The Multicriteria Assessment of the Green Growth in the Context of the European Union’s Green Deal

Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal AE 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). AE 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 AE 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 AE 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 AE 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. AE 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 AE 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: AE 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 746 Am i ea u Economic  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 AE 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. AE 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 AE 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. Re e ences Anon. 2019. C i e ium DecisionPlus 3.0. [online] A ailable a : h p://www.in oha es .com/ ih oo /in oha /p oduc s.asp [Accessed 16 Sep embe 2021]. Anon. 2021. DAME – Decision Analysis Module o Excel. [online] A ailable a : h p://www.op .slu.cz/kmme/DAME/en.h ml [Accessed 16 Sep embe 2021]. Bak, I., Cheba, K. and Ziolo, M., 2019. A New App oach o he S udy o Simila i y S uc u e in he A ea o G een G ow h in Oecd Coun ies. In: A. Auzina, ed. Economic Science o Ru al De elopmen 2019. [online] Jelga a: La ia Uni . Li e Sciences & Technologies, pp. 31-38. h ps://doi.o g/10.22616/ESRD.2019.054. Bonissone, P., Subbu, R. and Lizzi, J., 2009. Mul ic i e ia decision making (MCDM): A amewo k o esea ch and applica ions. Compu a ional In elligence Magazine, IEEE, 4, pp. 48-61. h ps://doi.o g/10.1109/MCI.2009.933093. B iec, W. and Ke s ens, K., 2009. The Luenbe ge p oduc i i y indica o : An economic speci ica ion leading o in easibili ies. Economic Modelling, 26(3), pp. 597-600. h ps://doi.o g/10.1016/j.econmod.2009.01.007. D’Alessand o, S., Cieplinski, A., Dis e ano, T. and Di me , K., 2020. Feasible al e na i es o g een g ow h. Na u e Sus ainabili y, 3(4), pp. 329-335. h ps://doi.o g/10.1038/s41893-020-0484-y. Expe Choice, 2021. AHP So wa e o Decision Making and Risk Assessmen . [online] A ailable a : h ps://www.expe choice.com/2021 [Accessed 16 Sep embe 2021]. Fassinge , R.E., Shullman, S.L. and Buki, L.P., 2017. Fu u e Shock: Counseling Psychology in a VUCA Wo ld. Counseling Psychologis , 45(7), pp. 1048-1058. h ps://doi.o g/10.1177/0011000017744645. Fu a i, S. and Mund, E., 2021. Is he Eu opean g een deal achie able? Eu opean Physical Jou nal Plus, 136:1101. h ps://doi.o g/10.1140/epjp/s13360-021-02075-7. Ga u o a, B., Megyesio a, S. and Hudak, M., 2021. G een G ow h in he OECD Coun ies: A Mul i a ia e Analy ical App oach. Ene gies, 14(20):6719. h ps://doi.o g/10.3390/en14206719. AE 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 756 Am i ea u Economic He, R., Balezen is, T., S eimikiene, D. and Shen, Z., 2022. Sus ainable G een G ow h in De eloping Economies: An Empi ical Analysis on he Bel and Road Coun ies. Jou nal o Global In o ma ion Managemen , [online] 30(6). h ps://doi.o g/10.4018/JGIM.20221101.oa1. Hougaa d, J.L. and Smilgins, A., 2016. Risk capi al alloca ion wi h au onomous subuni s: The Lo enz se . Insu ance: Ma hema ics and Economics, 67, pp. 151-157. h ps://doi.o g/10.1016/j.insma heco.2015.12.002. Houssini, K. and Geng, Y., 2021. Measu ing Mo occo’s g een g ow h pe o mance. En i onmen al Science and Pollu ion Resea ch. [online] h ps://doi.o g/10.1007/s11356- 021-15698-1. Janicek, P., 2017. Sys ems Concep ion o P oblem-Sol ing. In: Enginee ing Mechanics 2017. P ague 8: Acad Sci Czech Republic, Ins . The momechanics, pp. 402-405. Kasz elan, A., 2017. G een G ow h, G een Economy and Sus ainable De elopmen : Te minological and Rela ional Discou se. P ague Economic Pape s, 26(4), pp. 487-499. h ps://doi.o g/10.18267/j.pep.626. Kim, S.E., Kim, H. and Chae, Y., 2014. A new app oach o measu ing g een g ow h: Applica ion o he OECD and Ko ea. Fu u es, 63, pp. 37-48. h ps://doi.o g/10.1016/j. u u es.2014.08.002. Lan anchi, M., Gianne o, C., De Pascale, A. and Ho noiu, R.I., 2015. An Applica ion o Quali a i e Risk Analysis as a Tool Adop ed by Public O ganiza ions o E alua ing ‘G een P ojec s’. Am i ea u Economic, 17(40), pp. 872-890. Lee, C.-M. and Chou, H.-H., 2018. G een G ow h in Taiwan – an Applica ion o he Oecd G een G ow h Moni o ing Indica o s. Singapo e Economic Re iew, 63(2), pp. 249-274. h ps://doi.o g/10.1142/S0217590817400100. Ma jani, A., Babanezhad, M. and Shi azian, S., 2020. Applica ion o adap i e ne wo k-based uzzy in e ence sys em (ANFIS) in he nume ical in es iga ion o Cu/wa e nano luid con ec i e low. Case S udies in The mal Enginee ing, 22:100793. h ps://doi.o g/10.1016/j.csi e.2020.100793. Mon ana ella, L., 2020. Soils and he Eu opean G een Deal. I alian Jou nal o Ag onomy, 15(4), pp. 262-266. h ps://doi.o g/10.4081/ija.2020.1761. Nilsson, M., G iggs, D. and Visbeck, M., 2016. Map he in e ac ions be ween Sus ainable De elopmen Goals. Na u e, 534(7607), pp. 320-322. h ps://doi.o g/10.1038/534320a. OECD, 2017. G een G ow h Indica o s 2017. [online] Pa is: O ganisa ion o Economic Co- ope a ion and De elopmen . A ailable a : h ps://www.oecd-ilib a y.o g/en i onmen / g een-g ow h-indica o s-2017_9789264268586-en [Accessed 12 Janua y 2022]. Pa oussos, L., F agkiadakis, K. and F agkos, P., 2020. Mac o-economic analysis o g een g ow h policies: he ole o inance and echnical p og ess in I alian g een g ow h. Clima ic Change, 160(4), pp. 591-608. h ps://doi.o g/10.1007/s10584-019-02543-1. Pe zina, R. and Ramik, J., 2014. Decision Analysis Module o Excel. [online] h ps://doi.o g/10.5281/zenodo.1094721. Rao Tummala, V.M. and Ling, H., 1998. A No e on he Compu a ion o he Mean Random Consis ency Index o he Analy ic Hie a chy P ocess (Ahp). Theo y and Decision, 44(3), pp. 221-230. h ps://doi.o g/10.1023/A:1004953014736. Financial and Compe i ion Implica ions o he Eu opean Union’s G een Deal AE Vol. 24 • No. 61 • Augus 2022 757 Roszkowska, E. and Filipowicz-Chomko, M., 2021. Measu ing Sus ainable De elopmen Using an Ex ended Hellwig Me hod: A Case S udy o Educa ion. Social Indica o s Resea ch, 153, pp. 1-24. h ps://doi.o g/10.1007/s11205-020-02491-9. Saa y, T.L., 1980. The analy ic hie a chy p ocess. New Yo k: McG aw-Hill. Saa y, T.L., 1988. Wha is he Analy ic Hie a chy P ocess? In: G. Mi a, H.J. G eenbe g, F.A. Loo sma, M.J. Rijkae and H.J. Zimme mann, eds. Ma hema ical Models o Decision Suppo , NATO ASI Se ies. Be lin, Heidelbe g: Sp inge , pp. 109-121. h ps://doi.o g/10.1007/978-3-642-83555-1_5. Saa y, T.L., 1994. How o Make a Decision: The Analy ic Hie a chy P ocess. In e aces, 24(6), pp. 19-43. Sedagha , M., 2013. A P oduc i i y Imp o emen E alua ion Model by In eg a ing Ahp, Topsis and Viko Me hods Unde Fuzzy En i onmen (case S udy: S a e-Owned, Pa ially P i a e and P i a e Banks in I an). Economic Compu a ion and Economic Cybe ne ics S udies and Resea ch, 47(1), pp. 235-258. Shen, Z., Boussema , J.-P. and Leleu, H., 2017. Agg ega e g een p oduc i i y g ow h in OECD’s coun ies. In e na ional Jou nal o P oduc ion Economics, 189, pp. 30-39. h ps://doi.o g/10.1016/j.ijpe.2017.04.007. Siekelo a, A., Podho ska, I. and Imppola, J.J., 2021. Analy ic Hie a chy P ocess in Mul iple– C i e ia Decision–Making: A Model Example. SHS Web o Con e ences, 90:01019. h ps://doi.o g/10.1051/shscon /20219001019. Song, B. and Kang, S., 2016. A Me hod o Assigning Weigh s Using a Ranking and Nonhie a chy Compa ison. Ad ances in Decision Sciences, 2016:8963214. h ps://doi.o g/10.1155/2016/8963214. T ian aphyllou, E. and Sánchez, A., 1997. A Sensi i i y Analysis App oach o Some De e minis ic Mul i‐C i e ia Decision‐Making Me hods. Decision Sciences, 28, pp.151-194. h ps://doi.o g/10.1111/j.1540-5915.1997. b01306.x. Wang, Y., Sun, X. and Guo, X., 2019. En i onmen al egula ion and g een p oduc i i y g ow h: Empi ical e idence on he Po e Hypo hesis om OECD indus ial sec o s. Ene gy Policy, 132, pp. 611-619. h ps://doi.o g/10.1016/j.enpol.2019.06.016. Winkle , R., 1990. Decision Modeling and Ra ional Choice – Ahp and U ili y-Theo y. Managemen Science, 36(3), pp. 247-248. h ps://doi.o g/10.1287/mnsc.36.3.247.