Abbasiha o eh, Milad; He mans, F ans
A icle — Published Ve sion
Using a che ypal analysis o de i e a ypology o
knowledge ne wo ks in Eu opean bioclus e s
Regional S udies
P o ided in Coope a ion wi h:
Leibniz Ins i u e o Ag icul u al De elopmen in T ansi ion Economies (IAMO), Halle (Saale)
Sugges ed Ci a ion: Abbasiha o eh, Milad; He mans, F ans (2025) : Using a che ypal analysis
o de i e a ypology o knowledge ne wo ks in Eu opean bioclus e s, Regional S udies, ISSN
1360-0591, Taylo & F ancis, London, Vol. 59, Iss. 1, pp. 1-17,
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Using a che ypal analysis o de i e a ypology o knowledge
ne wo ks in Eu opean bioclus e s
Milad Abbasiha o eh
a
and F ans He mans
b
ABSTRACT
The shi o a bioeconomy necessi a es adical inno a ions o mo e om a ossil- o a bio-based economy, wi h bioclus e s
playing a c ucial ole by os e ing collabo a ion among a wide ange o co-loca ed ac o s o ind solu ions. Th ough a
no el me hodological app oach in egional s udies (i.e., a che ypal analysis), his s udy discusses he speci ici ies o
bioclus e s and analyses he ela ional p ope ies wi hin Eu opean bioclus e s’ knowledge ne wo ks. Resul s sugges
ou a che ypal knowledge ne wo ks. I is a e o obse e he ansi ion o bioclus e ne wo ks om one a che ype
g oup o ano he . The indings p o ide a da a-d i en bioclus e s ypology, o e ing insigh s in o designing in o med
place-based policies.
KEYWORDS
bioclus e s; knowledge ne wo ks; in en i e ac i i ies; a che ypal analysis; unsupe ised lea ning
JEL D85, O30, O31, Q57
HISTORY Recei ed 28 Sep embe 2022; in e ised o m 29 Oc obe 2024
1. INTRODUCTION
The u gency o g een inno a ions, pa icula ly a he
egional le el, has become inc easingly appa en in ligh
o global en i onmen al challenges and he need o sus-
ainable de elopmen (Gibbs & O’Neill, 2017). The
bioeconomy has come up as a concep p omo ed by di e -
en go e nmen s wo ldwide as a po en ial pa hway o his
ansi ion (S a k e al., 2022). The bioeconomy encom-
passes using enewable biological esou ces o ex ac
essen ial componen s o ma e ials, chemicals, and ene gy.
These aw ma e ials and hei associa ed was e s eams a e
hen p ocessed o yield alue-added p oduc s, including
bu no limi ed o ood, eed, bioplas ics, pha maceu icals
and bioene gy. The o e a ching objec i e o he bioecon-
omy lies in educing ou cu en eliance on ca bon-based
sou ces de i ed om ossil uels by eplacing hem wi h
enewable sou ces o ca bon oo ed in pho osyn hesis.
Apa om he shi away om ossil uels, he bioecon-
omy also p omises o con ibu e o egional sus ainable
de elopmen , gene a e employmen in high- ech sec o s,
and p omo e he c ea ion o g een inno a ions (McCo -
mick & Kau o, 2013). By capi alising on egional asse s,
such as biodi e si y, ag icul u al lands, and na u al
esou ces, egions can ha ness he po en ial o he
bioeconomy and c ea e new de elopmen pa hs (Re sgaa d
e al., 2021). Ne e heless, he in icacies o his p ocess
emain unclea , unde sco ing he need o a close examin-
a ion o he bioeconomy and how i in luences inno a ion
p ocesses a he egional le el.
In his pape , we ocus on he ole o bioeconomy clus-
e s, o bioclus e s o sho , ha a e essen ial ools o
go e nmen s o p omo e he egional bioeconomy and o
es ablish, p omo e, and s eng hen economic collabo -
a ion, lea ning, and inno a ion p ocesses wi hin pa icula
egions (He mans, 2018). The idea is ha hey possess he
po en ial o in eg a e local esou ces, knowledge, and ne -
wo ks in o biobased inno a ions ha esul in sus ainabil-
i y e ec s ha ing impac s beyond hei local le el
(Ay ape yan e al., 2022; Kama h e al., 2022). As such,
he g owing a en ion o bioclus e s alls in o a b oade
end in clus e esea ch o ake he ole o clus e s in
he gene a ion o sus ainable, g een o eco- ech inno-
a ions mo e in o accoun (McCauley & S ephens, 2012;
Njøs e al., 2017; Sedi a & Blasi, 2021). The li e a u e
on indus ial clus e s has paid much a en ion o he
unde lying ne wo k cha ac e is ics o clus e s and o he
e i o ial inno a ion sys ems (Abbasiha o eh, 2020;
Abbasiha o eh & Maghssudipou , 2024; He mans,
2020). This s and o li e a u e discusses ha clus e s
© 2025 The Au ho (s). Published by In o ma UK Limi ed, ading as Taylo & F ancis G oup
This is an Open Access a icle dis ibu ed unde he e ms o he C ea i e Commons A ibu ion License (h p://c ea i ecommons.o g/licenses/by/4.0/), which pe mi s un es ic ed
use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal wo k is p ope ly ci ed. The e ms on which his a icle has been published allow he pos ing o he
Accep ed Manusc ip in a eposi o y by he au ho (s) o wi h hei consen .
CONTACT Milad Abbasiha o eh [email p o ec ed]; [email p o ec ed]
a
Depa men o Economic Geog aphy, Facul y o Spa ial Sciences, Uni e si y o G oningen, G oningen, he Ne he lands
b
Depa men o S uc u al Change, Leibniz Ins i u e o Ag icul u al De elopmen in T ansi ion Economies, Halle, Ge many
Supplemen al da a o his a icle can be accessed online a h ps://doi.o g/10.1080/00343404.2024.2430354
REGIONAL STUDIES
2025, VOL. 59, NO. 1, 2430354
h ps://doi.o g/10.1080/00343404.2024.2430354
di e based on hei le el o de elopmen and sec o al,
ins i u ional, and echnological composi ions. Tha said,
s udies ha in es iga e he knowledge ne wo ks wi hin
hese g een- ech, o sus ainable clus e s, a e spa se. Ou
esea ch ques ion is, he e o e: Wha a e he key cha ac-
e is ics o bioclus e s’ knowledge ne wo ks?
Wi h his pape , we aim o wo di e en con i-
bu ions: a me hodological and an empi ical one. Fi s ,
we wan o illus a e a ela i ely new and, we hink, p om-
ising ool o he u he de elopmen o ne wo k s udies
wi hin egional s udies and economic geog aphy a che y-
pal analysis o ne wo ks (D’Esposi o e al., 2010). To
answe ou esea ch ques ion, we u ilised a me hodological
amewo k based on an unsupe ised clus e ing algo i hm
o c ea e benchma ks o analysing he s uc u al p ope -
ies o knowledge ne wo ks in bioclus e esea ch. Ou
s udy ma ks he i s applica ion o his me hod in egional
s udies, despi e i s common use in o he scien i ic disci-
plines anging om biology o as onomy (Chan e al.,
2003; Gimbe na -Mayol e al., 2022). Ou app oach has
b oade applicabili y making i sui able o u u e egional
s udies ha aim o add ess classi ica ion p oblems o
answe heo e ically in o med esea ch ques ions.
Second, ou esea ch makes an empi ical con ibu ion
by i s discussing key cha ac e is ics o bioclus e s’ knowl-
edge ne wo ks and hei speci ici ies ela ed o inno a ion
p ocesses in he bioeconomy. Subsequen ly, we sys ema i-
cally iden i y and ack Eu opean bioclus e s o e ime,
‘ om he bo om-up’, adding insigh s o he ongoing
deba e su ounding his concep . C ea ing a da a-d i en
ypology o bioclus e s enabled us o dis inguish bioclus-
e s based on hei place- and pa h-dependen a ibu es.
In his pape , we discuss how his ypology helps policy-
make s o design bioeconomy policy in he u u e ha is
place-sensi i e and add ess he speci ic needs o
bioclus e s.
The pape is s uc u ed as ollows. Sec ion 2 discusses
explici ly some o he cha ac e is ics o inno a ion p o-
cesses wi hin he bioeconomy and bioclus e s and d aws
implica ions o he esul ing knowledge ne wo ks and
hei e olu ion. Sec ion 3 in oduces ou me hodology
wi h he speci ic machine lea ning app oach we applied.
Sec ion 4 p esen s and discusses he esul s and sugges s
how u u e s udies can ake ou s udy as a poin o depa -
u e o classi y o he ypes o clus e s. Sec ion 5 concludes
he pape and discusses po en ial policy implica ions con-
ce ning he success o place-based inno a ion policy
measu es.
2. INNOVATION AND KNOWLEDGE
NETWORKS IN INDUSTRIAL CLUSTERS
The in e es in he causes and e ec s o agglome a ions
and clus e s goes a long way back o he wo k o Ma shall
(1890), who obse ed he geog aphical co-loca ion o
i ms in di e en indus ial sec o s in 19 h-cen u y B i ain
and explained hem h ough he e ec s o labou ma ke
pooling, supplie specialisa ion and knowledge spillo e s.
Since he 1990s clus e s ha e become a popula ool o
go e nmen s o s eng hen egional economic compe i-
i eness and inno a ion (McCauley & S ephens, 2012;
Po e , 1998). Wi h inno a ion being an impo an goal
o many clus e policies, in e es has g own in o he
cha ac e is ics o he knowledge ne wo ks wi hin agglom-
e a ions and clus e s and how hese change o e ime
(Glückle , 2007; Powell e al., 2005). This ype o esea ch
in es iga es he spa ial dis ibu ions o egional economic
p ocesses no in isola ion, bu ins ead hey conside he
dynamic and in e connec ed na u e o economic ac i i ies
and uses a lens o social ela ionships, ne wo ks and in e -
ac ions (Ba hel & Glückle , 2003; Boggs & Ran isi, 2003;
Oinas & Malecki, 2002). This esea ch in es iga es how
ne wo k cha ac e is ics o an indus ial clus e in luence
bo h he pe o mance o he indi idual o ganisa ions
wi hin he clus e as well as i s o e all unc ioning (Belussi
e al., 2010; B eschi & Male ba, 2005; Ka lsson e al.,
2005).
Wi h he ‘ ela ion u n’, he li e a u e on indus ial
clus e s has de eloped and applied se e al di e en
me hods: analysis ools, models, and p ocedu es o aid in
hese in es iga ions: social ne wo k analysis (SNA) (Giu-
liani & Pie obelli, 2011; Te Wal & Boschma, 2009),
(adap ed) g a i y models, quad a ic assignmen p ocedu es
(QAP) (B oekel e al., 2014; Simensen & Abbasiha o eh,
2022), and s a is ical ne wo k models such as exponen ial
andom g aph models (ERGMs) and s ochas ic ac o -
o ien ed models (SAOMs) (Abbasiha o eh, 2020; He -
mans, 2020; Maggioni e al., 2007). In his sec ion, we
will discuss h ee opics. Fi s , we discuss some o he cu -
en ne wo k app oaches in egional s udies and economic
geog aphy, and how hey ela e o some o he me hodo-
logical oolboxes in use. We will sho ly in oduce he
a che ypal analysis and compa e i wi h exis ing me hods.
We will speci ically discuss he con ex o ou empi ical
case s udy: bioclus e and hei main o e lap and di e -
ences wi h o he sec o al indus ial clus e s. We will end
his sec ion wi h a discussion on how he cha ac e is ics
o bioeconomy will likely in luence also he ype o inno-
a ion p ocesses and hei associa ed knowledge ne wo ks
wi hin bioclus e s.
2.1. Analysing knowledge ne wo ks in
indus ial clus e s: me hods and applica ions
Ne wo k analysis is a me hodological app oach used o
s udy he s uc u es o ne wo ks, ocusing on nodes
(ac o s) and edges ( ela ionships) o unde s and hei
in e ac ions and dynamics. P e ious esea ch has applied
ne wo k analysis o indus ial and inno a ion clus e s o
unde s and in e ac ions, collabo a ions, and dynamics
wi hin clus e s (B oekel e al., 2021). Ea ly ne wo k
s udies ocused especially on how he s uc u al posi ion
o an ac o wi hin he clus e knowledge ne wo k would
de e mine i s economic and inno a i e pe o mance.
This ype o esea ch looks a he in luence o s uc u al
holes (Ahuja, 2000; Walke e al., 1997) o he posi ion
o a i m wi hin he co e o he pe iphe y o he clus e
(Giuliani, 2013). La e , o he models in es iga e he
cha ac e is ics o he comple e clus e ne wo k: how
2 Milad Abbasiha o eh and F ans He mans
REGIONAL STUDIES
hese ne wo ks we e o med unde he in luence o mic o-
le el mechanisms o ie o ma ion be ween ac o s, o
ins ance, h ough ecip oci y, homophily o iadic closu e.
O en, hese mic o-le el p ocesses we e hen linked o
di e en o ms o p oximi y: geog aphical, ins i u ional,
social, cogni i e, and o ganisa ional and hei in e play
(Janssen & Abbasiha o eh, 2022; Kabi igi e al., 2022).
O he s udies looked in o how hese ne wo ks changed
o e ime in s udies o ne wo k e olu ion (Menzel e al.,
2017; Nico a e al., 2013; Te Wal, 2013), o a combi-
na ion o hese wo: how di e en o ms o p oximi y
in luence ne wo k e olu ion o e ime (Balland, 2012;
Lazze e i & Capone, 2016).
Al hough ne wo k heo y and ne wo k ools ha e
become inc easingly popula in clus e and agglome a ion
s udies, he e is no consensus on he e ec s o di e en
ypes o ne wo k cons ella ions on a clus e ’s inno a i e
pe o mance. Wha is conside ed bene icial in some
ins ances can become de imen al in o he cases: new and
eme ging indus ial clus e s e sus old and es ablished
indus ial clus e s, sec o al speci ics o clus e s (high ech
e sus low ech), he ype o inno a ion being pu sued:
inc emen al o adical and explo a i e e sus exploi a i e.
I is he e o e impo an o analyse he ea u es o a sec o al
clus e , in ou case bioclus e s. This would also align wi h
He mans (2020), who calls o mo e e lec ion on a clus e ’s
in e nal and ex e nal cha ac e is ics in he se up o a ne -
wo k s udy. In a simila ein, Glückle and Pani z (2021)
a gue o he use o ‘in o med p e-speci ica ion’, which
hey de ine as ‘ac i ely in eg a ing he al eady exis ing sub-
s an i e unde s anding o a pa icula esea ch con ex in o
he speci ica ion o a o mal ne wo k model’ (p. 19).
In his pape , we u ilised a che ypal analysis o s udy
knowledge ne wo ks in clus e s, s a ing om his
‘in o med p e-speci ica ion’. The a che ypical analysis
in ol es he iden i ica ion o o iginal pa e ns o models
in mul i a ia e da a. In o he wo ds, he a che ypal
model aims o ind ‘pu e ypes’ in he da a and de e mine
how each da um poin is a combina ion o con igu a ions
ep esen ed by hose pu e ypes (Cu le & B eiman, 1994;
Eugs e & Leisch, 2009). The lexibili y o his me hod
allows o classi ying a wide ange o da a ypes anging
om ex s o ci ies, o spa ial di usion pa e ns, and o
ne wo ks. In his con ex , he p oblem is iden i ying se -
e al hypo he ical poin s (a che ypes) wi hin a se o mul i-
a ia e da a based on heo e ically in o med indices (Cu le
& B eiman, 1994). Ne wo k scien is s use his echnique
o educe he dimensionali y o ne wo k da a and classi y
hem based on ne wo k a ibu es (Po zio e al., 2008;
Ragozini e al., 2017). This echnique is ele an o in es-
iga ing clus e ne wo ks o wo easons. Fi s , he model
is lexible, enabling us o de ine ne wo k indices ha a e
heo e ically ele an o speci ic ypes o clus e s. In his
pape , we will illus a e he app oach in he case o bioec-
onomy clus e s, bu he me hod would also be adap able
o use in o he indus ial clus e s. Second, he esul s
show how each da a poin is a mix u e o iden i ied pu e
ypes. The la e is pa icula ly impo an o unde s and-
ing he e olu iona y ajec o ies o clus e s by unco e ing
he unde lying s uc u es and dynamics (see he me hod
sec ion o a mo e de ailed accoun o he a che ypal analy-
sis me hod). In he ollowing sec ion, we will unde ake
in o med p e-speci ica ion o in es iga e bioclus e knowl-
edge ne wo ks.
2.2. G een inno a ions in he con ex o he
bioeconomy and bioclus e s
In his sec ion, we i s analyse some o he pa icula i ies
o knowledge ne wo ks in bioclus e s. I is impo an o
no e ha al hough he bioeconomy and bioclus e s a e
o en associa ed wi h posi i e policy goals as sus ainable
de elopmen , ag icul u al de elopmen , and eco-inno-
a ions (Wilde & He mans, 2021), in eali y, many sec o s
ela ed o he bioeconomy ca y a hea y pollu ion load.
No all bio-based inno a ions can be called sus ainable,
and some can e en ha e se ious de imen al sus ainabili y
e ec s and ade-o s a o he scales and le els (Ay ape yan
& He mans, 2020; B ö ing e al., 2020). The e o e,
Kama h e al. (2022) a gue ha many bioclus e s need o
make a sus ainabili y ansi ion be o e con ibu ing o
he o e a ching goals o g een inno a ion. Based on he
ea lie wo k o Ma kusen (1996) and Iamma ino and
McCann (2006), He mans (2021) iden i ies ou di e en
ypes o bioclus e s and hei po en ial sus ainabili y e ec s
in hei localised p oduc ion chains om aw ma e ials o
p oduc ion and use (Table 1).
Table 1 explo es he sus ainabili y o a ious bioclus-
e s, highligh ing hei en i onmen al impac s and inno-
a ion dynamics. Fi s , ag icul u al agglome a ion and
g een chemis y bioclus e s depend hea ily on biomass
p oduc ion, o en using impo ed biomass, which can
cause signi ican en i onmen al p essu es. Ma shallian
bio-dis ic s, which can localise hei p oduc ion p ocesses,
may achie e sel -su iciency and hus ha e a close link o
local na u al esou ces. Li e science clus e s, on he o he
hand, ely minimally on biological sou ces, making bio-
mass sou cing less c i ical. A he same ime, bioclus e s
se icing global ma ke s can c ea e subs an ial local
en i onmen al p essu es due o hei concen a ed p o-
duc ion. G een chemis y clus e s can heo e ically
enhance sus ainabili y h ough ci cula ‘c adle- o-c adle’
designs based on indus ial ecology p inciples (Ay ape yan
e al., 2022).
The ypology o bioclus e s shows ha bioclus e s a e a
b oad opic whe e inno a ion p ocesses can a y acco ding
o he ype o knowledge base: doing, using and in e ac ing
(DUI) and science, echnology, inno a ion (STI) (Jensen
e al., 2007). Clus e s using DUI knowledge, ypical o
ag icul u al agglome a ions and bio-dis ic s, o en lack
esou ces o high- ech inno a ions. En i onmen al inno-
a ions usually come om go e nmen -induced public–
p i a e pa ne ships and scale h ough local knowledge
ans e (He mans e al., 2019). On he o he hand,
g een chemis y and li e science clus e s ely on STI
knowledge o high- ech inno a ions. Regional uni e -
si ies play a key ole in aining specialis s, pa ne ing in
R&D, and gene a ing spin-o s. These clus e s’ ans o -
ma i e po en ial lies in de eloping en i onmen ally
Using a che ypal analysis o de i e a ypology o knowledge ne wo ks in Eu opean bioclus e s 3
REGIONAL STUDIES
iendly p oduc ion me hods and new p oduc s, hough
li e sciences’ sus ainabili y impac s a e ini ially mo e econ-
omic and social.
2.3. P e-speci ica ion o bioclus e knowledge
ne wo ks
In his pape , we will ocus on he ypes o bioclus e s ha
ely mo e on codi ied o ms o knowledge: li e science
clus e s and g een chemis y clus e s. This ocus simpli ies
he complexi y associa ed wi h he wide a ie y o bioclus-
e s in heo y and p ac ice and aligns wi h he na u e o ou
pa en -based da abase (see he me hodology sec ion).
D awing on Golembiewski e al. (2015) and Van Lancke
e al. (2016), we iden i y ou key ac o s ha cha ac e ise
sus ainable bio-based inno a ion p ocesses and a e
especially ele an o o ming knowledge ne wo ks in
hese clus e s.
Fi s , a ansi ion o he bioeconomy would equi e
adical changes in he exis ing business models and supply
chains. E en inno a ions, such as so-called ‘d op-in
chemicals’, ha ocus on he manu ac u ing o known
chemical building blocks, like (bio)me hane and (bio)e ha-
nol, will equi e adical changes in hei supply chains as
e ine ies make he ansi ion owa ds bio e ine ies and
swi ch om a single sou ce o inpu (c ude oil) ha is
a ailable all yea a ound o a la ge numbe o smalle sup-
plie s and whe e wea he and clima e e ec s ha e o be
aken in o accoun (Bi ch & Cal e , 2015). The swi ch
owa ds hese new alue chains may be challenging and
g adual, equi ing a ocus on di e en o ms o connec-
i i y as his swi ch in ol es a mo e om well-es ablished
ela ionships in he ossil uel indus y o agmen ed,
sho e connec ions in new alue chains (Wilde & He -
mans, 2024).
Second, he bioeconomy encompasses a complex
knowledge base de i ed om di e se ields, including li e
sciences, bio echnology, ag onomy, ood science, social
science, nano echnology, enewable ene gy, was e man-
agemen , in o ma ion and communica ion echnologies
(ICT), and enginee ing (Golembiewski e al., 2015).
The complexi y o he knowledge base necessi a es in e-
g a ing di e se knowledge ypes wi hin ou sample, s a -
ing om a b oad ange o pa en s a he han a na owly
de ined knowledge base. In addi ion, his b oad knowl-
edge base also equi es speci ic a en ion o he issue o
mul idisciplina i y, as many inno a ions occu on he
in e ace o speci ic knowledge bases in di e en o ms
o egional di e si ica ion (Boschma e al., 2017).
Thi d, policy and ins i u ional e ec s in he bioecon-
omy also shape p e e ed ne wo king ypes. Es ablished
Table 1. Theo e ical bioclus e ypes and ne wo ks.
Bioclus e ype Examples
Links o esou ce
base
P oduc ion and
manu ac u ing
Goal o inno a ion
in sus ainabili y
ansi ion
Ag i- ood clus e s
(ag icul u al
agglome a ions)
Ho icul u al clus e s,
wine clus e s, in ensi e
animal husband y a eas
Sec o speci ic:
.C ops and dia y:
mainly local
.In ensi e animal
husband y: global
Local nega i e
en i onmen al e ec s a
clus e le el
Op imising e iciency
o p oduc ion
( h ough NARS)
G een chemis y
clus e s
Bio e ine ies, g een
chemis y (indus ial
bio ech), pape and pulp
clus e s
Sec o speci ic:
.Pape and pulp:
mainly local
.Bio uel and
bioene gy: global
Local nega i e
en i onmen al e ec s a
clus e le el
.Imp o e e iciency
o con e sion
.Indus ial ecology
Ma shallian bio-
dis ic s
Design, ashion, lea he ,
wood cons uc ion, and
building
Medium o high
dependence on local
inpu s, o en linked o
egional b anding
Risk o ou sou cing
en i onmen ally
pollu ing ac i i ies
( ex iles, lea he )
.Demand-d i en
p oduc design o
eco iendly
consume s
.Localisa ion o
p oduc ion chains
and ag o-ecology
Li e science clus e s Pha maceu icals and
medicine ( ed bio ech),
cosme ics
Low Low .Inno a ion as a
goal: new p oduc s
and p ocess
incuba o
.Risk o ebound
e ec
No e: NARS, Na ional Ag icul u al Resea ch Sys em.
Sou ce: Adap ed om He mans (2021), used wi h pe mission.
4 Milad Abbasiha o eh and F ans He mans
REGIONAL STUDIES
indus ies, especially hose ela ed o ossil uels and he
chemical indus y, es ic i e policies, and high comme -
cialisa ion cos s lead o signi ican ba ie s due o high
swi ching cos s and a lack o quali y s anda ds (Wilde &
He mans, 2021). Sec o s wi h es ic i e policies and
high comme cialisa ion ba ie s a e mo e likely o de elop
hie a chical knowledge ne wo ks. These ne wo ks a e
cha ac e ised by a ew dominan nodes (key ins i u ions
o companies) con olling he low o in o ma ion, leading
o slowe knowledge dissemina ion bu po en ially mo e
con olled and high-quali y inno a ion ou pu s. Howe e ,
sec o s wi h lexible policies, suppo i e en i onmen s, and
e o s o educe adop ion ba ie s may cul i a e small-
wo ld ne wo ks. These ne wo ks ha e nume ous in e con-
nec ions among nodes, p omo ing apid and wide- each-
ing knowledge ans e and os e ing inno a ion h ough
equen in e ac ions and collabo a ions. These ne wo ks
can be cha ac e ised by hei small-wo ldliness.
Finally, i is impo an o no e ha we expec a mix o
es ablished and eme ging indus ial clus e s, wi h some
clus e s eme ging a ound comple ely ne echnologies
and o he bioclus e s ha a e ac ually in di e en an-
si ion phases, o ins ance, mo ing om ‘b own o
g een’, o ‘pa h upg ading’ (T ippl e al., 2020). Following
Menzel and Fo nahl’s (2010) clus e e olu ion heo y, he
indus ial ne wo ks wi hin a clus e only de elop in one o
he la e s ages, meaning ha some Eu opean bioclus e s
migh s ill be in an ea ly de elopmen s age, and one can
s ill expec he connec edness o hei ne wo ks. This
a gumen also esona es wi h o he empi ical indings
ha show collabo a i e ne wo ks o en s a as agmen ed
and ‘ luid’ (F i sch & Zoellne , 2020). This means o ou
knowledge ne wo ks, we a e pa icula ly in e es ed in he
issues o (1) connec i i y, (2) hie a chy and (3) small-
wo ldliness. We will u he elabo a e on his in he me h-
odology sec ion.
3. METHODOLOGY
3.1. Cons uc ion o bioclus e s and hei
knowledge ne wo ks
Inspi ed by he wo k o Ma shall (1890) and Po e
(1998), one can desc ibe an indus ial clus e as a geo-
g aphical concen a ion o i ms and a ilia ed o ganisa-
ions, such as uni e si ies, wi hin one o mul iple
in e connec ed indus ies. These en i ies a e linked
h ough inpu –ou pu ela ionships, in o mal in e ac ions,
coope a i e local ne wo ks, and labou mobili y. An
empi ical de ini ion o a clus e is daun ing (Ma in &
Sunley, 2003). Mo e ecen concep ual and empi ical
wo ks sugges ha de ec ing clus e s should go beyond
inding a geog aphical concen a ion o economic ac i i-
ies and conside in e -o ganisa ional ela ions (Janssen
& Abbasiha o eh, 2022; Delgado e al., 2016). Following
hese s udies, we ha e used pa en s o map he geog aphi-
cal concen a ion o bioeconomy collabo a i e ela ions. In
o he wo ds, we de ine bioclus e s om a ne wo k pe -
spec i e as egions wi h a signi ican ly highe numbe o
in en i e ies han su ounding egions. The assump ion
is ha his numbe also co ela es wi h inpu –ou pu
ela ions, in o mal in e ac ions, coope a i e local ne -
wo ks, and labou mobili y.
While one should be awa e o p oblems associa ed wi h
using his ype o da a (A chibugi & Plan a, 1996; F i sch
e al., 2020), he assump ion is ha join in en i e ac i i-
ies a e accompanied by knowledge exchange and mu ual
lea ning. We used he O ganisa ion o Economic Co-
ope a ion and De elopmen ’s (OECD) REGPAT da a-
base.
1
This da abase p o ides in o ma ion ega ding he
geog aphical loca ion o in en o s and applican s a he
NUTS3 le el, In e na ional Pa en Classi ica ion (IPC)
echnological classes, and espec i e iling and g an
da es o each pa en . Conce ning ou b oad de ini ion
o a clus e i is impo an o no e ha pa en s a e always
iled by one o se e al in en o s, and hose indi iduals can
be associa ed wi h a business, bu also a uni e si y, esea ch
ins i u e, legal i m o go e nmen agency. As such,
pa en s a e a sou ce o public and p i a e o ganisa ions.
Since his s udy ocuses on iden i ying and analysing
bioeconomy clus e s, we should i s sys ema ically de ine
bioeconomy ela ed pa en s. Technology codes included
in pa en documen s p o ide aluable in o ma ion abou
u ilised echnology in in en ions. K iesch and Losacke
(2024) employed ad anced machine lea ning echniques
o iden i y bioeconomy pa en abs ac s. Thei esea ch
sugges s ha se e al ag icul u al and ood- ela ed echnol-
ogy classes a e associa ed wi h an excep ionally high sha e
o bioeconomy pa en s. Acco dingly, we ollowed he
ex ensi e li e a u e e iew o F ie sch e al. (2016) ha
iden i ied he key IPC echnology codes o he bioecon-
omy (see Appendix A in he supplemen al da a online).
In e es ingly, he sugges ed echnologies align wi h he
indings o K iesch and Losacke (2024). Fo ins ance,
bo h s udies iden i ied bioeconomy echnologies such as
new plan s o p ocesses o ob aining hem; plan ep o-
duc ion by issue cul u e echniques (A01H), and o ganic
e ilise s, o ins ance, e ilise s om was e (C05F).
We selec ed pa en s ha include echnology codes
associa ed wi h bio echnology (bio ech) and a leas one
o 12 ollowing bioeconomy ca ego ies: (1) ag icul u e
and o es y, (2) pulp and pape , (3) machines ca ons
boxes p in ing, (4) gene ic enginee ing, (5) landscape
managemen , (6) ood, (7) p o eins, (8) bio uels (including
bio uels o anspo ), (9) biomass, (10) bioma e ials, (11)
ma ine, (12) animals li es ock managemen , and (13)
ood- ela ed household appliances. Using his app oach,
we il e ed 568,883 pa en s (17% o pa en s) a he agg e-
ga e le el. Ou app oach esona es wi h ou concep ual
ocus on in e disciplina y in en i e ac i i ies ha unde -
line a se o ac i i ies in which bio ech con ibu es o
o he bioeconomy echnologies by p o iding cu ing-
edge echnology solu ions.
Nex , we p ojec ed wo-mode (in en o -by-p ojec )
collabo a i e knowledge ne wo ks o one-mode (in en-
o -by-in en o ) ne wo ks. We used he in o ma ion on
in en o s’ geog aphical posi ion (NUTS2 Eu opean
egions) o c ea e in a- and in e egional ne wo ks.
2
Fol-
lowing a common app oach, he da a we e disagg ega ed
Using a che ypal analysis o de i e a ypology o knowledge ne wo ks in Eu opean bioclus e s 5
REGIONAL STUDIES
in o six i e-yea ime-windows: (1) 1986–90, (2) 1991–
95, (3) 1996–2000, (4) 2001–05, (5) 2006–10 and (6)
2011–15 (Abbasiha o eh e al., 2023a; Li e al., 2014;
Menzel e al., 2017; Te Wal, 2014). Fo he sake o sim-
plici y, we add ess each ime-window wi h he las yea o
each ime-window.
As a cu -o poin o he clus e egion, we de ined he
90 h pe cen ile o he densi y o each ime-window (see
Appendix B in he supplemen al da a online). Howe e ,
i is essen ial o no e ha de ining clus e based on admin-
is a i e bounda y such as he NUTS classi ica ion sys em,
is c i icised due o p oblems associa ed wi h he so-called
modi iable a ea uni p oblem (B enne , 2017; Scholl &
B enne , 2014). To ackle his p oblem, we included col-
labo a o s ou side bioclus e s as a pa o bioclus e s’ ne -
wo ks i he e is a leas one collabo a i e ie be ween
hem. Inspi ed by he ela ional u n in economic geog a-
phy, his implies ha knowledge ans e ela ions a e no
limi ed o he adminis a i e bounda ies o a gi en clus e .
Thus, we alloca ed an in e egional collabo a i e connec-
ion o mo e han one NUTS2 egion i hey a e home
o in en o s who de eloped a gi en pa en (Ba hel &
Glückle , 2003).
3.2. S ylised cha ac e isa ion o clus e
knowledge ne wo ks
Based on ou e iew o inno a ion p ocesses wi hin he
bioeconomy we iden i ied h ee elemen s as pa icula ly
salien : hie a chy (as displayed in scale- ee ne wo ks),
small wo d ne wo ks and connec i i y. The scale- ee ne -
wo ks o Ba abási and Albe (1999) and he small-wo ld
ne wo ks o Wa s and S oga z (1998) belong o he mos
well-known ne wo k s uc u es in science. These wo ne -
wo ks can ha e bo h posi i e and nega i e e ec s on inno-
a ion ou comes. The scale- ee, cen alised ne wo ks o
Ba abási and Albe (1999) possess many ‘s uc u al
holes’. B idging a s uc u al hole p o ides indi iduals
wi h no el and non- edundan in o ma ion (Bu , 1992,
2005). The skewed deg ee dis ibu ion o scale- ee ne -
wo ks, wi h only a ew nodes ha possess he as majo i y
o connec ions, esul s in a s a -like ne wo k con igu a ion
ha inc eases he likelihood o ac o s ecei ing new in o -
ma ion. I we de ine inno a ion as he no el combina ion
o exis ing knowledge, hen such a scale- ee ne wo k
could po en ially be associa ed wi h mo e inno a ions
due o he e iciency o a cen alised ne wo k in knowledge
ci cula ion (Albe e al., 2000).
Ano he signi ican ne wo k pa e n discussed in he
li e a u e ha holds po en ial o os e ing inno a ion is
he ‘small-wo ld’ s uc u e, as in oduced by Wa s and
S oga z (1998). This ype o ne wo k exhibi s a high
le el o clus e ing ( ansi i i y) and sho a e age pa h
leng h be ween i s nodes. Schola s in he ield o social
sciences ha e emphasised he impo ance o clus e ing as
a o m o social embeddedness esul ing om sha ed
pas expe iences. Clus e ing, in u n, acili a es us o -
ma ion, limi s oppo unis ic beha iou , and educes ans-
ac ion cos s (Coleman, 1988; G ano e e , 1985; Uzzi,
1997).
Besides clus e ing, he second c i ical elemen is con-
nec i i y wi hin he ne wo k. Connec i i y plays a c i ical
ole in how knowledge is ans e ed among indi iduals
and o ganisa ions because highly agmen ed knowledge
ne wo ks do no p o ide he equi ed knowledge ans e
channels (Fleming e al., 2007). Connec i i y can wo k
in wo ways. As clus e s de elop, i is expec ed ha hei
knowledge ne wo ks will become mo e connec ed o e
ime (Menzel & Fo nahl, 2010), howe e as we ha e
a gued in he heo e ical sec ion in he swi ching p ocess
om es ablished indus ies owa ds mo e biobased p o-
duc ion, he connec i i y o a clus e migh a he i s
ime show a dec ease o connec i i y o e ime.
Figu e 1 b ings he wo connec i i y and deg ee dis i-
bu ion elemen s in o a single amewo k. We can iden i y
ou quad an s based on he ne wo k hie a chy (scale- ee
e sus small-wo ld) and connec i i y (low e sus high
densi y).
I is plausible ha complex knowledge ne wo ks en ail
s uc u al p ope ies o bo h small-wo ld and scale- ee
ne wo ks a he same ime. Fo ins ance, an ill-s uc u ed
co e–pe iphe y knowledge ne wo k could show small-
wo ldness a he co e (i.e., a high deg ee o clus e ing
and sho a e age pa h leng h) and scale- eeness in he
pe iphe y whe e poo ly connec ed nodes a e connec ed
o nodes a he co e (Vicen e, 2017).
3.3. Using a che ypal analysis o c ea e a
knowledge ne wo k ypology
In es iga ing he s uc u al p ope ies o knowledge ne -
wo ks equi es using mul iple ne wo k indices as p oxies
o a ious aspec s o ne wo ks. Al hough ne wo k indices
migh heo e ically cap u e di e en s uc u al p ope ies
o a ne wo k, hey a e o en co ela ed. The a che ypal
analysis me hod sugges ed by Cu le and B eiman
(1994) is an unsupe ised machine lea ning echnique
ha educes he dimensionali y o da a by g ouping obse -
a ions in o a se o a che ypes using a da a-d i en
app oach. Ins ead o looking o ‘ ypical’ obse a ions
(clus e cen es), i seeks ex emal poin s in mul idimen-
sional da a. We used his me hod o benchma k bioclus-
e s knowledge ne wo ks (Po zio e al., 2008; Ragozini
e al., 2017). We desc ibe his me hod below in wo
s eps: (1) selec ing a se o pa ame e s ha desc ibe he
s uc u e o ne wo ks; and (2) de ining a se o a che ypes
ha maximise wi hin-a che ypes commonali ies and
be ween-a che ypes di e ences.
3.3.1. Selec ing a se o ne wo k pa ame e s
We ha e ope a ionalised he ou ne wo k ypes o Figu e
1by se e al indica o s. Table 2 p esen s a lis o ne wo k
measu es and co esponding me hods ha e lec he
ex en o which he obse ed ne wo ks a e scale- ee,
small-wo ld and connec ed. We e ained om including
dis ance-based indices (e.g., he a e age pa h leng h) in
he analysis because he obse ed bioclus e s collabo a ion
ne wo ks a e o en agmen ed.
To con ol o he clus e ing p oblem associa ed wi h
he p ojec ion o bipa i e ne wo ks, we applied he so-
6 Milad Abbasiha o eh and F ans He mans
REGIONAL STUDIES
called ‘ne wo k o places’ (Abbasiha o eh e al., 2023a;
Lucena-Pique o & Vicen e, 2019; Piza o, 2007). This
clus e ing p oblem occu s when wo king wi h he p ojec-
ion o bipa i e ne wo ks. This clus e ing becomes p o-
blema ic because eam size inc eases o e ime (Van de
Wouden, 2020) and a ies ac oss di e en echnologies
(B oekel, 2019), causing a di e en deg ee o clus e ing
and biases clus e -based measu es o s uc u al p ope ies.
To add ess he p ojec ion bias and educe he impac o
clus e ing caused by ne wo k p ojec ion, we u ilise he
concep o ‘s uc u al equi alence’, which has i s oo s in
social ne wo k heo y. This concep was de eloped by Lo -
ain and Whi e (1971) and Bu (1987). In his app oach,
nodes wi hin a ne wo k a e conside ed s uc u ally equi -
alen i hey sha e iden ical ela ionships. This equi alence
enables hem o access simila esou ces wi hin he ne -
wo k (Gnyawali & Madha an, 2001; S ua & Podolny,
1996). Figu e 2 illus a es h ee ne wo ks. The isualisa-
ion on he le -hand side shows a bipa i e ne wo k in
which in en o s A, B and C collabo a e on de eloping a
pa en , and in en o s C and D a e in ol ed in de eloping
ano he pa en . This ne wo k can be p ojec ed o a one-
mode ne wo k shown in he middle, which p o ides a
high deg ee o clus e ing due o p ojec ing a wo-mode
ne wo k. The me hod o ne wo ks o places (Figu e 2,
igh ) enables us o p ojec a wo-mode ne wo k wi hou
inc easing he deg ee o clus e ing in he ne wo k. Ea lie
esea ch shows ha he new ne wo k (i.e., a ne wo k o
places) is no biased owa ds di e en eam sizes o e
ime and ac oss echnologies. I ep esen s he s uc u al
p ope ies o o iginal ne wo ks (Abbasiha o eh e al.,
2023a). Ou analysis is based on he empi ical
in es iga ion o ne wo ks o places ( o he sake o consis -
ency, we use he e m knowledge ne wo ks o collabo a i e
ne wo ks).
The second p oblem is associa ed wi h he ac ha
some ne wo k indices a e size a ian . This implies ha
es ima ed indices o collabo a i e ne wo ks wi h di e en
sizes need o be no malised and hen used as inpu s o
he a che ypal analysis me hod. To no malise, we plo ed
each ne wo k index (co esponding o a bioclus e in a
gi en ime-window) on he y-axis and he size o he ne -
wo k (node numbe ) on he x-axis. We calcula ed he
esiduals o he non-pa ame ic eg ession i ( he dis-
ance be ween each do and he blue spline in Figu e
3, le panel). In his way, he ne wo k size does no
s ongly a ec he no malised coe icien s (Figu e 3,
igh panel).
3.3.2. De ining a se o a che ypes
Based on he p e ious s ep, each ne wo k can be p esen ed
as a se o nume ical ec o s (i.e., no malised ne wo k indi-
ces). The a che ypal analysis me hod is an unsupe ised
machine lea ning echnique o de ine dis inc ca ego ies
by minimising he wi hin-g oup and maximising he
be ween-g oup squa ed e o s in each mix u e o iden i ied
a che ypes in he mul i a ia e Euclidean space (Cu le &
B eiman, 1994). Fo mally, Ragozini e al. (2017) ake a
se o n ne wo ks V={xi} i ¼1, … , n, x
i
∊ Rp and a
di ision C ¼(C
1
, … , C
k
) o Ω in K g oups, and de ine
an in e nal simila i y as R(x
i
, C
h
), x
i
∊ C
h
, and ex e nal dis-
simila i y as D(x
i
, Ch), x
i
∉ C
h
. A mixing unc ion Φ(.)
combines wo in e nal simila i y and ex e nal dissimila i y
Figu e 1. S ylised examples o knowledge ne wo k ypes wi h a simila numbe o nodes.
No e: Randomly emo ing hal o he ies leads o a highly agmen ed ne wo k wi h a scale- ee s uc u e, whe eas he small-
wo ld ne wo k is no spli in o mul iple componen s.
Using a che ypal analysis o de i e a ypology o knowledge ne wo ks in Eu opean bioclus e s 7
REGIONAL STUDIES
unc ions:
T(xi,Ch)=F(R(xi,Ch); D(xi,Ch)) (1)
A se o a che ypes A ¼(a
1
, … , a
k
) is hen de ined as:
A={ah[Rp|aha gmax T(xi,Ch), h
=1, ...,K} (2)
This equa ion p o ides mul iple solu ions sugges ing a
di e en numbe o a che ypes wi h co esponding
esidual sum o squa es (RSS) coe icien s ha show
he abili y o de ined a che ypes o ca ego ise ne wo ks
in o dis inc g oups ( o echnical de ails, see Ragozini
e al., 2017). Using he elbow plo , one can selec he
minimum numbe o a che ypes ha cap u e he op imal
deg ee o a iance in he obse ed da a. Finally, one can
use a uzzy membe ship unc ion and es ima e a ‘pu e-
ness pa ame e ’ o maximise he in e nal simila i y o
a che ypes. Then, ne wo ks a e assigned o a speci ic
a che ype when i s dis ance in he a che ypal space is
close o one a che ype and a om he o he . They
a e no assigned a pa icula a che ype i hey ha e a
simila dis ance o wo o se e al de ined a che ypes.
To p o ide an example o he abo e-discussed
me hod and o es he eliabili y o he a che ypal analy-
sis me hod in g ouping ne wo ks based on hei s uc u al
p ope ies, we simula ed 60 ne wo ks wi h h ee dis inc
s uc u al p ope ies. We used he a che ypal analysis
me hod o g oup hem based on ne wo k indices
desc ibed in Table 1. Mo e speci ically, we simula ed
20 small-wo ld ne wo ks, 20 scale- ee ne wo ks and 20
andom ne wo ks and andomly emo ed be ween 10%
and 50% o hei ies o c ea e di e en deg ees o con-
nec i i y. Figu e 4 shows ha his echnique p o ides a
eliable me hod o g ouping ne wo ks based on s uc u al
p ope ies. A1 ep esen s an a che ype o small-wo ld
s uc u al p ope ies, A2 o scale- ee ones, and A3
and A4 o andom ne wo ks. Fou andom, i e small-
wo ld and one scale- ee ne wo ks a e assigned o none
o he ou a che ype g oups, pe haps due o hei low
connec i i y. In con as , he me hod is highly accu a e
in assigning ne wo ks o each g oup. Tha is, he numbe
o alse-posi i e cases is ze o.
Table 2. O e iew o ne wo k indices o desc ibing he s uc u e o bioclus e s knowledge ne wo ks.
S uc u al
p ope y Measu e Me hod Va iable
Small-wo ldness Communi y numbe Numbe o iden i ied communi ies using he mul ile el modula i y
op imisa ion algo i hm
COMMUNITY
Sha e o in e -
communi y ies
Numbe o ies connec ing communi ies di ided by he o al
numbe o ies using he mul ile el modula i y op imisa ion
algo i hm
INTERCOM
Modula i y Modula i y sco es MODULARITY
T ansi i i y Clus e ing coe icien (Wasse man & Faus , 1994)TRANSITIVITY
Scale- eeness
a
Ne wo k cen ali y GINI coe icien o ac o s’ deg ee cen ali y (Giuliani, 2013)CENTRALITY
Connec i i y Sha e o he la ges
componen
Numbe o nodes in he la ges componen di ided by he o al
numbe o nodes
COMPONENT
Sha e o isola es Numbe o isola ed nodes di ided by he o al numbe o nodes ISOLATES
Densi y Numbe o ies di ided by he numbe o possible ies (Wasse man
& Faus , 1994)
DENSITY
No e:
a
We used o he ne wo k cen ali y indices and co eness (K-co e decomposi ion) as a p oxy o scale- eeness. We e ain om including hese indices
in he analysis as hese indices a e highly co ela ed wi h he GINI coe icien o ac o s’ deg ee cen ali y.
Figu e 2. The me hod o ne wo ks o places.
8 Milad Abbasiha o eh and F ans He mans
REGIONAL STUDIES
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