A transport policy tool for reduction of CO2 emissions in Finland - Visions, scenarios and pathways using pluralistic backcasting method

T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 2352-1465 © 2015 Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/). Pee - e iew unde esponsibili y o In e na ional S ee ing Commi ee o T anspo Su ey Con e ences ISCTSC doi: 10.1016/j. p o.2015.12.016 A ailable online a www.sciencedi ec .com ScienceDi ec 10 h In e na ional Con e ence on T anspo Su ey Me hods A anspo policy ool o educ ion o CO2 emissions in Finland – Visions, scena ios and pa hways using plu alis ic backcas ing me hod Tuuli Jä i a,*, Anu Tuominen a, Pe i Tapio b, Vilja Va ho b a VTT Technical Resea ch Cen e o Finland L d, FI-02044 VTT, Finland b Finland Fu u es Resea ch Cen e, FI-20014 Uni e si y o Tu ku, Finland Abs ac The educ ion o g eenhouse gas emission was he basis o a Delphi s udy whe e expe opinions abou u u e de elopmen we e asked and used o o m isions o he u u e, and u he elabo a ed o scena ios using a plu alis ic backcas ing me hod. A new inno a i e app oach on how o use and combine me hods and da a om a ious disciplines in scena io modelling, on anspo policy packaging and de e mining pa hways o each he desi ed u u es, is p esen ed o wo o he isions analysed in de ail. This pape ocuses on he di e se me hods and da a used while he scien i ic backg ound o he backcas ing me hod has al eady been published in Tuominen e al. (2014). © 2016 The Au ho s. Published by Else ie B.V. Pee - e iew unde esponsibili y o In e na ional S ee ing Commi ee o T anspo Su ey Con e ences ISCTSC. Keywo ds: anspo ; emission educ ion; impac assessmen ; scena ios; policy packages; backas ing 1. In oduc ion Global wa ming, u banisa ion, secu i y issues, aging popula ion as well as digi alisa ion o ou echnical and se ice en i onmen s a e g and challenges o he anspo sec o . In addi ion, long- e m in es men s and he s ong ole o egula ion a e ypical ea u es o he anspo sys em, posing challenges o s a egic long- e m planning. They call o sys emic inno a ions o ansi ions in anspo sys ems, i.e. a shi om he cu en socio- echnical * Co esponding au ho . Tel.: +358 40 531 1947 E-mail add ess: [email p o ec ed] © 2015 Published by Else ie B.V. This is an open access a icle unde he CC BY-NC-ND license (h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/). Pee - e iew unde esponsibili y o In e na ional S ee ing Commi ee o T anspo Su ey Con e ences ISCTSC 186 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 sys em o a mo e sus ainable one o a long pe iod o ime. S a egic planning and ansi ions can be suppo ed by in e disciplina y, sys emic and in eg a ed esea ch app oaches p esen ing al e na i e isions o he u u e and pa hways o each hem. Tuominen e al. (2014) In a na ional Finnish esea ch p ojec ILARI unded by he Minis y o T anspo and Communica ions he aim was o s uc u e mul iple isions o he u u e on CO2 emissions o anspo in Finland up o he yea 2050 o he use o he policy-make s in hei decision-making p ocess. Fo his pu pose a plu alis ic backcas ing me hod was de eloped and a se o isions o he u u e ha we e ans o med o scena ios o di e en sec o s o anspo was o med. The p ocess was inally complemen ed wi h pa hways om p esen o he u u e. Calcula ions o he ac ual impac s on GHG emissions we e based on ends and o ecas s o bo h anspo beha iou and echnical de elopmen including a wide se o policy packages o achie e he u u es se by he isions. Fo he en i e p ocess om ision o mula ion o anspo beha iou al changes and policy packages o s ee he ans o ma ion om p esen o he desi ed u u e se e al me hods, a el su eys, s a is ics, ends and o he da abases as well as li e a u e o pa ame e s we e used and combined. The o e all s uc u e o he me hod is p esen ed in Figu e 1 and in mo e de ail wi h da a sou ces in Appendix A. This pape concen a es on he me hods and da a sou ces used and how hey we e combined in he di e en phases o he s udy. Figu e 1 The me hod o plu alis ic backcas ing o na ional clima e policy o anspo 2. Se ing he u u e 2.1. Theo y o backcas ing Scena io building p o ides a amily o me hods ha can be used in u u es s udies o de eloping s a egies and pa hways. Many o he scena ios a e cons uc ed om he pas and p esen owa ds he u u e and a e hence o wa d looking. Backcas ing scena ios ins ead look backwa ds om he desi ed u u e (Robinson, 1990; Hi scho n,1980). The majo conce n is no which u u es a e mos likely o occu , bu how o a ain desi able u u es. The wo ypes o 187 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 scena ios a e illus a ed as cases a and b in Figu e 2. The pu pose o his s udy is o de elop a hi d op ion (c), whe e mul iple p e e ed o desi able u u es a e aken as s a ing poin s o he backcas ing exe cise. Hence, we call i plu alis ic backcas ing. In gene al, h ee classes o u u e scena ios ha e been dis inguished by Ve g ag & Quis (2011) answe ing o he ques ions: wha will happen ( end ex apola ions, business as usual scena ios, p obable scena ios); wha could happen ( o ecas ing, o esigh ing, s a egic scena ios) and wha should happen (no ma i e scena ios like hose used in backcas ing). No ma i e scena ios can also be called desi able u u es o isions o he u u e. All o he h ee scena io classes can be made in a o wa d- and backwa d-looking way. Ou s udy is based on a backcas ing app oach ha builds upon Robinson’s (1990) hinking o wo king backwa ds om a pa icula desi ed endpoin o he p esen and es ima ing wha policy measu es would be equi ed o each ha poin . Robinson uses a single end poin , bu he e a me hod o mul iple isions o he u u e is p esen ed, in which he di e en isions will be achie ed, bu wi h di e gen pa hways as p esen ed in Figu e 2. Mul iple isioning o scena io building in backcas ing is a no el app oach in anspo s udies, bu in some o he sec o s i has al eady been used success ully. Figu e 2 Baseline o ecas wi h (a) h ee o wa d-looking scena ios, (b) single ision backcas ing wi h h ee scena ios and (c) plu alis ic backcas ing wi h wo isions and ou scena ios. 2.2. Fo ming he u u es Su eys The goal se by he Finnish Go e nmen is o educe Finland’s g eenhouse gas emissions by 80% om he 1990 le els by 2050 whe eas he EU a ge equi es o a 60% educ ion. The educ ion o GHG emissions was also he base o he u u e scena ios o med o he yea 2050. The isions o he u u e we e o med using expe opinions abou u u e de elopmen and ends ob ained h ough a wo- ounded Delphi s udy o a ound hi y expe s complemen ed by an in e iew ound in be ween (Va ho e al., 2011). The pa icipan s we e asked o gi e hei opinion o wo di e en u u es, he u u e hey conside ed mos p obable and o he u u e hey conside ed mos desi able. Also he desi able u u e had o be ealis ic in he opinion o he esponden as s a ed by Ama a (1981). In addi ion, isions o he u u e o high school s uden s a ound Finland we e ga he ed om hei essays especially w i en o his pu pose o inc easing no el y in he goal se ing. The expe s i s ga e quan i a i e es ima es o olumes o di e en anspo modes in bo h passenge and eigh sec o s: o al CO2 emissions om a ious modes, ehicle densi y, sha e o bio uels, a e age CO2 emissions/km o new passenge ca lee , and GDP. This was ob ained h ough a ques ionnai e su ey ( i s ound) a e which he esponden s we e in e iewed ( ound wo) abou bo h he quali a i e a gumen s ela ed o he quan i a i e es ima es and abou hei b oade iews on he de elopmen o he anspo sec o and clima e issues in Finland in 2020 and 2030. The hi d ound consis ed o an expanded ques ionnai e ha , in addi ion o he p e ious ques ions abou olumes and sha es, con ained many ques ions ega ding policies and d i e s o de elopmen co e ing he yea s 2020, 2030 and 2050. The anonymous answe s o he whole expe panel om he i s ound we e shown in he hi d- ound ques ionnai e, and a summa y pape o he quali a i e a gumen s om he in e iews was sen o he panellis s. Pas P esen Fu u e a) Pas P esen Fu u e b) Pas P esen Fu u e c) 188 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 In he ILARI exe cise only he iews o he yea 2050 we e used al hough he sou ce ma e ial con ained iews o he yea s 2020 and 2030 as well. Al oge he we had 4560 quan i a i e obse a ions in he expe da a ma ix and 1152 obse a ions in he s uden da a ma ix. The quali a i e ma e ial consis ed o 650 pages o ape- eco ded and ansc ibed expe in e iew alk, and abou 50 pages o o iginal essays w i en by he s uden s. Vision o ma ion Unlike he adi ional Delphi s udies, consensus was no he main objec i e he e. Ins ead, he in en ion was o condense he iews o he expe panels in o a small numbe o di e en iews abou he u u e. The me hod belongs o he amily o dissensus based Delphi a ian s by S eine (2009) ha ejec he sea ch o p obabili y and aim a di e si y ins ead (Va ho & Tapio, 2005 and 2013). The i s s ep in his p ocess was o g oup he quan i a i e es ima es om he hi d ound expe ques ionnai e using clus e analysis. Fi s , we g ouped he a iables om he ques ionnai e in o six hemes ( anspo olumes, emissions, economy, ehicles, policies, and o he d i e s). Each a iable wi hin a heme was gi en a weigh desc ibing i s ela i e impo ance in he heme, o example, oad was gi en a highe weigh han ail in passenge anspo . In addi ion, all a iables we e s anda dised o a scale be ween 0 and 100 in o de o make hem p opo ional o each o he . Then, using clus e analysis he a ious answe s we e g ouped o a manageable numbe o clus e s, which can be used in c ea ing al e na i e isions o he u u e (Va ho & Tapio, 2013). This way o g ouping mean ha he answe s o one esponden did no necessa ily end in he same clus e . In his s udy each a iable ecei ed ou o se en al e na i e u u e s a es. The a e age alue o answe s gi en o each ques ion wi hin a clus e was conside ed he clus e cen e. The quali a i e ma e ial, i.e. he in e iew ansc ip s and he s uden s’ essays, was analysed sepa a ely using quali a i e con en analysis. This ma e ial was dis illed o quali a i e a iables and hei al e na i e u u e s a es. Fo example, ‘‘ca ashions’’ was a a iable ha desc ibed wha ype o ca s would become popula . Combining he quan i a i e and quali a i e ma e ial, a able o hemes and u u e s a es o he a iables was p oduced, known as he u u es able (Va ho & Tapio, 2013). This able o ms he basis o scena io cons uc ion. In he able, each ow ep esen s a a iable, such as “passenge ca densi y” o “GDP”. Each a iable has ei he quan i ied ( he clus e cen e) o quali a i e al e na i e u u e s a es, ma ked in he cells o he ow in Figu e 3. Figu e 3 Fu u es able, combining quali y and quan i y. Using he u u es able he al e na i e u u e s a es we e o ganised in o cohe en isions o he u u e by eo ganising he cells showing he al e na i e s a es o a iables on each ho izon al ow so ha a e ical column ep esen s one cohe en ision. In p ac ise, om all plausible combina ions o he a iables eigh di e en isions o 2050 we e selec ed by he esea che s using hei expe opinion, o example in Figu e 3 e ms one ision could be A2, B2, C1, D2 and E1, and ano he A3, B5, C4, D3 and E5 (Va ho e al., 2011). Ou o he eigh isions o med six 189 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 we e based on combined da a, one (“De eloping deg ow h”) on Delphi s udy expe isions only and one (“Co nucopia”) on high school s uden s’ essays only (Tapio e al., 2011). Being based on he esul s o clus e analysis o con en s analysis he isions did no ep esen he hinking o any one expe bu each ision elemen was a combina ion o iews and ideas o a ious expe s. Two o he eigh isions o iginally c ea ed and p esen ed in Table 1 we e chosen by he S ee ing G oup o he ILARI p ojec , consis ing o bo h policy make s and esea che s, o ull analysis ega ding he scena ios and pa hways. Su p isingly, hese isions we e nea ly he ou mos ones, a om he business-as-usual ends: The “U ban Bea ” is a adical ision, based on compac ci ies and high use o ICT, and he “Co nucopia” ision is based on as echnological de elopmen and new anspo solu ions ha help o cu CO2 emissions adically (Tuominen e al., 2012). As he “Co nucopia” ision was o iginally based on quali a i e da a, o he scena io desc ip ion quan i a i e da a om he expe s udy was combined. Table 1 The eigh isions o he u u e o med Name o he ision Desc ip ion U ban Bea The ision is based on compac ci ies and high use o ICT. The economy has g own s eadily. Passenge anspo olume has no g own, bu he e has been a adical modal shi owa ds ail and so modes, dec easing emissions adically. T ansi Finland The economy has g own slowly and se led on he 2020 le el. Passenge anspo has dec eased wi h he simul aneous inc ease o ansi eigh anspo . Eco-mode ni y The economy has g own as e han anspo olumes and he sha e o non-ma e ial consump ion has inc eased. Technologies ha e de eloped as and he sha e o bio uels is high. Small s eps The ision clings o he p esen and any changes ha e been e y cau ious. The economy has g own slowe han be o e. Business as usual Ce ain imp o emen s and new policies ha e been in oduced, bu he ision o he u u e is a he conse a i e. Ma e ial g ow h The economy has g own ai ly as and u ban sp awl has con inued. T anspo olumes ha e con inued o g ow. The ision is pessimis ic in e ms o emission educ ions. Co nucopia The ision is based on adical echnological de elopmen and new anspo solu ions ha ha e helped o cu emissions subs an ially. The economy has g own subs an ially and anspo olumes sligh ly. De eloping deg ow h The economy has become inc easingly se ice-in ensi e and as measu ed by GDP, has s a ed o decline. Road anspo olumes and emissions ha e declined d ama ically. 3. Reaching he desi ed u u e 3.1. Calcula ion me hodology Ha ing he wo di e en u u es o nea ly o y yea s ahead selec ed o de ailed analysis o he scena ios and de ini ion o pa hways we had o answe he ques ion whe he ei he o e en bo h could be eached and how. Fo o ming he pa hways and conduc ing a de ailed analysis o he CO2 educ ion impac s a la ge numbe o di e en da a bases, su eys, ends and o ecas s we e used as well as elas ici ies o p obabili y o change ega ding esponse o anspo policy measu es. In addi ion, echnology de elopmen o bo h he ehicles and ICT and ITS suppo ing emission educ ion we e aken in o accoun . The elemen s o he pa hway de ini ion and impac analysis a e shown in Figu e 4. 190 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 Figu e 4 Elemen s o pa hway de ini ion and impac analysis Analysis was ca ied ou by mode, ip pu pose and use o anspo g oup by calcula ing educ ion ac o s o each policy and policy package, and inally adding all hese up s epwise s a ing om he mos powe ul policy and con inuing wi h addi ional e ec s o he supplemen a y policies. A de ailed desc ip ion o da abases used is p esen ed in Appendix A. 3.2. Baseline scena io o CO2 emissions Fo assessing g eenhouse gas (GHG) educ ion po en ial in 2050 a us wo hy e e ence scena io i.e. he baseline scena io was needed. In ou case his was no a p oblem as in Finland we ha e a na ional anspo emission calcula ion sys em LIPASTO (h p://lipas o. . i/). This unique da abase and calcula ion sys em, suppo ed by ou Minis ies and S a is ics Finland, co e s all modes o anspo and is also used o o icial epo ing o anspo emissions in Finland, as well as o moni o p og ess owa ds anspo and en i onmen al a ge s. P esen ly he sys em p o ides annually upda ed p ojec ions and also a baseline o ecas up o 20 yea s o he u u e. The baseline scena io o he ILARI exe cise up o 2050 was made by upda ing and u he elabo a ing he LIPASTO o ecas in acco dance wi h: • Popula ion o ecas up o 2050 (da a sou ce: S a is ics Finland) • Demog aphics, d i ing licence holding and mobili y beha iou ends (da a sou ces o end analysis: S a is ics Finland and Na ional T a el Su eys) • Ex ended o ecas s o mileage by ehicle ype (da a sou ce o o ecas s: Finnish T anspo Agency) • Baseline o ecas o he echnical de elopmen o he ehicle lee in e ms o CO2 emissions (da a sou ces o o ecas s: Finnish T anspo Sa e y Agency and VTT ca lee model). As can be seen in Figu e 5 he baseline o CO2 emissions is no a business-as-usual o ecas bu includes al eady launched policies ha only begin o esul in emission educ ions o e a pe iod o ime, like he sha e o bio uels in uel mix and he e ec o GHG a ge s se o he Eu opean ca indus y. Tapio e al. (2011) Figu e 5 CO2 emissions o domes ic anspo in Finland 1980–2010 and he Baseline de elopmen o 2011–2050 191 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 3.3. Policies, policy packages and iming Based on he li e a u e (Tuominen e al., 2014) and commen s om he S ee ing G oup o he ILARI-p ojec , policy packages we e cons uc ed o include wo main elemen s. These a e he p ima y measu es, which ac as he leading and s ee ing elemen s o he package, and addi ional measu es, which p omo e he e ec i eness, accep abili y and easibili y o he p ima y measu es (see e.g. Op ic, 2010 and Sessa e al., 2003). In choosing he policies a i s a lis o policy a eas was o med. All he plausible indi idual policy measu es sui able o Finland we e hen lis ed and analysed unde hese a eas. The lis o policy a eas included: 1. Measu es o s op u ban sp awl 2. Measu es o compac communi y s uc u es 3. New s a egic planning model in eg a ing land use, housing, anspo , se ice and business sec o s 4. Dec easing public anspo ees con olled o subsidised by na ional o local au ho i ies 5. Func ional a e ial ne wo k and nodes o public anspo 6. Na ional public anspo in o ma ion se ice 7. A o dable icke p oduc s and new paymen ypes o long dis ance public anspo 8. Dec easing business ip mileage allowances 9. Lowe speed limi s 10. Dec easing wo k ip mileage allowances ( ax allowance o wo k ip mileage as a compensa ion o poo public anspo connec ions in low densi y a eas) 11. Na ional le el oad p icing i.e. kilome e based ca use cha ging 12. Highe pa king ees 13. Company anspo plans 14. En i onmen ally iendly modi ica ions in ca ax policies 15. Raise o uel axes 16. En i onmen ally iendly modi ica ions in hea y goods ehicle axes 17. Inc easing ene gy e iciency o hea y goods ehicles 18. Hyb id and elec ic ehicles o u ban deli e y 19. Reduc ion in he numbe o emp y loads 20. Walking and cycling measu es 21. In es men s in ail in as uc u e 22. In es men s in oad in as uc u e 23. Awa eness ising, campaigns, e c. 24. Emission no ms 25. Ca w ecking schemes In o de o cus omise he policies om he li e a u e, na ional p ac ice and s a egies and o widen he pe spec i e on he iming o implemen a ion and syne gies o con lic s be ween indi idual measu es, a hal -day expe wo kshop was o ganised o ind a common iew on packaging he measu es. The wo kshop b ough oge he expe s om se e al minis ies, anspo adminis a ion and esea ch ins i u es. In esul a ma ix o syne gies and con lic s be ween he measu es was p oduced and a sepa a e “ ime scale” o he policy op ions on he policy a ea le el. The main message was ha mos measu es ha need o be used in o de o achie e he wo isions need o be used as soon as possible. As o he “Co nucopia”, mos immedia e measu es we e inc ease o ene gy e iciency o hea y goods ehicles, inc ease uel axes and emission no ms; whe eas he “U ban Bea ” ision would equi e immedia e ac ion in land-use planning ac i i ies and dec ease o ax allowances o wo k ip mileage. Finally policy a eas and de ailed policy packages o bo h isions “U ban Bea ” and “Co nucopia” we e de e mined. 3.4. Calcula ion o emission educ ions and e alua ion o he e ec s The emission educ ion calcula ion i.e. impac o a measu e was ca ied ou using educ ion ac o s wi h e e ence o he baseline scena io o be de ined by mode, pu pose and pe son g oup o pe son anspo and by mode, indus y sec o and p oduc ype o goods anspo . The educ ion ac o s (o inc ease ac o s e.g. in case o a shi om ca 192 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 o bus, ca -mode emissions a e educed bu bus-mode s ay a he same le el o inc ease) we e calcula ed di ec ly as changes in he ehicle mileage, no as changes in he numbe o ips. The de ailed educ ion ac o s by each policy in oduced we e calcula ed a i s and hen mul iplied o one ac o o each mode. In o ming he educ ion ac o ables he ollowing backg ound da a was used: 1. Elas ici ies and/o p obabili ies o change o di e en anspo use g oups (bo h pe son and goods anspo ) based on na ional esea ch. 2. Mobili y da a by mode and pu pose om na ional a el su eys and goods anspo su eys by indus y sec o espec i ely o de ine he sha e o espec i e mileage. In p ac ice he isions o he u u e we e ans o med in o scena ios di ec ly o he a ge yea 2050 by in oducing one policy measu e a a ime s a ing om he mos e ec i e and accep able p ima y policy measu es and adding o he s om he p ede ined policy package in oduc ion scheme un il eaching he a ge . Emissions educ ion ac o s o each mode (bo h pe son anspo and eigh anspo modes) we e de ined sepa a ely o each policy measu e using elas ici ies om na ional esea ch, i a ailable, bu mos ly using p obabili ies o change based on na ional esea ch and expe opinion. In de ail he educ ions we e calcula ed as ollows: • Elas ici ies om na ional esea ch, i a ailable (e.g. o “Public anspo p icing” a long e m elas ici y o 0.6 o numbe o ips was used oge he wi h an a e age leng h o a ip by pu pose) • Emissions educ ion ac o s we e de ined by mul iplying in luence ac o by p opo ion o mileage a ec ed: o In luence ac o was de e mined as he p obabili y o change o he sha e o anspo use s a ec ed based on na ional esea ch (e.g. Jä i 2009; Jä i & Himanen 2006; Koljonen e al. 2012; MinTC 2009, 2007 and 2004-2007; Nylund 2011; Mäkelä e al. 2008; Rosenbe g e al. 2008 and 2007; Välipi i e al.2011) o P opo ion o mileage by mode and pu pose a ec ed by he measu e (FTA 2006; Jä i & Himanen 2006; OSF 2011). Fo example o he measu e “Conges ion cha ging” p opo ion o mileage was de e mined as u ban ca mileage in g ea e ci ies and he in luence ac o o pu pose “wo k” was 70% and o o he pu poses 25%. • De elopmen o ehicle echnology and use o al e na i e uels we e al eady included in he baseline scena io bu o measu es p omo ing hese (ea lie and wide in oduc ion) na ional li e a u e was used (Nylund, 2011). The le el o powe o in oducing a policy measu e o a majo measu e o he complemen a i y e ec o a side measu e was hen de ined i.e. de e mining he ac ual e ec o he measu e by aking accoun on bo h he powe and syne gies o di e en policy measu es based on li e a u e, na ional case s udies and esul s o he expe wo kshop ( o example measu es “ci y oll ing” and “pa king cha ging” ha e high syne gy, bu “incen i es o low emissions ca s” and “p omo ing walking” ha e less syne gy). In addi ion, as modelled di ec ly o he yea 2050, o measu es de eloping by he ime he phase o he measu e in 2050 was imbedded in he powe o he measu e. Las ly, when app op ia e, he anspo use g oup esponse o he powe o he measu e was aken in o conside a ion. In gene al, he maximum accep able le e o a measu e was used o achie e he maximum CO2 educ ion. Th ee anspo use g oups we e used in e ms o lexibili y o change in espond o he powe o a policy measu e o policy package (Tuominen e al., 2007; MinTC 2004-2007): • olun ee s i.e. ea ly adop e s – no ex a o ce needed, • he majo i y – a g oup ha can be in luenced by ligh posi i e o nega i e policy measu es • lagga ds and opponen s – a g oup ha mus be o ced o he desi ed change. Finally, elas ici ies om he li e a u e we e used o con ol and check-up o hese na ional calcula ions. 4. Resul s In he ILARI s udy he wo isions selec ed o backcas ing i.e. inding he pa hways and policies o lead o he desi ed u u e we e “U ban Bea ” and “Co nucopia” isions ha bo h u ned ou o equi e subs an ial in es men s ha migh be un ealis ic. Wi h hei choice he decision-make s who made he selec ion, howe e , wan ed o see inno a i e, nea ly u opian esul s which in p ac ice could be ealised i he economic equi emen s could be ul illed. Taking his, i would ha e been in e es ing o see wha kind o policy packages could ha e led o he “De eloping deg ow h” scena io o he slow g ow h op ions. 193 Tuuli Jä i e al. / T anspo a ion Resea ch P ocedia 11 ( 2015 ) 185 – 198 The a ge s and inal esul s o he wo isions selec ed o de ailed analysis a e shown in Figu e 6. Bo h o he isions we e s a ed o each he EU a ge o 60% educ ion, bu he “U ban Bea ” ision eached e en he na ional a ge o 80% educ ion while he “Co nucopia” ision was e y close. The scena io analysis esul s showing calcula ed po en ial o CO2 emission educ ion con i med ha he a ge s could be eached, he EU a ge o 60% e en i somewha weake assump ions o policy impac s a e used. Howe e , ega ding he na ional a ge , he oles change while going om isions o ac ual scena io modelling and de ini ion o pa hways using anspo policy measu es and packages. Only he “Co nucopia” scena io eaches he na ional a ge o 80% by using s ong assump ions o policy measu es (in oduc ion wi h high powe , e.g. high p ices and axa ion, s ic egula ion) and impac s acco dingly, he “U ban Bea ” scena io being e y close. Figu e 6 T anspo CO2 emissions in Finland in 2010, Baseline de elopmen and he a ge s o he isions “Co nucopia” and “U ban bea ” by 2050; and calcula ed po en ial CO2 emissions o he wo scena ios using policy packages (weak/s ong usage) and pa hways by 2050. The policy packages inally selec ed o ul il he adical ision “U ban Bea ” based on compac ci ies and high use o ICT and he “Co nucopia” ision based on adical echnological de elopmen and new anspo solu ions ha help o cu CO2 emissions adically a e shown in Table 2. The se o measu e packages needed is much sho e o “Co nucopia” han o “U ban Bea ” as in he o me boos ed echnology de elopmen (e.g. mo o , uel and in elligen echnology) akes ca e o he majo pa o he educ ions (75%). Fo “U ban Bea ” he i s ou policy packages oge he , wi h p icing as he mos e ec i e one, accoun o a ound 40% o he educ ions, echnology de elopmen alone o ano he 40% and eigh measu es o he es . Table 2 Policy Packages o U ban Bea and Co nucopia isions.   !!- %# #*"   !!   #     %   " # "   "   ! ! #*"  #      "   ! !  As an example o ull con en s o a policy package policies included in Co nucopia “Low-emission ehicles” policy package a e gi en in Table 3. Taxa ion measu es e e o imp o ing he p esen al eady CO2-based axa ion policies, and ca w ecking ees a e in oduced o enewal o he p esen ly e y old ca lee (e.g. a p esen a ial o