Se e al key s udies on ma e ial analysis laid he g oundwo k o
e iewing, unde s anding and imp o ing models’ ep esen a ions o CE
policies in IAMs’ low ca bon scena ios (Aguila -He nandez e al. 2021;
Kullmann e al. 2021; McCa hy 2018; Nikas e al. 2022; Pauliuk e al.
2017). They sugges ha IAMs alone a e unable o ully cap u e he
b oad spec um o CE policies and hei in e ac ions (Nikas e al. 2022).
Impo an ly, CE measu es a e in ima ely connec ed o he use and
managemen o ma e ials; hey hus equi e a ull unde s anding o he
ma e ial-ene gy-clima e nexus — a capabili y o en absen in IAMs
(Pauliuk e al. 2017). Seconda y ma ke s and he possibili y o ep e-
sen ing endogenously he e ec s o ma e ial subs i u ion a he egional
le el a e also a undamen al ea u e which is cu en ly absen in
mac oeconome ic and gene al equilib ium models (CGE) (McCa hy
2018). IAMs ha e also been subjec o b oade c i iques, including he
need o a shi beyond echnology-cen ic iews o emb ace
demand-side measu es and a di e se ep esen a ion o mi iga ion ac-
ions ac oss di e en scales (Pye e al. 2021; Keppo e al. 2021).
While esea ch on CE de elopmen in IAMs has expanded o e he
pas yea s, a c i ical gap emains: he inabili y o ake a sys em-wide
pe spec i e and conside in e linkages be ween CE s a egies. This
means embedding he complexi y o ci cula economy s a egies ac oss
mul iple in e linked s ages o ma e ial ex ac ion, p oduc ion, use and
end-o -li e while also accoun ing o unin ended e ec s ac oss en i-
onmen al, economic, and social subsys ems. Ye , such sys em-wide
pe spec i e is ins umen al o assess he in e dependencies be ween
di e en CE policies, a ge s and cons ain s ac oss he supply chain
(Milios 2018; S eenmans and Lesniewska 2023). Conside , o ins ance,
(i) incen i es o he ecycling sec o , (ii) a pe cen age equi emen o
ecycled plas ic in packaged goods in indus y, and (iii) a policy ha
educa es consume s o educe plas ics consump ion. Policies a ge ing
ecycling and p oduc ion phases migh con ibu e o one ano he : as he
i s inc eases he demand o ecycled plas ics and he second c ea es a
ma ke o ecycled ma e ial in indus y. Howe e , in he opposi e di-
ec ion he policy applied o he consume s can educe he quan i y o
plas ics a ailable o ecycling and, consequen ly, o u he use in in-
dus y. Al e na i ely, in absence o educa ional policies, ecycling and
p oduc ion incen i es esul in highe plas ics consump ion.
This pape add esses his gap by de eloping a s uc u ed amewo k
which de ails he key dimensions o he assessmen o CE s a egies.
Such amewo k is hen used o in es iga e he s a e o he a o CE
ep esen a ion in IAMs and o iden i y he key de elopmen s necessa y
o imp o e CE policies e alua ion in IAMs. To achie e his, we i s
de elop a concep ual amewo k g ounded in key de ini ions and clas-
si ica ions o CE policies, and which inco po a es he p inciple o li e
cycle hinking. This pe spec i e – which is widely adop ed in CE poli-
cymaking, e.g., he EU Ci cula Economy Ac ion Plan (Eu opean Com-
ission, 2020) - accoun s o en i onmen al and esou ce impac s ac oss
all s ages o ma e ials o p oduc s’ li ecycle - om aw ma e ial
ex ac ion h ough p oduc ion, use and end-o -li e managing and
ep ocessing. Second, based on his amewo k we p esen an in-dep h
e iew o how CE s a egies ha e been e alua ed in IAMs. Las ly, we
comp ehensi ely discuss he cu en challenges and po en ial imp o e-
men s needed in IAMs, ocusing on di e en app oaches, no el model
capabili ies and couplings.
The es o he pape is o ganized as ollows. Sec ion 2 de ails he
concep ual amewo k and ou lines he me hodological app oach used
o conduc he e iew. In Sec ion 3 he esul s a e p esen ed, de ailing
he cu en s a e o IAMs in inco po a ing CE policies. Sec ion 4 dis-
cusses po en ial op ions o imp o ing CE policies in models and p o-
ides insigh s in o u u e esea ch and modelling imp o emen s. Sec ion
5concludes.
2. Me hods
The me hodology o his pape is based on wo consecu i e s eps.
Fi s , a concep ual amewo k was de eloped o o ganize he key di-
mensions o he CE and se e as he basis o unde s anding he s a e-o -
he-a o IAMs modelling o CE and o iden i y key a enues o u u e
esea ch (Fig. 1). Second, a li e a u e e iew was conduc ed o examine
how CE policies ha e been ep esen ed in IAMs. Based on his e iew,
and using he amewo k as an analy ical lens, key gaps and limi a ions
in cu en modeling p ac ices we e iden i ied. The subsec ions below
desc ibe each s ep in u he de ail.
2.1. O ganizing he p oblem: A CE amewo k o modele s
The concep ual amewo k de eloped in his s udy d aws om
es ablished concep s in indus ial ecology and clima e and ene gy policy
L. Magala e al.
Resou ces, Conse a ion & Recycling 225 (2026) 108614
2
analysis. I de ails he key elemen s and laye s o p o ide a comp e-
hensi e assessmen o he mi iga ion po en ial o CE policies. I also
highligh s how he di e en elemen s/laye s in e ac wi h each o he in
he con ex o p omo ing ci cula i y. Modele s should hen be awa e o ,
and accoun o , hese in e ac ions.
The amewo k dis inguishes ou key laye s. Fi s , in he e minol-
ogy o Bocken e al. (2016), CE policies di e in e ms o s a egies: hey
can p omo e na owing, slowing o closing s a egies. . Na owing pol-
icies ocus on s a egies o educe ma e ial use, incen i izing companies
o adop se i iza ion o sha ing economy p inciples. This includes
designing p oduc s ha minimize ma e ial and ene gy use, such as
ligh weigh and mul i- unc ional i ems, encou aging consume s o
choose e sa ile o e specialized p oduc s. These policies should also
d i e consume beha io owa ds educed consump ion and p e e ence
o sus ainable op ions. Slowing policies a e p ima ily cen e ed on add-
ing alue o p oduc s o ex end hei economic li espan. This s a egy is
mainly achie ed by imp o ing p oduc du abili y and epai abili y
du ing he p oduc ion phase. I is essen ial o push he de elopmen o
new se ices and ma ke s and encou age consume s o u ilize hese
se ices. Closing policies p omo e he ein eg a ion o ma e ials in o he
economy ollowing hei pos -consume phase. In he p oduc ion phase,
p oduc design policies can enhance he e ec i eness o hese policies by
p io i izing ecyclable ma e ials and easy disassembly. Addi ionally,
policies ha cu ail land ill-bound was e and p omo e he c ea ion o
new ma ke s o pos -consume p oduc s con ibu e o he success o
hese e o s. Eco-pa ks and he p omo ion o indus ial symbiosis
u he se e as signi ican incen i es o closing policies.
Second, independen ly o whe he hey a ge he na owing, he
slowing o he closing o ma e ial loops, policies di e by na u e.
Following he classi ica ion amewo k om Bemelmans-Videc (2017),
as applied in clima e and ene gy policy s udies (e.g., Skilling on e al.
2022), we o ganize CE policies in h ee main ca ego ies. Regula o y
policy ins umen s, encompassing codes, s anda ds, and quo as, es ab-
lish manda o y en i onmen al benchma ks and p ac ices - as is he case
o he EU Single-use Plas ic Di ec i e (Eu opean Commission 2019).
Economic and Financial ins umen s, including axes, use cha ges, sub-
sidies, and ax educ ions such as Sweden’s Reduced VAT on Repai s
(Eu opean Commission 2021), aim o in e nalize en i onmen al ex e -
nally by al e ing he cos s associa ed wi h ma e ial use, nudging he
ma ke owa ds esou ce e iciency and sus ainable inno a ion. Las ly,
Volun a y – o So – policies aim a in luencing he beha io o economic
agen s h ough in o ma ion p o ision, educa ion and awa eness and
beha io al change, as in he case o he EU Ecolabel ce i ying goods and
se ices wi h a educed en i onmen al impac (Eu opean Commission
2024).
Thi d, all policy ins umen s can be applied a di e en and mul iple
scales. We d aw on e minologies used in indus ial ecology and CE
policy ield (e.g., Linde e al., 2017; Pauliuk and Hee en, 2020; Eu o-
pean Comission, 2020) o classi y hese scales as: Ma e ial-le el ini ia-
i es p omo e he euse o ecycling o speci ic componen s, like
elec onic pa s o packaging, aiming o inc ease ecycled con en in
p oduc s like plas ic bags. P oduc -le el policies ocus on he en i e
li ecycle o p oduc s, implemen ing eco-design p inciples o imp o e
du abili y, ecyclabili y, and epa abili y, exempli ied by he F ance
Repa abili y Index (Eu opean En i onmen al Agency 2024) Sec o -
al-le el policies apply o en i e indus ies, ad oca ing o sus ainable
p ac ices, esou ce e iciency, and ci cula business models wi hin sec-
o s such as manu ac u ing and ene gy, as seen in he U.S. Solid Was e
In as uc u e o Recycling G an P og am (US EPA 2022). Sys em-le el
policies adop a holis ic app oach, encou aging closed-loop sys ems and
Ex ended P oduce Responsibili y (EPR) schemes (OECD 2024).
Fou h, gi en he b ead h o CE s a egies, o success ully achie e
mac o-le el ma e ial demand educ ion policies in suppo o CE should
conside he en i e p oduc li e cycle (Jol eau e al., 2025). Fo
ins ance, a policy migh a ge ei he consume s (demand-side policy) o
manu ac u e s and end-o -li e managemen sec o s (supply-side policy).
This di e en ia ion leads o e y di e en impac s on ma e ial demand,
lows, s ocks, and po en ial ebound e ec s. Fo his eason, i is
impo an o unde s and in which s age o he supply chain a gi en
policy ins umen is applied - namely ex ac ion, p oduc ion, con-
sump ion, and end-o -li e managemen . Ex ac ion in ol es he ini ial
p ocu emen o esou ces, p oduc ion co e s he ans o ma ion o p i-
ma y esou ces in o inal p oduc s o se ices, consump ion pe ains o
he end-use o hese p oduc s and se ices, and end-o -li e managemen
encompasses he pos -consump ion p ocesses ela ed o
pos -consump ion ans o ma ion. This ou -phase classi ica ion sum-
ma izes he main s ages ypically used in c adle- o-g a e and
c adle- o-c adle li e cycle hinking and ma e ial low analysis (Cu an
2012).I is impo an o dis inguish be ween li e cycle hinking, which is
a concep ual app oach o assessing en i onmen al impac s ac oss all
s ages o a p oduc ’s li e, and supply chain managemen , which ocuses
on he coo dina ion and ope a ional capaci y ac oss hese s ages. In he
modeling con ex , bo h a e complemen a y: li e cycle hinking ensu es
consis ency in en i onmen al accoun ing and helps p e en bu den
shi ing, while supply chain managemen highligh s he need o ech-
nical and economical cohe ence be ween s ages. Fo example,
end-o -li e p ocessing sys ems conside ed in models migh no be eal-
is ically easible in some egions (e.g. pos -consump ion lows a e no
managed p ope ly o so and ecycling acili ies). Bo h pe spec i es
equi e ha all s ages o he supply chain be cohe en ly ep esen ed and
in e connec ed wi hin he models.
Impo an ly, CE policy ins umen s, when applied a di e en le els,
can be in eg a ed in o a policy po olio wi h a speci ic objec i e, such as
he educ ion o p ima y c i ical me al ex ac ion. As shown in Fig. 1B,
his policy po olio can be implemen ed in a e ical app oach, whe eby
dis inc ins umen s a e employed o achie e one o mo e CE s a egies
wi hin a single s age o he supply chain. In con as , a ho izon al policy
po olio en ails he implemen a ion o ins umen s and, subsequen ly,
CE s a egies ac oss he a ious s ages o he supply chain. In bo h cases,
ins umen s can a ge di e en scales, om ma e ial-le el measu es o
b oade p oduc - o sys em-le el in e en ions by using di e en R
Fig. 1. Concep ual amewo k o CE policy e alua ion in ene gy-
clima e models.
L. Magala e al.
Resou ces, Conse a ion & Recycling 225 (2026) 108614
3
s a egies. Fo he sake o isual cla i y, Fig. 1B ocuses on he s uc u al
logic o policy laye ing and in e ac ion ac oss he supply chain, while
s a egy ypes and scales a e de ailed in Fig. 1A.
2.2. Li e a u e e iew
Using he heo e ical amewo k om he p e ious subsec ion, we
ca y ou a sys ema ic li e a u e e iew o iden i y app oaches used o
model he in e sec ion o clima e mi iga ion and CE in IAMs, as well as
gaps ha emain o be add essed. To his end, we selec a sample o
publica ion as de ailed below, classi y each publica ion depending on
he objec i es o CE policies, he policy mechanisms employed, he CE
s a egies u ilized, he scales o applica ion, he s ages o he supply
chain add essed, and he in e dependence and c oss-sec o ial analysis o
CE s a egies. Ou inal sample includes 31 a icles ha e alua ed he
impac o CE s a egies on clima e mi iga ion wi hin IAMs (Fig. 2),
encompassing 15 dis inc models o a ying complexi y.
The sample selec ion o ou e iew ollows he P e e ed Repo ing
I ems o Sys ema ic Re iews and Me a-Analysis (PRISMA) me hodology
(Page e al. 2021). Fi s , we sea ched he Scopus and Web o Science
da abase using keywo ds ela ed o ’ci cula economy’, ’ene gy-clima e
models’, ’in eg a ed assessmen model*’, ’mac oeconomic models’ as
well as he 10R amewo k. We ocused on pee - e iewed pape s pub-
lished be ween 2014 and 2024, as his pe iod ma ks a shi in IAM
esea ch ollowing he Pa is Ag eemen , wi h inc easing emphasis on
demand-side mi iga ion s a egies and he in eg a ion o ci cula econ-
omy conside a ions (e.g. Can zle e al. (2020); C eu zig e al. (2024)).
Addi ionally, we applied he o wa d snowballing echnique o ensu e
ha ele an a icles we e included (Wohlin 2014). De ails a e p o ided
in Annex A.
Pape s we e eligible o inclusion in he e iew i hey (i) we e
o iginal esea ch a icles (excluding me a-analyses and e iew a icles)
and pee - e iewed, (ii) conside ed CE s a egies ocused on ma e ial and
non-ene gy use eeds ocks and (iii) examined hei impac s on a leas
ene gy use and g eenhouse gas emissions in ene gy-clima e low ca bon
scena ios. The second selec ion c i e ia implies ha we excluded a icles
ocusing solely on he swi ch o ene gy gene a ion echnology om ossil
o enewable ene gy o biomass on supply-side ene gy e iciency mi i-
ga ion op ions, which a e common mi iga ion s a egies. Addi ionally,
we did no conside pape s in which he scena ios esul ing om he
IAMs we e used as inpu s o o he models o ca y ou hei analyses (as
is he case o Pauliuk e al. 2021 and Sacchi e al. 2022).
Gi en he he e ogenei y o IAMs, we classi y pape s based on he
g ouping p oposed in Wiedenho e e al. (2024): Compu able Gene al
Equilib ium (CGE) models, Pa ial Equilib ium models (PE), and Mac-
oeconome ic models and Sys em Dynamics (SD) models. CGE models
o e a de ailed economic ep esen a ion, examining policy impac s
aiming o op imal economic closu e. PE models ocus on he in e ac ion
be ween en i onmen al impac s and speci ic economic sec o s, o en
in eg a ed wi h CGE models o comp ehensi e analysis. Mac o-
econome ic models, dis inc om CGE models, base hei simula ions
on his o ical da a o cap u e dynamic, nonequilib ium economic be-
ha io s, sui able o e alua ing di e se clima e policies. SD models a e
usually based on simula ion o non-linea dynamics be ween ac o s in a
speci ic sys em and hey a e oo ed in causal eedback s uc u es.
The amewo k p esen ed in subSec ion 2.1 was used as he basis o
a s uc u ed analysis o he selec ed pape s, wi h he aim o iden i ying
gaps and hus shaping u u e esea ch p io i ies. This p ocess in ol ed
examining which dimensions o ci cula economy a e unde ep esen ed
o inconsis en ly add essed in exis ing modeling app oaches. F om his,
a se o esea ch di ec ions was o mula ed by assessing whe e im-
p o emen s a e bo h mos needed and mos easible. The sugges ions
we e no de i ed a bi a ily bu eme ged h ough a sys ema ic e alua-
ion o exis ing gaps and a a ge ed sea ch o li e a u e, including om
adjacen ields, ha p oposed me hodological pa hways o policy s a-
egies o add ess hem.
The me hodological app oach desc ibed so a is ce ainly subjec o
some limi a ions. Fi s ly, he pape s selec ed o e iew we e based on a
se o keywo ds ha a e ypically used in he ield o ci cula economy
esea ch, as ci ed in e e ences such as Ki chhe (2023) and Reike e al.
(2018). Howe e , i should be acknowledged ha some models may
ep esen he ci cula economy using di e en names o classi ica ions
ha we e no cap u ed by he esea ch que y. The applica ion o he
snowballing me hod was speci ically in ended o mi iga e his po en ial
limi a ion, he eby ein o cing he obus ness o he esul s in ela ion o
he esea ch objec i es. Mo eo e , he lack o anspa ency ega ding
he speci ics o how pa icula a iables and assump ions a e inco po-
a ed in o models pa ially p e en s us om discussing ce ain ace s o
CE ep esen a ion and modelling assump ions. This is a
well-documen ed issue in he li e a u e (Keppo e al. 2021; Robe son
2021).
3. Rep esen a ion o ci cula economy in IAMs
Fou main insigh s eme ge om ou analysis. Fi s , modelling e o s
ocus on na owing and closing s a egies, bu la gely o e look policies
aimed a slowing. Second, wi h espec o scales, i is e iden ha IAMs
lack he g anula i y necessa y o model de ailed CE op ions. Thi d, wi h
espec o s ages, IAMs a e cu en ly limi ed in hei abili y o accoun
o he ull supply chain e ec s and c oss-linkages o CE s a egies.
Fou h, IAMs lack he abili y o model speci ic policy ins umen s and
ace challenges in e alua ing policy po olios. We discuss each o hese
esul s in de ail below.
Fig. 3 illus a es he cha ac e is ics o ou sample. Fig. 3A demon-
s a es ha he p e alence o CE measu es in deca boniza ion scena ios
has been inc easing o e ime. Fu he mo e, Fig. 3B depic s ha PE
models ep esen he la ges ca ego y o in es iga ed models, ollowed
by CGEs, Sys em Dynamics models and Mac oeconome ic models.
Annex B con ains a de ailed discussion o hese pape s in ou sample.
3.1. S a egies: na ow and close, bu no slow enough
Mos a icles in ou e iew ocus on modelling how ’Na ow’ and
’Close’ CE policies a ec GHG emissions (Fig. 4). CE s a egies aimed a
na owing he loop a e he ocus o mos o he con ibu ions, ep e-
sen ing hal o all s a egies mapped in his e iew, and ocus on
Fig. 2. Re iewing p ocess conduc ed in his pape .
L. Magala e al.
Resou ces, Conse a ion & Recycling 225 (2026) 108614
4
Reducing p ima y ma e ial eeds ock o indus ies and ma e ial se ices
demand o end-use sec o s. The second mos modelled s a egy is
Recycling (28 %), ha is p edominan ly conside ed in indus y scena ios
as an inc ease in he u iliza ion o ecycled eeds ock du ing p oduc ion
(Fig. 4). All o he s a egies a e add essed only in a hand ul o con i-
bu ions. Some CE s a egies such as Re use and Re hink sum oge he
oughly 7 % o he s a egies examined in ou sample. They a e p e-
dominan ly applied in he anspo and building sec o s, meaning ha
(i) na ow s a egies, which p esen less economy-wide and ebound
e ec s, a e unde explo ed, (ii) he sca ci y o hese op ions poses a
signi ican knowledge gap ega ding unde s anding and e alua ing he
impac s o new business models in indus y. In some cases, he in e es in
CE is also mo i a ed by he hope o educing he dependency on im-
po ed c i ical ma e ials, which is c ucially impo an deba e ela ed o
he ene gy ansi ion (Blas e al. 2020; Capell´
an-P´
e ez e al. 2019; Hu
e al. 2024; Tokima su e al. 2018).
’Slow’ policies a e la gely unde esea ched. This is pa ly because
hey ely on end-use beha io s, which a e highly he e ogeneous and
in ol e complex dynamics be ween p ices and income ha a e di icul
o cap u e in IAMs. In he ew cases in which hey a e conside ed, hey
a e only included as a componen in he mix o CE policies op ions
desc ibed in wha -i scena ios’ na a i es (Ba e e al. 2022; Hu e al.
2024; Tokima su e al. 2018) o implici ly conside ed h ough he use o
sensi i i y analysis o es ima e he po en ial e ec s o inc easing he
li espan o ene gy gene a ion echnologies (Tokima su e al. 2018).
Reuse op ions ep esen a ound 6 % o all s a egies only and conside ed
as pa on na a i es ha oge he wi h o he s a egies se a educ ion in
p ima y eeds ocks in indus y (Boonman e al. 2023; Ba e e al. 2022;
de Oli ei a e al. 2021). Repu pose ep esen ed only 2.5 % o he CE
op ions essen ially comp ising cascading ba e ies (Hu e al. 2024a) and
indus ial symbiosis ( an Sluis eld e al. 2021) o epu posing oil
e ining in as uc u e o p ocess biomass (Be gman-Fon e e al. 2023).
Nei he Re u bish no Remanu ac u e we e conside ed in any o he a -
icles in es iga ed.
3.2. Scales: lack o g anula i y in CE modelling op ions
Wi h espec o he scale dimension, policies p omo ing CE o en
a ge ma e ials, componen s, and p oduc s (e.g. he new EU ba e ies
egula ion (Eu opean Union 2023)), bu models ypically ep esen CE
s a egies a he sec o le el. IAMs, in hei o iginal amewo k, lack he
necessa y g anula i y o cap u e hese policies e ec i ely due o insu -
icien de ail on economic sec o s and physical and economic lows
be ween hem. As shown in Fig. 5, a ound 6 % and 29 % o e iewed
pape s ocus on he p oduc and ma e ial le els, espec i ely, while all
o he s ocus on he sec o al le el. This limi a ion hinde s he accu a e
assessmen o ac o s such as was e a ailabili y, egional ecycling ca-
paci ies, and in es men cos s, making i di icul o de elop
ma e ial-balanced, low-ca bon scena ios.
The abili y o ocus on p oduc and ma e ial le el is much highe
when IAMs a e coupled wi h o he models. Among he analyses based on
single models, p oduc and ma e ial-le els accoun o jus 7.4 % and 3.7
%, espec i ely; hese numbe s aise o 42 % o ma e ial-le el and
educe o 5.8 % o p oduc -le el pe cen when in he same o analyses in
Fig. 3. Numbe o publica ions e iew by publica ion yea (A) and by Models (B).
Fig. 4. CE s a egies mapped in his e iew by model ype.Figu e.
L. Magala e al.
Resou ces, Conse a ion & Recycling 225 (2026) 108614
5
which IAMS a e linked wi h bo om-up models. Simila ly, while p o-
duc ion emains he mos analyzed sec o , model linkages signi ican ly
inc eased he ep esen a ion o he consump ion s age. Wi hou link-
ages, CE s a egies we e p edominan ly applied o he p oduc ion sec o
(63 %) and consump ion (37 %). Howe e , wi h model linkages, he
ocus shi ed: p oduc ion accoun ed o 40 %, consump ion 31 %, EoL 25
%, and ex ac ion 4 %. No ably, e alua ing CE s a egies a he ma e ial
le el du ing he consump ion phase was only possible h ough coupled
models.
The Sankey diag am p esen ed in Fig. 5 e eals se e al impo an
insigh s. Fi s , CGE models a e mo e likely o endogenously assess
di e en ypes o policy ins umen s, while PE and SD models end o
neglec his aspec . Second, Fig. 5 highligh s he limi ed ep esen a ion
o he ex ac ion and EoL s ages, unde sco ing he cu en di icul y
IAMs ace in ully applying li e cycle hinking. The p edominance o
measu es ocused on he p oduc ion s age u he illus a es ha CE
modeling, like adi ional mi iga ion modeling, o en cen e s on supply-
side in e en ions. Thi d, he unde ep esen a ion o CE s a egies ha
ac ou side he p oduc ion s age sugges s ha imp o ing he modeling o
hese s a egies depends on de eloping he ep esen a ion o o he
supply chain s ages. This, in u n, is mo e e ec i ely achie ed h ough
model linkages wi h ools ha o e g ea e sec o al and ma e ial de ail.
3.3. S ages: limi ed abili y o accoun o he ull supply chain
The e ec i e implemen a ion o CE depends on he pa icipa ion o a
di e se ange o s akeholde s wi hin he supply chain (Elia e al., 2020).
Ye , mos a ailable analysis lacks he g anula i y and he e ogenei y
needed o accu a ely ep esen complex supply chains and he CE policy
mix applied o hem. Wi h espec o s ages, Fig. 5 also highligh s a
disp opo iona e ocus on CE s a egies in he consump ion and p o-
duc ion s ages, wi h less a en ion o end-o -li e and ex ac ion sec o s.
PE models and SD amewo ks ace challenges in cap u ing he ull
supply chain, pa icula ly when no linked wi h o he models. E en
when model linkages a e applied, hey all sho in adequa ely ep e-
sen ing ex ac ion sec o s, o en accoun ing only o a ailable ese es
o speci ic esou ces, such as a e ea h mine als. CGE and mac o-
econome ic models a e pa icula ly well-sui ed o ep esen ing en i e
supply chains due o hei s uc u al cha ac e is ics; howe e , as no ed,
hey la gely lack ma e ial-le el g anula i y.
F om ou e iew, no IAM has success ully de ailed bo h ex ac ion
and End-o -Li e sec o s comp ehensi ely. Speci ically, cu en IAMs lack
an in eg a ed app oach o accoun o esou ce a ailabili y, o e decaying
and he ene gy expendi u es associa ed wi h p ocessing and e ining
esou ces in ex ac ion sec o s. Simila ly, mos models p o ide ei he
limi ed o sec o -speci ic ma e ial and ene gy-balanced ep esen a ions
o EoL sec o s, o en omi ing he ene gy cos s ela ed o he ade and
anspo o ma e ials and p oduc s pos -consump ion.
3.4. Ins umen s: limi ed modelling o speci ic policy ins umen s and
challenges in e alua ing policy po olios
Mos s udies (70 %) elied on s ylized CE na a i es wi h limi ed o
highly agg ega ed CE s a egies, hus ailing o inco po a e policy
mechanisms in a de ailed manne (Fig. 5 and Fig. 6). Ra he , exogenous
adjus men s a e made o e lec di ec ly he policies’ ou comes, a com-
mon p ac ice in PE models (´
Al a ez-An elo e al. 2024; F agkos 2022;
F i zeen e al. 2023; Oli ei a e al. 2021; Oshi o e al. 2021; Puli-
do-S´
anchez e al. 2022; Sluis eld e al. 2021; Speize e al. 2023; S eg-
mann e al. 2022; Tokima su e al. 2018; Ven e al. 2018). Only 30 % o
Fig. 5. Flow o CE s a egies mapped in he e iewed li e a u e, o ganized by model ype, policy ins umen ep esen a ion, model coupling, scale, supply chain
s age, and CE s a egy. The Sankey diag am isualizes how ci cula economy s a egies a e ep esen ed ac oss IAMs. The wid h o each low co esponds o he
numbe o CE s a egy ep esen a ions ha mo e om one node o he nex —indica ing, o example, how many s a egies a e modeled endogenously, applied a a
gi en scale, o linked o speci ic supply chain s ages.
Fig. 6. Type o policy mechanisms we e conside ed by model ype o whe he
he e is no speci ic policy mechanism we e di ec ly modelled (Indi ec ).
L. Magala e al.
Resou ces, Conse a ion & Recycling 225 (2026) 108614
6
he s udies apply endogenous CE s a egies; mos o hem use CGE and
Mac oeconome ic models (Fig. 6). O hese, 43 % in es iga e economic
policy ins umen s, 38 % ocus on egula ions, 14 % on R&D in es men s
and only 5 % on so mechanisms. This sugges s ha linking
de ailed-p ocess and economic models can be a aluable s a egy o
imp o e CE analysis, pa icula ly o e alua ing he e ec i eness o
di e en policy ins umen s. Pape s ocusing on economic ins umen s
usually e e o (i) axes on was e o ca bon-in ensi e p ima y eeds ocks
(Ha ield-Dodds e al. 2017; Polli e al. 2020), (ii) subsidies o inc ease
ecycling (F ei e-Gonz´
alez e al. 2022), and (iii) economic incen i es o
consume s o encou age he eplacemen o ba e ies ins ead o he
acquisi ion o new ca s (Hu e al. 2024b). The egula o y mechanisms
mos s udied a e bans and a ge s owa ds he consump ion o bio-based
eeds ock, educ ion o p ima y ma e ials use and was e p e en ion
(Ha ield-Dodds e al. 2017; Boonman e al. 2023). Modelling e o s
ocusing on R&D conside ed inancial incen i es o inc ease ecycling
and ma e ial e iciency a es (Lu e al. 2024). So mechanisms a e
conside ed in na a i es o p omo e beha io al shi s ega ding was e
so ing (Boonman e al. 2023).
Models ep esen CE policies in h ee ways: al e ing mone a y alues
(e.g., Boonman e al. 2023), adjus ing elas ici ies o subs i u ion o
eshape inpu ela ionships (e.g., Polli e al., 2020), o imposing con-
s ain s on ma e ial o echnological a ailabili y and consump ion (e.g.
S egmann e al. 2022). Howe e , IAMs do no inco po a e he mone a y
cos s o policy implemen a ion in o hei p icing amewo ks, and he e
a e limi ed s udies on he e ec i eness o al e na i e policy mecha-
nisms, whe he applied indi idually o in combina ion (e.g. Ha ield--
Dodds e al., 2017).
Ano he no ewo hy insigh is ha exis ing models a e de icien in
adequa ely ep esen ing he in e ac ions be ween CE s a egies when
implemen ed concu en ly. This is ue bo h in he case o policy po -
olios implemen ed wi hin a gi en scale and s age, o ac oss di e en
scales and s ages. The only excep ion is Boonman e al. (2023), which
was he sole s udy o in es iga e oge he he impac o enhanced was e
collec ion sys ems by in eg a ing was e so ing wi h ecycling a es o
p ojec he use o ecycled ma e ial in p oduc ion p ocesses. Howe e ,
no analysis was conduc ed o e alua e he pe o mance o indi idual CE
s a egies in compa ison wi h he policy mix. A compa able gap is
e iden wi h espec o he e ical policy po olio, whe ein a combi-
na ion o CE s a egies is implemen ed a he same s age o he supply
chain.
3.5. Unde s anding models’ speci ici ies and esea ch ocus
We ind ha he IAMs iden i ied in his e iew di e signi ican ly in
hei capaci y o ep esen he mul iple dimensions equi ed o obus
CE policy assessmen , as ou lined in he concep ual amewo k. PE
models, pa icula ly de ailed p ocess models linked wi h sec o al
bo om-up ools, o e a mo e g anula ep esen a ion o echnologies
and, in some cases, ma e ials, making hem sui able o simula ing
na ow, demand-side CE s a egies such as p oduc educ ion o sub-
s i u ion. Howe e , hey lack de ailed ep esen a ions o he e ogeneous
beha io s, economic eedbacks, and di e se policy ins umen s, o en
elying on exogenous assump ions implemen ed h ough “wha -i ” sce-
na ios. Thei sec o al scope is also limi ed o p oduc ion and consump-
ion s ages, cons aining sys em-wide analysis and in eg a ion o li e
cycle hinking.
Mac oeconomic IAMs, including CGE and mac oeconome ic
models, a e mo e e ec i e in cap u ing p ice-based policy ins umen s
and economy-wide eedbacks. Thei sys em-le el s uc u e enables he
assessmen o c oss-sec o al in e ac ions and dis ibu ional impac s.
Ne e heless, hey usually ely on agg ega ed ep esen a ions o ma e-
ials and echnologies, limi ing hei abili y o assess p oduc - o
ma e ial-speci ic CE s a egies. Al hough hese models can e alua e
ce ain social impac s o CE s a egies, such as e ec s on income and
employmen , hey s uggle o inco po a e non-p ice beha io al
ins umen s and de ailed agen he e ogenei y. Sys em Dynamics models
a e well-sui ed o simula ing complex CE s a egies as hey can cap u e
nonlinea eedback be ween subsys ems and dynamically ep esen
biophysical and socioeconomic cons ain s (Pulido-S´
anchez e al. 2022).
Howe e , hei high spa ial and empo al agg ega ion and eliance on
sensi i e causal assump ions make empi ical alida ion challenging. In
addi ion, he absence o explici ma ke mechanisms limi s hei ca-
paci y o assess b oade economic ade-o s. A mo e de ailed analysis o
how di e en IAMs implemen CE, including speci ic ins umen s,
s a egies, and modeling app oaches, is p o ided in Annex B, which
includes a sec o -by-sec o b eakdown o CE implemen a ion based on
he e iewed li e a u e.
Ac oss he e iewed li e a u e, all IAMs ocused on e alua ing he
bene i s o CE s a egies p ima ily in e ms o educ ions in CO₂ emis-
sions and ene gy use. CGE and mac oeconome ic models ex ended his
e alua ion o include economic indica o s, mos no ably GDP impac s.
When i comes o ma e ial demand educ ion, such bene i s we e ypi-
cally cap u ed by SD and PE models o , in a mo e agg ega ed way, by
CGE models. Howe e , his was he case in less han hal o he models
analyzed. Impo an ly, social ou comes emain la gely absen om
cu en CE-in eg a ed modeling e o s. Among he s udies e iewed,
only one SD model conside ed changes in employmen because o he
implemen a ion o CE app oaches (Allen e al., 2022), as well as he
bene i s o Sus ainable De elopmen Goals (SDGs).
This lack o a en ion o social dimensions e eals a c i ical gap in
cu en CE modeling. Al hough CE s a egies ha e he po en ial o in-
luence job c ea ion, equi y, and well-being (Vanhuyse e al. 2022),
hese impac s a e a ely add essed. Inco po a ing such dimensions is
essen ial o p o ide a mo e holis ic assessmen o CE policies and o align
modeling e o s wi h he b oade objec i es o he SDGs.
While no single model ype can ully cap u e all dimensions o CE
policy analysis, each has s eng hs wi hin speci ic laye s o he ame-
wo k. This highligh s he need o u he esea ch on model in eg a ion
and hyb idiza ion.
4. Ad ancing CE policy modeling in IAMs: key challenges and
u u e p ospec s
IAMs hold g ea po en ial as sui able ools o he e alua ion o CE
policies and hei impac on clima e mi iga ion. Ye , c i ical gaps
desc ibed in Sec ion 3 need o be add essed. Key oppo uni ies o
enhancing IAMs include model de elopmen and coupling, be e in e-
g a ion o high-quali y da a and empi ical insigh s, and de elopmen o
new scena ios. Table 1 summa izes hese oppo uni ies, iden i ying
speci ic gaps and highligh ing a eas whe e an in eg a ed app oach could
be pa icula ly bene icial. I also illus a es how add essing he iden i-
ied gaps h ough he sugges ed esea ch pa hways can con ibu e o
imp o ing IAMs.
The classi ica ion p esen ed in he Table is based on h ee main
c i e ia: (i) consis ency, which e e s o whe he he model main ains a
ma e ial-balanced amewo k, meaning ha all ma e ial lows
(including ex ac ion, p oduc ion, use, ecycling, and disposal) a e
cohe en ly acked and aligned wi h he p inciple o mass conse a ion,
ensu ing ha s ocks and a ailabili y o ep ocessing ma ch in lows and
ou lows; (ii) accu acy, de ined as he ex en o which he imple-
men a ion o CE policies in he model e lec s empi ical and sys em-le el
condi ions, pa icula ly whe he assump ions (e.g., on ecycling a es,
beha io al change, o echnological easibili y) a e g ounded in
obse ed da a o suppo ed by indus ial ecology li e a u e and applied
case s udies, he eby educing he gap be ween modeled ou comes and
policy ou comes; and (iii) comp ehensi eness, e e ing o he model’s
capaci y o e alua e a wide ange o CE policies s a egies, whe he
ac oss mul iple li e-cycle s ages (e.g., design, consump ion, end-o -li e),
h ough di e en ypes o policy ins umen s (e.g., egula o y, eco-
nomic, beha io al), applied e ically and ho izon ally. No ably, some
iden i ied gaps o e lap wi h p oposed solu ions, indica ing ha ce ain
L. Magala e al.
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7
challenges equi e an in eg a ed app oach. In he emainde o his
sec ion, we discuss in de ail key app oaches which can be implemen ed
o shi he ocus o he IAMs communi y om he modelling o CE as a
collec ion o ma ginal changes o i s ep esen a ion as a s uc u al
change implying new business models, dema e ializa ion and
se i iza ion.
4.1. Model imp o emen s
Model imp o emen s should ocus on enhancing he abili y o
conside comp ehensi ely and consis en ly he impac o CE policies.
This can be achie ed by imp o ing g anula i y (e.g., om ma e ial-le el
scale o he agg ega e mac o-scale) and be e cap u ing ade-o s,
bu den shi s, and ebound e ec s. Imp o emen s should also o e op-
po uni ies o o e come he dominan app oach o e alua ing CE pol-
icies and s a egies in isola ion.
We mapped six main challenges ha need o be ackled. Fi s , models
need o be imp o ed o include ma e ial lows be ween ups eam sec o s
(manu ac u ing and ex ac ion) and downs eam sec o s (end-o -li e
managemen ) a ma e ial le el, e ec i ely es ablishing connec ions
be ween ma e ial lows in a way ha ex ends beyond he con en ional
bounda ies o consump ion and p oduc ion. The po en ial o his solu-
ion is shown, o ins ance, by he ecen ly de eloped MESSAGEix-
Ma e ials model (Ünlü e al. 2024), which enhanced he indus ial
ep esen a ion o he MESSAGE model by adding an accoun ing o
s ocks and lows o s eel, cemen , and aluminum. Addi ionally, IAMs
should de elop inno a i e me hods o ob ain es ima es o u u e ma e-
ial consump ion ha anscend he adi ional app oach based on
econome ic eg ession models – as he la e may o he wise lock model
exe cises o po ay a ajec o y o esou ces consume ism ied o
economic g ow h a he han one mo e in line wi h he implica ions
b ough abou by ci cula p ac ices.
Second, he ange o Rs s a egies po ayed in models needs o be
expanded. Al hough na ow and slow s a egies ela ed o beha io al
choices a e ypically he mos e ec i e in e ms o mi iga ion po en ial
(Wol am e al., 2021), his e iew shows ha mos CE s a egies ocus
on Recycling and Reduce, which demons a es an unde explo ed space in
IAMs. Some ecen con ibu ions p o ide guidance on inco po a ing CE
s a egies in o IAMs. Fo ins ance, he In e na ional Resou ce Panel
(IRP) epo on he RECC model (UNEP, 2020) ou lines me hods o
quan i y he e ec s o esou ce e iciency s a egies, such as emanu-
ac u ing and epu pose, on g eenhouse gas educ ion.
Thi d, pa ame e s ep esen ing CE policy ins umen s and s a egies
in models equi e e inemen o be e e lec egional socio- echnical,
economic, sec o al and cul u al ac o s. Ins ead o using gene ic, ixed
alues (e.g., a uni o m ecycling a e in all model egions), models
should inco po a e egion-speci ic pa ame e s ha cap u e local d i e s
and ba ie s o CE adop ion. As poin ed ou by Bassi e al. (2021), CE
le e s and ba ie s a e s ongly ela ed o local speci ici ies. These local
speci ics can include logis ics, ma e ial and ene gy penal y cos s, ech-
nical a ailabili y o ans o ma ion p ocesses (e.g. ecycling, e u bish-
ing), ma ke accep ance conside ing li es yles and cul u al alues. Fo
example, Lessa d e al. (2021) has demons a ed ha he a ailabili y
and cos o by-p oduc s a y ac oss egions, which can impede he
implemen a ion o CE s a egies a he local le el. Fu he mo e, i is
impo an o ecognize ha he same policy mechanism can ha e
di e en impac s ac oss sec o s. Fo ins ance, Meglin e al. (2022) show
ha le ies a e less e ec i e han bans in he land ill sec o , while he
opposi e is ue in he cemen sec o , highligh ing he need o accoun
o such sec o -speci ic di e ences.
Table 1
Main gaps and u u e esea ch pa hways. Accu acy in blue, Comp ehensi eness in o ange, Consis ency in g een.
L. Magala e al.
Resou ces, Conse a ion & Recycling 225 (2026) 108614
8
Fou h, i is impe a i e ha models be e cap u e he beha io al
dimensions o CE and hei e ec s on emissions educ ion. Models mus
imp o e in ep esen ing he di e si y o consume s’ li es yles and i m
beha io s. Accoun ing o hese di e ences enables mo e ealis ic model
ou comes, as access o esou ces, p e e ences, and willingness o adop
sus ainable p ac ices a e in luenced by cul u al and economic con ex s
(C eu zig e al., 2018; Geels e al., 2017). While p og ess has been made
in in eg a ing li es yle ac o s in o IAMs ( an den Be g e al. 2019),
cu en models s ill ely hea ily on s ylized, exogenous na a i es and
lack amewo ks o endogenize eme ging ends, such as he sha ing
economy and shi s owa d sus ainable consump ion. New amewo ks
o unde s and he impac s o li es yle on mi iga ion (e.g. as in Pe i o
e al. (2023)) could p o ide guidance o u he de elopmen s o CE in
IAMs.
Fi h, IAMs should be de eloped o e alua e di ec and indi ec im-
pac s o CE policies on clima e mi iga ion. F om ou e iew, many gaps
pe sis , especially ega ding indi ec e ec s ac oss economic sec o s. Fo
example, he subs i u ion be ween cemen and wood in he cons uc ion
sec o can impac he o es y sec o and, consequen ly, land a ailabili y
o o he uses such as ood cul i a ion o ene gy c ops. O he nega i e
impac s such as bu den shi s and ebound e ec s can u he complica e
he e alua ion o he seconda y impac s o CE s a egies in IAMs.
Imp o emen can come om he coupling o models, pa icula ly i e -
o s a e ocused on endogenizing o he ex en possible he esponses o
he e ogeneous ac o s o hese policies.
Six h, model imp o emen s should also ocus on be e cap u ing he
compound e ec s o coo dina ed in e en ions ac oss supply chains, as
highligh ed by Milios (2018). This shi helps mo e beyond e alua ing
CE s a egies in isola ion, which o en esul s in a agmen ed iew o
CE’s deca boniza ion bene i s. Fo ins ance, economic mechanisms,
such as incen i es o ecycling ma ke de elopmen , EPR schemes,
educa ion policies, and was e impo bans, can in luence he ecycling
a es assumed in models bu a e o en o e looked. The e o e, i is
essen ial o gain a deepe unde s anding o he ope a ional mechanisms
h ough which di e se policies ope a e ac oss di e en sec o s and e-
gions. This could be achie ed by examining he indings o exis ing
s udies ha ha e explo ed he po en ial ou comes o di e en CE pol-
icies, as illus a ed by La ain e al. (2022), Meglin e al. (2022) and
Ballee e al. (2024). Few s udies ha e examined he e ec i eness o
policy po olios, whe he indi idually o in combina ion (e.g., Ha -
ield-Dodds e al., 2017). This gap highligh s he need o u he
explo a ion o he di e en ho izon al and e ical policy implemen a-
ions ha migh impac CE ou comes.
Al hough se e al model imp o emen s ha e been discussed in his
subsec ion, some can be conside ed pa icula ly ounda ional. The i s
s ep is o disagg ega e he indus ial sec o in o ele an sub-indus ies,
ollowed by i s ep esen a ion as an in e media e sec o whose ou pu is
endogenously de e mined by demand om end-use sec o s. This s uc-
u al change enables a mo e consis en analysis o how CE measu es
implemen ed in end use sec o s a ec ma e ial p oduc ion, ene gy e-
qui emen s, and indus ial emissions. Mo eo e , i acili a es he explici
ep esen a ion o ma e ial lows.
Inco po a ing a ull, mul i-laye ed depic ion o CE s a egies in o
IAMs migh equi e subs an ial s uc u al modi ica ions ha may no
align wi h hei o iginal pu pose. Conside ing hese cons ain s, adop -
ing a modula modeling app oach, in which IAMs in e ace wi h ex e nal
ools such as dynamic MFA models, can o e a lexible and echnically
easible pa hway. These s a egies a e u he elabo a ed in he
ollowing subsec ion on model coupling.
4.2. Models coupling
Many IAMs a e based on s a ic op imiza ion o simula ion ap-
p oaches o iginally designed o model ene gy lows and emissions. O e
ime, hese models ha e e ol ed o include land-use and wa e sys ems,
bu hei a chi ec u e o en emains cons ained when i comes o
inco po a ing ma e ial lows wi h su icien g anula i y. Inco po a ing
de ailed ma e ial lows wi hin hese models equen ly equi es sub-
s an ial modi ica ions ha may no align wi h he model’s ini ial design
o echnical easibili y. As such, model coupling eme ges as a solu ion,
allowing IAMs o be complemen ed by ex e nal models wi hou neces-
sa ily comp omising in e nal consis ency. Coupling enables he in e-
g a ion o CE- ele an insigh s while main aining he s eng hs o bo h
modeling app oaches.
A ange o ex e nal models can be linked o IAMs o suppo di e en
laye s o CE analysis. Fo ins ance, model linkages ha e been ecen ly
conside ed in e iewed pape s h ough in eg a ing in IAMs o indus ial
ecology amewo ks such as Ma e ial Flow Analysis (MFA), Li e Cycle
Assessmen (LCA) and En i onmen al Ex ended Mul i-Region Inpu -
Ou pu ables (EE-MRIO), Agen -Based Models (ABM) o by liking
sec o -speci ic bo om-up models in o hei main s uc u e. MFA
amewo ks ack ma e ial lows and s ocks ac oss sec o s, ensu ing
consis en conside a ion in e ms o ma e ial a ailabili y. LCA helps (i)
he calcula ion o ma e ial demand o low-ca bon echnologies due o
ma e ial in ensi ies coe icien s a ailable in li e cycle in en o ies (e.g.
Pulido-S´
anchez e al. 2022) and (ii) he choice o less ca bon-in ensi e
eeds ock and p ocesses in indus ies (e.g. Peng e al. 2022). EE-MRIO
amewo ks expand sec o al ep esen a ion in mac oeconomic models,
acili a ing he analysis o en i onmen al impac s om consump ion (e.
g. F ei e-Gonz´
alez e al. 2022). ABMs simula e he e ogeneous agen s
and hei in e ac ions, showing how indi idual beha io s shape sys em
ou comes. Linking hem wi h IAMs in oduces beha io al di e si y, so-
cial in luence, and inno a ion di usion (Rizza i and Landoni 2024).
F amewo ks such as Gia ola e al. (2022) p o ide a ounda ion o
de eloping ABMs ha simula e consume and p oduce decisions on CE
adop ion, imp o ing he ep esen a ion o mic o-le el dynamics in
long- e m CE policy analysis. Bo om-up models p o ide p ecise calcu-
la ions o ma e ial and ene gy demand in housing, mobili y and indus y
sec o s assessing he impac o consump ion pa e ns on ma e ials and
ene gy use. When combined wi h MFA, hese models e alua e CE s a-
egies like was e educ ion and ma e ial euse wi h a mass-balanced
pe spec i e, signi ican ly educing emissions (F i zeen e al. 2023;
S egmann e al. 2022).
Fu he in eg a ion be ween di e en IAMs is essen ial o be e
analyze CE s a egies. While CGE and mac oeconomic models cap u e
economy-wide e ec s, hey s uggle wi h long- e m echnological
changes. PE models, wi h de ailed echnological analysis, a e ideal o
s udying ma e ial lows and can be e be in eg a ed wi h mac oeco-
nomic models o a mo e comp ehensi e app oach.
F ui ul a enues o u u e esea ch on CE e alua ion a e he c ea ion
o i e a i e p ocesses no only be ween IAMs and indus ial ecology
models bu also be ween mac oeconomic and PE models o e ine
e alua ion o CE e ec ac oss supply chains. This includes linking
models o be e ep esen p ice luc ua ions o esou ces and hei
a ailabili y. These linkages would enable esea che s o ad ance hei
unde s anding o how CE policies migh eshape global ade and migh
educe he isk o supply dis up ions o c i ical ma e ials conside ing
economical and geopoli ical ba ie s.
Se e al app oaches can be used o ope a ionalize model coupling,
depending on he le el o in eg a ion and he di ec ion o in o ma ion
low. These include: (i) Ha d-linking, whe e wo models a e ully in e-
g a ed in o a single amewo k and sol ed simul aneously. This
app oach allows o dynamic eedback be ween sys ems bu is echni-
cally complex and equi es deep compa ibili y be ween model s uc u es
(e.g., Ba e e al. 2022); (ii) So -linking, in which models ope a e
independen ly bu exchanging da a be ween hem. This exchange can
occu in one di ec ion o i e a i ely un il con e gence o a maximum
numbe o i e a ions is eached (e.g. as in IMAGE-Ma e ials (A p e al.
2024), which is a dynamic MFA model ha ecei es se ice demands
om IMAGE model and p o ides back ma e ial demand da a) (iii)
Ha moniza ion, in which pa ame e s o assump ions om one model a e
used o calib a e ano he . Fo example, sec o al se ice demands,
L. Magala e al.
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