Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal
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Vol. 24 • No. 61 • Augus 2022 739
THE MULTICRITERIA ASSESSMENT OF THE GREEN GROWTH
IN THE CONTEXT OF THE EUROPEAN UNION’S GREEN DEAL
Radek Doskočil1
*
1) B no Uni e si y o Technology, B no, Czech Republic.
Please ci e his a icle as:
Doskočil, R., 2022. The Mul ic i e ia Assessmen o he
G een G ow h in he Con ex o he Eu opean Union’s
G een Deal. Am i ea u Economic, 24(61), pp. 739-757.
DOI: 10.24818/EA/2022/61/739
A icle His o y
Recei ed: 3 Feb ua y 2022
Re ised: 12 May 2022
Accep ed: 16 June 2022
Abs ac
The a icle deals wi h he issue o assessmen o he g een g ow h in con ex o he G een
Deal in he Eu opean Union Coun ies. Because his issue leads o he decision p oblem
which has ypical he p ope ies o mul i-c i e ia decision making (MCDM), he Analy ical
Hie a chy P ocess (AHP) me hod, including he sensi i i y analysis, was used as a sui able
me hod o sol ing his issue. The main aim o he pape is o p opose a new and sui able
app oach o a complex and sys ema ically assessed G een G ow h in coun ies o he
Eu opean Union. The public OECD G een G ow h da abase was used o he analysis. The
mul ic i e ia assessmen model employs ou c i e ia, indica o s o moni o ing p og ess
owa ds g een g ow h (1. P oduc ion-based CO2 p oduc i i y; 2. Annual su ace empe a u e;
3. The mean exposu e o he popula ion exposu e o PM2.5; 4. En i onmen ally ela ed ax).
Thanks o he new app oach o he G een G ow h assessmen based on mul ic i e ia
e alua ion, i is possible o au oma e his p ocess and i is epea edly applied. This ul ima ely
p o ides managemen au ho i ies wi h a ool o measu e he ma u i y o he G een Deal no
only in EU coun ies. Based on he p oposed mul i-c i e ia model, I eland is e alua ed as he
land wi h he highes le el o G een G ow h and La ia as he coun y wi h he lowes le el
in he analyzed yea 2020.
Keywo ds: G een Deal, G een G ow h, indica o s, mul ic i e ia decision-making, AHP
(Analy ical Hie a chy P ocess) me hod, OECD.
JEL Classi ica ion: C44, M10.
*
Co esponding au ho , Radek Doskočil – e-mail: [email p o ec ed]
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, 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. © 2022 The Au ho (s).
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The Mul ic i e ia Assessmen o he G een G ow h in he Con ex o he
Eu opean Union’s G een Deal
740 Am i ea u Economic
In oduc ion
The Eu opean G een Deal ( he p io i y o he Eu opean Commission) ep esen s he la ges
Eu opean e o so a o a sus ainable u u e and a solu ion o he clima e c isis. The
Eu opean G een Deal is being d a ed as he cu en sys em s a egy o u he g een g ow h.
The G een Deal is a g ow h s a egy, wi h he G een G ow h and a sus ainable ci cula
economy d i ing i . The sub-s a egies and ini ia i es o he G een Deal help build a mo e
esilien and sus ainable Eu ope and p o ide an in es men en i onmen conduci e o g een
g ow h. Acco ding o he OECD, g een g ow h means suppo ing economic g ow h and
de elopmen while ensu ing ha na u al esou ces con inue o p o ide he esou ces and
en i onmen al se ices on which ou well-being depends and which con ibu e o he
coun y’s p ospe i y (Kasz elan, 2017; Mon ana ella, 2020). A he end o 2019, he EU
commi ed i sel o achie ing clima e neu ali y by 2050, no ably h ough he in oduc ion o
sola and wind ene gy (D’Alessand o e al., 2020).
Fo he Eu opean G een Deal o be success ul, comp ehensi e and sys ema ic echnological,
poli ical, and economic changes a e needed (Pa oussos e al., 2020; Fu a i and Mund, 2021).
In addi ion, hese changes mus be implemen ed (wi h ega d o he de ined ime miles one
2050) in a ela i ely sho ime and wi h limi ed esou ces (pe sonnel, ma e ial, cos s, and
in es men s om public and non-public budge s). All his in addi ion o he condi ions o
oday’s VUCA en i onmen (Vola ile, Unce ain, Complex, Ambiguous), which basically
does no allow us o achie e ou goals by se e al epea ed a emp s (Fassinge e al., 2017).
Filling in he abo e aspec s equi es he applica ion o p ojec managemen o he success ul
implemen a ion o he G een Deal. Mode n p ojec managemen is based on wo key
p inciples. (1) The p inciple o eamwo k plays a c ucial ole in he G een Deal p ojec ,
because he p ojec s akeholde s a e e y di e se. This di e si y is due bo h o he di e si y
o p o essions needed o implemen he p ojec ( echnicians, economis s, manage s, e c.) and
o he in e na ional dimension (EU membe s a es, EU ci izens, EU companies). (2) The
p inciple o a sys ems app oach based on he applica ion o exac me hods in managemen
(Lan anchi e al., 2015).
I ollows om he abo e p inciples ha , o be able o manage he g een con ac p ojec well,
i is necessa y o measu e he pa ial esul s. Me ics o measu ing g een g ow h a e known
and a ailable om public da abases, OECD G een G ow h Da abase (OECD, 2017). These
da abases con ain alues o speci ic c i e ia o e ime and can also be cus omized. These
me ics se ed as a s a ing poin o p oposing a new app oach o assess g een g ow h. The
e alua ion is ypically based on se e al c i e ia ha can ha e di e en weigh s. I leads
he e o e o he p oblem o mul i-c i e ia decision making (MCDM) heo y (Bonissone e al.,
2009; Sedagha , 2013). The MCDM model (Hougaa d and Smilgins, 2016) based on AHP
(Analy ic Hie a chy P ocess) me hod includes sensi i i y analysis o assess he esul ing ank
o al e na i es is employed in his esea ch. This is he main aim o he a icle.
The main con ibu ion o his a icle is he p oposal o a sui able mul ic i e ial app oach o
a complex and sys ema ically assessed G een G ow h in coun ies o he Eu opean Union.
This app oach enables o au oma e e alua ion p ocess and applied i epea edly. In he end,
his p o ides managemen au ho i ies a use ul ool o measu e he ma u i y o he G een Deal
no only in EU coun ies.
Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal
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Vol. 24 • No. 61 • Augus 2022 741
1. Re iew o he scien i ic li e a u e
The esea ch by Houssini and Geng (2021) de eloped he TOPSIS (The Technique o O de
o P e e ence by Simila i y o Ideal Solu ion) model based on he inpu -ou pu amewo k o
conduc a complex e iciency e alua ion o he na ional sys em o Mo occo. The TOPSIS
model is a mul i-c i e ia decision analysis me hod based on he concep ha he chosen
al e na i e should ha e he sho es dis ance om he posi i e ideal solu ion and he longes
dis ance om he nega i e ideal solu ion. Da a en elope analysis (DEA) was applied o
measu e he G een G ow h e iciency o he pe iod o 2000-2018 (Houssini and Geng,
2021).
The esea ch by Lee and Chou applies he OECD G een G ow h moni o ing indica o s in
Taiwan. They use p incipal componen analysis and he analy ic hie a chy p ocess as
weigh ing me hods o calcula e an agg ega ing composi e index and pe o m sensi i i y
es ing. The esul s show ha be ween he yea s 2002-2011, Taiwan has been mo ing owa d
g een g ow h, al hough i has been nega i ely impac ed by i s na u al capi al s ock. This
means ha imp o ing na u al capi al s ock is a key ac o in sus aining G een G ow h in
Taiwan (Lee and Chou, 2018).
A mul i a ia e analy ical app oach was employed in he esea ch by Ga u o a e al. (2021)
ocused on analyzing he condi ion and de elopmen o he OECD coun ies using a se o
G een G ow h indica o s. The uni a ia e and mul i a ia e s a is ical app oach was used o
iden i y he main ac o s o he G een G ow h ma u i y o e wo ime spans – i s pe iod
(yea s 2000-2009) and second pe iod (yea s 2010-2019). The esea ch esul s show ha o
s imulan indica o s, an inc ease was achie ed, while o des imulan a iables, a dec ease
was eached be ween he analyzed pe iods (Ga u o a e al., 2021).
The esea ch by Shen e al. is ocused on measu ing he e olu ion o g een p oduc i i y ha
includes ca bon dioxide emissions based on he Luenbe ge p oduc i i y indica o (B iec and
Ke s ens, 2009). The esea ch was ca ied ou o he pe iod 1971-2011 in 30 OECD
coun ies. The esea ch esul is decomposed o g ow h in g een p oduc i i y a he agg ega e
le el. I sepa a es he p oduc i i y changes in o h ee componen s: (1) echnological p og ess,
(2) echnical e iciency change, and (3) s uc u al e iciency change. This s uc u al e ec
cap u es he he e ogenei y in he combina ion o inpu and ou pu mixes among coun ies,
which can impac p oduc i i y g ow h a a mo e agg ega e le el. This s uc u al e ec is a
no el y. They s a e ha he adi ional TFP (To al Fac o P oduc i i y) index unde es ima es
he G een G ow h, which is mo i a ed by he e ec i e en i onmen al policies o he OECD.
Fo he las 20 yea s, g een p oduc i i y g ow h has been mainly d i en by echnological
p og ess (Shen e al., 2017).
The esea ch by Kasz elan employed Hellwig’s me hod (Roszkowska and Filipowicz-
Chomko, 2021) o he e alua ion o g een g ow h in selec ed OECD coun ies. This
app oach allowed us o decompose he selec ed coun ies in o ou g oups, cha ac e ized by
simila le els o G een G ow h. The esea ch esul s show ha in g oup 1 ( he highes le el
o G een G ow h), he e is only one Denma k. In con as , 12 o he 21 coun ies analyzed
we e in g oup 4 ( he lowes le el o G een G ow h) (Kasz elan, 2017).
The esea ch by Wang e al. (2019) analyses he s ingency o en i onmen al egula ion
policies and measu es g een p oduc i i y g ow h employing an ex ended Slack-Based
Model-Di ec ional Dis ance Func ion (SBM-DDF) app oach based on panel da a om
OECD coun ies in indus ial sec o s. Dynamic panel eg ession in es iga es he impac s and
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The Mul ic i e ia Assessmen o he G een G ow h in he Con ex o he
Eu opean Union’s G een Deal
742 Am i ea u Economic
mechanism o en i onmen al policy s ingency on g een p oduc i i y g ow h in he indus ial
sec o s o OECD coun ies. The esea ch esul s a e: (1) he Po e hypo hesis is alida ed
ha en i onmen al policy has a posi i e impac on g een p oduc i i y g ow h wi hin a ce ain
le el o s ingency (lowe han 3.08); (2) he impac changes o ad e se when en i onmen al
egula ion policy is s ingen o e a ce ain le el, because he compliance cos e ec is highe
han inno a ion o se e ec (Wang e al., 2019).
The s udy by Bak e al. (2019) employed se en indica o s ha cha ac e ized G een G ow h
in OECD coun ies in 2004 and 2015. To iden i y he ela ions be ween hem, he
mul idimensional co espondence analysis wi h a complex ma ix o ma ke s was used. The
OECD coun ies we e decomposed in o ou g oups. These g oups desc ibe di e en le els
o de elopmen in he G een G ow h. The s udy esul s con i med no iceable changes in he
a ea o g een g ow h in he obse ed pe iod o mos coun ies (Bak e al., 2019).
The s udy by Kim e al. (2014) used an OECD amewo k o selec a se o 12 indica o s
p oposed o c oss-coun y compa isons o he G een G ow h s a egies. These indica o s a e
used o he e alua ion o 30 coun ies. The da a ob ained o each indica o is compa ed o
he 10 h pe cen ile o OECD coun ies and is e alua ed on a scale o 1 o 10. This s udy o e s
an app oach o e alua e he o e all e ec s o G een G ow h s a egies and p o ides he
in o ma ion necessa y o ewo k na ional economic plans based on cu en knowledge (Kim
e al., 2014).
He e al. (2022) deal wi h he issue o sus ainable G een G ow h in de eloping economies.
The esea ch ocused on he analysis o economic and en i onmen al pe o mance in 61
de eloping coun ies along he Bel and Road. The g ow h in he o al ac o p oduc i i y is
di ided wi h espec o he economic and en i onmen al con ibu ions. Bo h desi able and
undesi able ou pu s a e conside ed. Some coun ies expe ience s ong economic g ow h,
while en i onmen al pe o mance is slowing G een G ow h. This sugges s ha de eloping
economies should pay a en ion o en i onmen al impac s and p omo e sus ainable
de elopmen by sha ing emission- educ ion echnologies (He e al., 2022).
F om he abo e li e a u e e iew, i is clea ha he au ho s applied a ious app oaches o he
e alua ion o G een G ow h o he coun ies, such as TOPSIS me hod, Luenbe ge
p oduc i i y indica o s, Hellwig me hod, SBM-DDF model, sco ing scale. The e o e, he
esea ch gap is de ined in a sepa a e app oach o G een G ow h e alua ion. The new p oposed
mul i-c i e ia e alua ion app oach, based on he AHP me hod, espec s he ollowing
p inciples:
 Hie a chy p inciple – akes in o accoun all componen s ha a ec he ou come o he
decision p oblem (indi idual elemen s, links be ween elemen s, he in ensi y o in e ac ion
o elemen s); he decision p oblem is ep esen ed by he so-called hie a chical linea s uc u e
(Song and Kang, 2016), which can be modi ied as needed.
 P inciple o no maliza ion – he no maliza ion o e alua ion o all a ian s o all
c i e ia.
 The p inciple o pai wise compa ison – elemen s a e e alua ed in pai -wise
compa ison scale so-called Saa y’s scale, which allows, i necessa y, o e alua e e bal o
symbolic exp essions (quali a i e c i e ia) (Siekelo a e al., 2021).
 Weigh ed a e age p inciple – applied in he p ocess o inal e alua ion (syn hesis).
Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal
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Vol. 24 • No. 61 • Augus 2022 743
2. Resea ch me hodology
2.1 Resea ch design
The esea ch me hodology is based on he undamen al p inciples o scien i ic and esea ch
wo k. The selec ed scien i ic and esea ch me hods a e based on a p ede ined esea ch goal.
The p inciples o sys em me hodology ha combine sys em app oach, sys em hinking,
sys em disciplines, and sys em algo i hms we e applied o he solu ion o a scien i ic p oblem
(Janicek, 2017).
 Sys em app oach – en i y de ini ion: g een g ow h in he con ex o he Eu opean
Union’s G een Deal; aim o mula ion: assessmen o he g een g ow h in he con ex o he
Eu opean Union’s G een Deal; app oach o he en i y: pu pose ul, complex and hie a chical
assessmen o en i ies; cha ac e is ics conside ed by he en i y: assess he s a es and a ge
beha io o en i ies; en i y analysis me hodology: mul ic i e ia decision-making me hods
(MCDM) include sensi i i y analysis.
 Sys ems hinking – applica ion o p og essi e ypes o hinking: analy ical-syn he ic,
c ea i e, complex, and c i ical.
 Sys em disciplines – applica ion o sui able me hods in he ield o modelling, sys ems
heo y, ope a ions esea ch, especially in he ield o MCDM.
 Sys em algo i hms – design o a gene al p ocedu e o assessmen o he g een g ow h
in con ex o he g een deal in he EU coun ies espec ing a sys ems app oach, hinking
discipline.
2.2 Ma e ials
The inpu da a was ob ained om he OECD G een G ow h da abase (OECD, 2017). This
public da abase con ains selec ed indica o s o moni o ing p og ess owa ds G een G ow h
(see Table no. 3). The da abase syn hesizes da a and indica o s ac oss a wide ange o
domains. I d aws on a ange o OECD da abases and ex e nal da a sou ces.
The e a e many compu e so wa e applica ions, e.g., he Expe Choice (2021), C i e ion
Decision Plus (Anon., 2019), which could be used as a ool o sol e MCDM p oblems. In
his s udy, he add-in DAME (Decision Analysis Module o Excel) was used (Pe zina and
Ramik, 2014; Anon., 2021). Compa ed o o he so wa e ools o sol ing mul i-c i e ia
decision-making p oblems, DAME is ee and use - iendly.
2.3 Resea ch p ocedu e
The empi ical esea ch was pe o med as quan i a i e esea ch employing he echnique o
ma hema ical modelling in he a ea o MCDM based on he AHP me hod. The AHP me hod,
de eloped by Saa y (Saa y, 1980; 1988; Rao Tummala and Ling, 1998) is conside ed a well-
known, powe ul, and lexible decision-making echnique o modelling uns uc u ed mul i-
c i e ia p oblems in economy, managemen , socie y, o poli ics (Winkle , 1990). I can help
se p io i ies and make he bes decision when bo h quali a i e and quan i a i e aspec s o a
decision need o be conside ed.

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The Mul ic i e ia Assessmen o he G een G ow h in he Con ex o he
Eu opean Union’s G een Deal
744 Am i ea u Economic
The sensi i i y analysis (T ian aphyllou and Sánchez, 1997) was used o de e mine he mos
c i ical c i e ion in he decision-making p oblem. The mos c i ical c i e ion changes he
anking o he al e na i es in he decision-making p oblem. In his mul i-c i e ia p oblem, he
mos c i ical c i e ion is de ined in he way he in e es is on whe he he indica ion o he bes
( op) al e na i e changes o no . (The second op ion o de ine he mos c i ical c i e ion is he
way he in e es is on changes o he anking o any al e na i e.) On he issue o c i icali y, we
a e connec ed wi h he e m “ he smalles change.” I can be de ined in wo di e en ways.
The i s way is o de ine he smalles change in absolu e e ms. The second way is o de ine
he smalles change in ela i e (pe cen ) e ms. The i s app oach could be misleading because
i does no calcula e he o iginal alue o he weigh o he c i e ia. Fo his eason, i is mo e
meaning ul o use ela i e changes. This app oach will be applied o his p oblem. The ways
o exp essing he mos c i ical c i e ion a e summa ized in Table no. 1.
Table no. 1. The way exp essed by he mos c i ical c i e ion.
The way o exp essing o he change
in he anking o al e na i e
Top al e na i e
Any al e na i e
The way o exp essing o he
smalles change in he weigh s
c i e ia
Absolu e
Absolu e Top
(AT)
Absolu e Any (AA)
Rela i e
(Pe cen )
Pe cen Top (PT)
Pe cen Any (PA)
Sou ce: own p ocessing based on T ian aphyllou and Sánchez (1997)
The esea ch uses he ollowing p ocedu e (see Figu e no. 1).
Figu e no. 1. Resea ch p ocedu e
Sou ce: own p ocessing
De e mining he Resea ch Aim
Iden i ying he Sui able C i e ia
De e mining he C i e ia Weigh s
De e mining he Al e na i es
Apply AHP Me hod
Resul s In e p e a ion and Discussion
Sensi i i y Analysis
Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal
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Vol. 24 • No. 61 • Augus 2022 745
3. Resul s
The AHP me hod is applied o mul i-c i e ia assessing o he le el o he G een G ow h in
con ex o he G een Deal s a egy in he Eu opean Union Coun ies in he case s udy.
3.1 Iden i ica ion o sui able c i e ia
The assessmen o he le el o G een G ow h is based on OECD G een G ow h Indica o s.
These indica o s a e in he OECD G een G ow h da abase (OECD, 2017). I con ains selec ed
indica o s o moni o ing p og ess owa ds G een G ow h. The da abase syn hesizes da a and
indica o s ac oss a wide ange o domains.
The indica o s ha e been selec ed acco ding o well-speci ied c i e ia and embedded in a
concep ual amewo k, which is s uc u ed a ound ou a eas o cap u e he main ea u es o
he G een G ow h:
 En i onmen al and esou ce p oduc i i y indica e whe he economic g ow h is
becoming g eene wi h mo e e icien use o na u al capi al and o cap u e aspec s o
p oduc ion which a e a ely quan i ied in economic models and accoun ing amewo ks.
 The na u al asse base indica es he isks o g ow h om a declining na u al asse base.
 En i onmen al dimension o quali y o li e – indica e how en i onmen al condi ions
a ec he quali y o li e and well-being o people.
 Economic oppo uni ies and policy esponses – indica e he e ec i eness o policies
in deli e ing g een g ow h and desc ibe he socie al esponses needed o secu e business and
employmen oppo uni ies.
Each o he ou main a eas is u he di ided in o se e al sub-a eas, which con ain a se o
speci ic indica o s. Fo mo e de ails, see OECD (2017).
The da abase co e s OECD membe and accession coun ies, EU coun ies ( he membe ship
as o Feb ua y 1s 2020), key pa ne s (including B azil, China, India, Indonesia, and Sou h
A ica), and o he selec ed non-OECD coun ies.
To assess he le el o he G een G ow h in he con ex o EU coun ies ( he membe ship as
o 1 Feb ua y 2020), i was necessa y o ha e da a on he indica o s o all EU coun ies.
Un o una ely, he OECD G een G ow h da abase does no always con ain comple e da a on
hese indica o s o all coun ies. The c i e ia o selec ing sui able indica o s we e he
ollowing aspec s:
 The indica o mus con ain da a o minimally hei es ima ed alues (E, see Table no.
3) o all EU coun ies o 2020 (mos ecen da a) o o 2019 i comple e da a o 2020 a e
no ye a ailable.
 A leas one indica o om each o he ou main a eas mus be selec ed o a
comp ehensi e assessmen .
Fou speci ic indica o s we e selec ed o he o e all assessmen o g een g ow h based on
he abo e c i e ia:
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 P oduc ion-based CO2 p oduc i i y, GDP pe uni o ene gy- ela ed CO2 emissions –
P oduc ion-based CO2 p oduc i i y is calcula ed as he eal GDP gene a ed pe uni o CO2
emi ed (USD/kg). Included a e CO2 emissions om he combus ion o coal, oil, na u al gas,
and o he uels.
 Annual su ace empe a u e, change since 1951-1980 – annual su ace empe a u e
change is measu ed in Celsius deg ees (°C). I is calcula ed as he di e ence be ween he
annual a e age empe a u e (in a gi en yea ) and he a e age annual empe a u e o he 1951-
1980 pe iod.
 Mean popula ion exposu e o PM2.5 – The mean exposu e o he popula ion o ine
pa icula e ma e is calcula ed as he mean annual ou doo PM2.5 concen a ion weigh ed by
he popula ion li ing in he a ea. I is he le el o concen a ion, exp essed in mic og ams pe
cubic me e (μg/m3), o which a ypical esiden is exposed h oughou he yea .
 En i onmen ally ela ed axes, % GDP – en i onmen ally ela ed ax e enue is
exp essed as a pe cen age o GDP. En i onmen ally ela ed axes include (i) ene gy p oduc s
o anspo pu poses (pe ol and diesel) and o s a iona y pu poses ( ossil uels and
elec ici y); (ii) mo o ehicles and anspo (one-o impo o sales axes, ecu en axes
on egis a ion o oad use, and o he anspo axes); (iii) was e managemen ( inal disposal,
packaging, and o he was e- ela ed p oduc axes); (i ) ozone-deple ing subs ances, and ( )
o he en i onmen ally ela ed axes.
These indica o s we e used as he c i e ia o he mul i-c i e ia e alua ion model (see Table
no. 2).
Table no. 2. Summa y o C i e ia
No
C i e ia
Uni s
Symbol
Max/
Min
1
P oduc ion-based CO2 p oduc i i y, GDP pe
uni o ene gy- ela ed CO2 emissions
US dolla s pe
kilog am, 2015
CO
Min
2
Annual su ace empe a u e, change since 1951-
1980
Mic og ams pe
cubic me e
ST
Min
3
Mean popula ion exposu e o PM2.5 ( ine
pa icula e ma e )
Numbe
PM
Min
4
En i onmen ally ela ed axes, % GDP
Pe cen age
ET
Min
Sou ce: own p ocessing based on OECD (2017)
3.2 De e mining Al e na i es
Because ou opic is ocused on he assessmen o he le el o he G een G ow h in EU
coun ies ( he membe ship as o 1s Feb ua y 2020), hese coun ies (27) ep esen he da ase
o al e na i es ( a iables) in he mul i-c i e ia model (see Table no. 3).
Table no. 3. Nume ical summa y o he al e na i es: decision ma ix
C i e ia
Al e na i e
CO (2020)
ST (2020; E)
PM (2019)
RT (2019; E)
Aus ia
7.66
2.36
12.22
2.36
Belgium
6.25
2.55
12.73
2.13
Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal
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Vol. 24 • No. 61 • Augus 2022 747
C i e ia
Al e na i e
CO (2020)
ST (2020; E)
PM (2019)
RT (2019; E)
Czech Republic
4.58
2.36
16.97
2.47
Denma k
11.80
2.62
9.78
3.37
Es onia
5.12
3.60
5.95
3.69
Finland
6.90
3.34
5.64
2.80
F ance
10.41
2.48
11.37
2.32
Ge many
6.80
2.52
11.93
1.77
G eece
5.94
1.45
14.32
1.58
Hunga y
6.75
2.05
16.60
2.28
I eland
13.81
1.10
7.85
1.30
I aly
7.59
1.90
15.85
3.27
La ia
8.37
3.51
12.71
3.37
Li huania
8.92
3.43
10.47
1.94
Luxembou g
8.75
2.63
10.09
1.74
Ne he lands
6.68
2.48
12.03
3.63
Poland
4.43
2.65
22.77
2.44
Po ugal
8.47
1.97
8.18
2.59
Slo ak Republic
6.52
2.07
18.53
2.02
Slo enia
6.20
2.32
17.06
3.58
Spain
8.22
2.02
9.99
1.77
Sweden
15.84
2.94
5.72
2.00
Bulga ia
4.22
2.18
19.93
2.42
C oa ia
7.02
2.11
18.23
4.22
Cyp us
5.23
1.80
15.79
2.63
Mal a
12.09
1.24
13.07
2.58
Romania
7.54
2.29
15.06
2.23
Sou ce: own p ocessing based on OECD (2017).
No e: E – es ima ed alue
3.3 E alua ion o he le el o g een g ow h in EU coun ies using he AHP me hod
The p oposed decision-making model is c ea ed using an add-in DAME (Decision Analysis
Module o Excel) (Pe zina and Ramik, 2014; Anon., 2021). Compa ed o o he so wa e
ools o sol ing mul i-c i e ia decision-making p oblems, DAME is ee and use - iendly.
DAME is used o s uc u e he decision-making p oblem in o c i e ia/sub-c i e ia and
al e na i es, measu e he c i e ia and al e na i es using pai wise compa isons, syn hesize
c i e ia and subjec i e inpu s o a i e a a p io i ized lis o al e na i es.
A ou -s ep decision-making p ocess is p esen ed as ollows.
S ep 1: B eaking down he Decision-Making P oblem
The i s s ep in he AHP me hod is o de elop a hie a chical s uc u e o de ine he decision-
making p oblem. The AHP me hod decomposes he o e all decision aim in o a hie a chic
s uc u e o c i e ia, sub-c i e ia, and al e na i es (Saa y, 1994).
The highes le el o he hie a chy is he aim, i.e., o assess he le el o G een G ow h in he
EU coun ies. Le el 2 ep esen s he c i e ia (CO, ST, PM, ET). Le el 3 con ains he se o
al e na i es, which a e EU coun ies in ou case.
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The Mul ic i e ia Assessmen o he G een G ow h in he Con ex o he
Eu opean Union’s G een Deal
754 Am i ea u Economic
al e na i es, including he possibili y o hei modi ica ion acco ding o he scope o
e alua ion).
 Sys em hinking – an applica ion o analy ical-syn he ic and complex app oach
(no maliza ion p inciple, pai wise compa ison p inciple, weigh ed a e age p inciple
in he con ex o AHP me hod) and c ea i e app oach (choice o c i e ia and
possibili y o hei modi ica ion o decomposi ion in o sub-c i e ia, e c.).
 Sys em disciplines – applica ion o sui able me hods in he ield o modelling, sys ems
heo y, ope a ions esea ch, especially in he ield o MCDM.
 Sys em algo i hms – design o a gene al p ocedu e o assessmen o he g een g ow h
in con ex o he g een deal in he EU coun ies espec ing a sys ems app oach,
hinking discipline. An applica ion o me hods o mul i-c i e ia e alua ion o a ian s
o sol ing mul i-c i e ia decision p oblems in a socio- echnical sys em.
3) Hie a chicali y – he mul ic i e ia model in he con ex o he AHP me hod akes in o
accoun all key componen s ha a ec he ou come o he e alua ion p oblem. The
indi idual elemen s, he links be ween hem, and hei mu ual in ensi y a e quan i a i ely
e alua ed. In ou case, i is a h ee-le el hie a chy: aim – c i e ia – al e na i es ( a ian s).
4) P ac icali y – de elopmen o empla es (including hei modi ica ion) in he en i onmen
o he mos widesp ead MS Excel applica ion using he eely a ailable add-in DAME.
5) Repea abili y – he possibili y o epea ed e alua ion a egula in e als in o de o ob ain
a se o da a ha can be employed as inpu da a o p edic ion.
Respec ing he abo e p inciples inc eases he quali y, eliabili y, and a ailabili y o
measu emen o he p oblem, which is i s main con ibu ion. The esponsible manage s will
ha e a ool a hei disposal o e ec i ely measu e and, he e o e, manage he le el o he
G een Deal s a egy in he con ex o g ow h.
The accu acy o he e alua ion esul s depends mainly on he accu acy o he inpu da a
ob ained om he public da abase and on he selec ion o sui able c i e ia and espec i e sub-
c i e ia. In he case o de ining hei weigh s, hen also on he p e e ences o e alua o s
(expe s). The abo e ac s a e he main limi s o he p oposed model.
Wi h ega d o subjec i i y in he p ocess o de ining he weigh s o he c i e ia, i could be
app op ia e o ans o m he model, e.g., in o a uzzy MCDM o m. An adap i e neu o- uzzy
in e ence sys em (ANFIS) can be also used (Ma jani e al., 2020). The Fuzzy Logic Toolbox
in MATLAB so wa e is ecommended o his pu pose. Inpu da a will be used o aining
p ocess wi h he aim o iden i y he se ing o membe ship unc ion and in e ence ules. This
app oach allows you o wo k wi h ague e ms ha a e commonly used in expe op ions.
This opic will be he aim o he nex esea ch.
Conclusions
The a icle deals wi h he issue o e alua ion o he G een G ow h in he con ex o he G een
Deal s a egy in he coun ies o he Eu opean Union. The esea ch p esen s a new mul i-
c i e ia app oach using he AHP me hod o measu e G een G ow h. The inpu da a was
ob ained om he OECD G een G ow h Da abase. The sample analyzed was he coun ies o

Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal
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Vol. 24 • No. 61 • Augus 2022 755
he Eu opean Union. The p oposed model allows us o e alua e he G een G ow h
comp ehensi ely, sys ema ically, and epea edly.
Repea ed egula measu emen s can ob ain a se o da a o e ime, which can also se e as a
basis o p edic ion. This will p o ide in o ma ion and knowledge o iden i y speci ic
p oblems and implemen he necessa y measu es o minimize he isks associa ed wi h he
success ul implemen a ion o he G een Deal p ojec . As a inal consequence, i s main
mission will be ul illed, i.e., o keep he EU economy compe i i e and sus ainable (see 17
sus ainable de elopmen goals o he UNESCO s udy) in he u u e (Nilsson e al., 2016).
Acknowledgmen s
This a icle was suppo ed by g an No. FP-S-22-7977 “Modeling and op imiza ion o
p ocesses in he business sphe e” o he In e nal G an Agency o B no Uni e si y o
Technology.
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