The transition to bioeconomy and its implications for sustainable development: The case of Germany

Wen, Lanjiao
Doc o al Thesis
The ansi ion o bioeconomy and i s implica ions o
sus ainable de elopmen : The case o Ge many
Sugges ed Ci a ion: Wen, Lanjiao (2024) : The ansi ion o bioeconomy and i s implica ions o
sus ainable de elopmen : The case o Ge many, Uni e si ä s- und Landesbiblio hek Sachsen-Anhal ,
Halle (Saale),
h ps://nbn- esol ing.de/u n:nbn:de:gb :3:4-1981185920-1203362 ,
h ps://hdl.handle.ne /1981185920/120336
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/312663
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The ansi ion o bioeconomy and i s implica ions o
sus ainable de elopmen : The case o Ge many
Disse a ion
Zu E langung des
Dok o g ades de Ag a wissenscha en (D .ag .)
de
Na u wissenscha lichen Fakul ä III
Ag a ‐ und E näh ungswissenscha en, Geowissenscha en und In o ma ik
de Ma in‐Lu he ‐Uni e si ä Halle‐Wi enbe g
o geleg on
F au Lanjiao Wen
Gu ach e :
(1) P o . D . Al ons Balmann
(2) P o . D . José Gil Roig
Tag de Ve eidigung:
9. Dezembe 2024
i
Acknowledgmen
Finally, a e na iga ing he challenges o he COVID-19 pandemic and welcoming my child, I app oach
he inal pa o his disse a ion wi h mixed emo ions. This disse a ion ma ks he culmina ion o my second
PhD jou ney, a pa h illed wi h mo i a ion, ha dship, encou agemen , us a ion, and ul ilmen . This
jou ney has s eng hened my esilience, helping me ealize ha d eams can be achie ed h ough un emi ing
e o s. I ha e so much o be g a e ul o , pa icula ly o hose who ha e helped me each his poin .
Fi s and o emos , I would like o exp ess my deepes g a i ude o my supe iso s. I am ex emely hank ul
o my “Dok o a e ”, P o . D . Al ons Balmann, o allowing me o pu sue his wo k wi h scien i ic and
inancial suppo . P o . D . Al ons Balmann consis en ly iden i ied a eas o imp o emen in my wo k and
o e ed cons uc i e sugges ions. I am also deeply g a e ul o my daily supe iso , D . Zhanli Je y Sun,
who o e ed me g ea eedom o explo e my ideas and suppo ed me wi h in aluable ad ice. Thank you
o adop ing me when I was helpless and holding me up when I was climbing he moun ain o challenges.
You a e no only my supe iso bu also my spi i ual men o . I canno imagine inalizing his disse a ion
wi hou you suppo .
Second, I would like o since ely hank my collabo a o s. I hank my collabo a o s, D . Lioudmila Cha alo a,
P o . D . Anlu Zhang, D . I . F ans He mans, P o . D . Van Bu sic, P o . D .Zhiyong Fox Hu, D . Xin Gao,
and D . Wen ang Pu o hei suppo i e and cons uc i e inpu . Special hanks o D . Lioudmila Cha alo a
who augh me a lo in science and w i ing skills, P o . D . Anlu Zhang, one o my Thesis Ad iso y
Commi ee (TAC) membe s, o you con inued suppo and help ul ad ices ega ding my esea ch ca ee
and D . I . F ans He mans, one o my TAC membe s, o you insigh ul commen s ega ding he
bioeconomy and con inued suppo and help.
ii
Thi d, I would also like o exp ess my app ecia ion o my colleagues a IAMO. Thanks o he colleagues in
he Bioeconomy g oup and China g oup whe e I ound my esea ch amily. I would acknowledge he
suppo om he BSE membe s, pa icula ly P o . D . Daniel Mülle , D . F anziska Hau , D . S ephan
B osig, D . F anziska Appel, D . Changxing Dong, D . Flo ian Schie ho n, D . Lijuan Miao, D . B ian
Beadle, M . Flo ian Hein ich, and Ms. Claudia G ü zmache . I also app ecia e he iendly help om o he
IAMO colleagues, especially D . Sa a bek El aza o , D . Lau a Mo i z, D . Sö en P ehn, D . Nodi
Djanibeko and P o . D . Thomas He z eld. Thanks o IAMO o suppo ing my s ay and p o iding me a
amily- iendly wo king a mosphe e.
In addi ion, I am g a e ul o he suppo and assis ance o D . Milad Abbasiha o eh, P o . D . Sebas ian
Lakne , P o . D . Jus us Wessele and D . Yan Jin. I ex end hanks o D . Milad Abbasiha o eh o
p o iding me he da a and eaching me how o ex ac i ; o P o . D . Sebas ian Lakne o supplying da a
on EFA; o P o . D . Jus us Wessele and D . Yan Jin o hei aluable eedback on my esea ch idea abou
bioeconomy. The e a e coun less colleagues who ha e helped me along he way, a oo many o name
indi idually. Howe e , please know ha each o you holds a special place in my hea .
My las g a i ude is o my amily membe s and iends, who know I am no pe ec ye lo e me
uncondi ionally. Many hanks o my husband, L meng, o you las ing lo e, encou agemen and
in ol emen in looking a e ou amily membe s when I was absen ; o my lo ely daugh e , Yogu , o
gi ing me huge cou age o ace li e’s challenges wi hou hesi a ion; o my pa en s, o se ing me on he
pa h o pu sue my d eams, aising me, and helping o aise my daugh e ; o my pa en s-in-law o assis ing
us in se ing up ou home. Thanks o my ela i es and iends in Eu ope and China o you companionship
and suppo . I also hank mysel o ne e gi ing up.
Though my second PhD jou ney is nea ing i s end, my esea ch ca ee is a om o e . I will con inue
mo ing o wa d, always s i ing o become a be e e sion o mysel .
Halle (Saale), Sep embe 2024 Lanjiao Wen

iii
Summa y
Ensu ing ha clima e neu ali y, he bioeconomy, and economic compe i i eness go hand in hand has
become a key Eu opean goal o u u e sus ainable de elopmen . Ge many, as one o he leading coun ies
in he mode n bioeconomy, aims o boos i s bioeconomy as a key s a egy o achie e he Sus ainable
De elopmen Goals (SDGs). The ansi ion o bioeconomy depends no only on a su icien biomass supply
bu also on suppo ing echnological and ins i u ional inno a ions. In Ge many, he ole ha echnological
inno a ion can play in ca bon emissions educ ion in he ag icul u al sys em is a om clea . Cu en ly,
Ge many has low R&D p oduc i i y due o a gene al p oduc ion ac o misma ch and low alloca ion
e iciency. Addi ionally, li le is cu en ly known abou he pe o mance o ins i u ional inno a ion in he
bioeconomy. The e o e, a be e unde s anding o he eal impac s o echnological and ins i u ional
inno a ion on sus ainable de elopmen is c i ical o suppo ing policymaking and guiding he ansi ion o
bioeconomy.
This disse a ion ocuses on he bioeconomy in Ge many, examining he mechanisms by which
echnological and ins i u ional inno a ions in luence, p omo e, and suppo i s de elopmen . Speci ically,
his disse a ion i) e alua es he po en ial impac o R&D in es men s on ca bon emissions h ough he
dynamic in e ac ions among ag icul u al ca bon subsys ems using a sys em dynamics modelling app oach
based on sec o al da a (Chap e II), ii) es ima es he po en ial mi iga ion e ec s o echnological inno a ion
on ca bon emissions using an ex ended Spa ial Du bin Model based on 401 NUTS-3 le el panel da a
(Chap e Ⅲ), and ⅲ) examines he impac s o bioclus e s, ep esen ing egional ins i u ional inno a ion in
Ge many, on sus ainable pe o mance h ough he use o a supe slacks-based measu e (supe -e iciency
SBM), a se ies o quasi-na u al expe imen s, and a media ing model based on 401 NUTS-3 le el panel da a
(Chap e Ⅳ).
i
Chap e II epo s he modelling and analysis o a ious scena ios, whe e he simula ions o he dynamic
in e ac ions in he ag icul u al ca bon sys em om 2020 o 2050 sugges ha R&D in es men s can ha e a
mi iga ion e ec on ag icul u al ca bon emissions bo h di ec ly and indi ec ly, wi h he di ec e ec being
mo e signi ican . The esul sugges s ha inc easing he allow land, imp o ing he ci cula economy, and
inc easing R&D in es men a e e ec i e s a egies o educing ne ca bon emissions. These s a egies can
p o ide an e icien and mo e sus ainable pa hway o he ansi ion o bioeconomy in Ge many.
In Chap e III, he esul s o he s udy ega ding he implica ion o a o es -based bioeconomy on ca bon
emissions a e p esen ed and sugges ha echnological inno a ions in a o es -based bioeconomy can educe
ca bon emissions h ough p omo ing indus ial upg ading and c ea ing job oppo uni ies ela ed o he
bioeconomy in local a eas. Addi ionally, i can lowe ca bon emissions indi ec ly in neighbou ing a eas
h ough he spillo e e ec s o indus ial upg ading and he size o he bioeconomy. These indings
highligh he need o a coo dina ed app oach o align echnological inno a ion (as indica ed by he numbe
and applica ion o pa en s), employmen popula ion, and indus ial ansi ion s a egies.
Chap e IV in es iga es he po en ial e ec s o bioclus e s on g een o al ac o p oduc i i y (GTFP), and
he esul s indica e ha de eloping bioclus e s, including bo h Bio egions and g een clus e s, would ha e
posi i e e ec s on GTFP, bo h di ec ly and indi ec ly, essen ially h ough echnological inno a ion and
ma ke agglome a ion. Fu he mo e, i e eals ha di e en ypes o bioclus e s ha e he e ogeneous
impac s on GTFP, wi h he g ea es con ibu ion a ising om chemical g een clus e s.
By analysing a ious aspec s o he bioeconomy de elopmen and i s implica ions, he indings in his
disse a ion con ibu e o he ield and p o ide insigh s ha can in o m and suppo ongoing and u u e
scien i ic and policy ac ions ha guide he ansi ion o bioeconomy in Ge many.
Keywo ds: Bioeconomy; Technological inno a ion; Ins i u ional inno a ion; Bioclus e s; Ca bon
emissions; Mi iga ion e ec s; Dynamic in e ac ions; Spillo e e ec s; GTFP; Ge many
Zusammen assung
Die Siche s ellung, dass Klimaneu ali ä , Bioökonomie und wi scha liche We bewe bs ähigkei Hand in
Hand gehen, is zu einem zen alen eu opäischen Ziel ü eine nachhal ige Zukun sen wicklung gewo den.
Deu schland, als eines de üh enden Lände in de mode nen Bioökonomie, ziel da au ab, seine
Bioökonomie als Schlüssels a egie zu E eichung de Ziele ü nachhal ige En wicklung zu ö de n. De
Übe gang zu Bioökonomie häng nich nu on eine aus eichenden Biomasse e so gung ab, sonde n auch
on de Un e s ü zung echnologische und ins i u ionelle Inno a ionen. In Deu schland is die Rolle, die
echnologische Inno a ion bei de Reduzie ung on landwi scha lichen Kohlens o emissionen spielen
kann, noch unkla . De zei weis Deu schland eine ge inge F&E-P oduk i i ä au g und eines allgemeinen
Miss e häl nisses de P oduk ions ak o en und eine nied igen Alloka ionse izienz au . Zudem is wenig
übe die Leis ungs ähigkei ins i u ionelle Inno a ionen in de Bioökonomie bekann . Dahe is ein
besse es Ve s ändnis de a sächlichen Auswi kungen on echnologischen und ins i u ionellen
Inno a ionen au die nachhal ige En wicklung en scheidend ü die Un e s ü zung de poli ischen
En scheidungs indung und die S eue ung des Übe gangs zu Bioökonomie.
Um die genann en Punk e zu ad essie en, konzen ie sich diese Disse a ion au die Bioökonomie in
Deu schland und un e such die Ein lussmechanismen echnologische und ins i u ionelle Inno a ionen bei
de Fö de ung und Un e s ü zung de Bioökonomie. Konk e bewe e diese Disse a ion: (1) den
po enziellen Ein luss on F&E-In es i ionen au die Kohlens o emissionen du ch die dynamischen
In e ak ionen zwischen landwi scha lichen Kohlens o subsys emen mi hil e eines Sys emdynamik-
Modellie ungsansa zes au G undlage sek o ale Da en (Kapi el II), ii) die po enziellen Minde ungse ek e
echnologische Inno a ionen au Kohlens o emissionen mi hil e eines e wei e en Spa ial Du bin-
Modells basie end au Panelda en au NUTS-3-Ebene (Kapi el III); und iii) die Auswi kungen on
Bioclus e n, die egionale ins i u ionelle Inno a ionen in Deu schland ep äsen ie en, au die nachhal ige
i
Leis ung du ch den Einsa z eines Supe Slacks-basie en Maßes (supe -e izien es SBM), eine Reihe on
quasi-na ü lichen Expe imen en und ein Media ionsmodell basie end au Panelda en au NUTS-3-Ebene
(Kapi el IV).
Kapi el II be ich e übe die Modellie ung und Analyse e schiedene Szena ien, bei denen die
Simula ionen de dynamischen In e ak ionen im landwi scha lichen Kohlens o sys em on 2020 bis 2050
da au hinweisen, dass F&E-In es i ionen sowohl di ek als auch indi ek eine Minde ung de
landwi scha lichen Kohlens o emissionen bewi ken können, wobei de di ek e E ek signi ikan e is .
Das E gebnis leg nahe, dass die E höhung de B ach läche, die Ve besse ung de K eislau wi scha und
die E höhung de F&E-In es i ionen wi ksame S a egien zu Reduzie ung de Ne o-
Kohlens o emissionen sind. Diese S a egien können einen e izien en und nachhal ige en Weg ü den
Übe gang zu Bioökonomie in Deu schland bie en.
In Kapi el III we den die E gebnisse de S udie übe die Auswi kungen eine wald-basie en Bioökonomie
au die Kohlens o emissionen p äsen ie und legen nahe, dass echnologische Inno a ionen in eine wald-
basie en Bioökonomie die Kohlens o emissionen du ch die Fö de ung de indus iellen Au we ung und
die Scha ung on A bei splä zen im Zusammenhang mi de Bioökonomie in lokalen Gebie en eduzie en
können. Zudem können sie indi ek die Kohlens o emissionen in benachba en Gebie en du ch die
Spillo e -E ek e de indus iellen Au we ung und die G öße de Bioökonomie senken. Diese E kenn nisse
un e s eichen die No wendigkei eines koo dinie en Ansa zes zu Abs immung on echnologische
Inno a ion (angezeig du ch die Anzahl und Anwendung on Pa en en), Beschä igung, Be ölke ung und
indus iellen Übe gangss a egien.
Kapi el IV un e such die po enziellen Auswi kungen on Bioclus e n au die g üne o ale
Fak o p oduk i i ä (GTFP) und die E gebnisse zeigen, dass die En wicklung on Bioclus e n,
einschließlich Bio egionen und g ünen Clus e n, posi i e Auswi kungen au die GTFP sowohl di ek als
auch indi ek haben wü de, im Wesen lichen du ch echnologische Inno a ion und Ma k agglome a ion.
xiii
Lis o ables
Table 2.1: Coe icien s o ca bon emissions calcula ion ........................................................................... 26
Table 2.2: Rela i e e o s in he simula ion (%) ......................................................................................... 28
Table 2.3: Scena io schemes o di e en scena ios ................................................................................... 30
Table 3.1: Desc ip ions o he a iables ...................................................................................................... 52
Table 3.2: Mo an’s I alues o ne ca bon emissions om 2000 o 2021 ................................................... 60
Table 3.3: Es ima ion esul s om he Spa ial Du bin Model and i s ex ensions ....................................... 61
Table 3.4: Di ec e ec , indi ec e ec , and o al e ec o he model pa ame e s...................................... 64
Table 4.1: Inpu -ou pu indica o s o GTFP .............................................................................................. 78
Table 4.2: Desc ip i e s a is ics o a iables .............................................................................................. 83
Table 4.3: Es ima ion esul s o SDiD model o Bio egions ..................................................................... 88
Table 4.4: Es ima ion esul s o SDiD model o g een clus e s ................................................................. 89
Table 4.5: PSM-SDiD eg ession esul s .................................................................................................... 93
Table 4.6: Media ing eg ession esul s o Bio egions .............................................................................. 94
Table 4.7: Media ing eg ession esul s o g een clus e s ......................................................................... 96
Table 4.8: Reg ession esul s o DDD model o Bio egions ..................................................................... 97
Table 4.9: Reg ession esul s o DDD model o g een clus e s ................................................................. 99

xi
Lis o igu es in he appendix
Figu e A. 1: Resul s o Placebo es ......................................................................................................... 150
x
Lis o ables in he appendix
Table A. 1: Main a iables and pa ame e indexes in he SD model du ing 2000-2050 .......................... 139
Table A. 2: Scena io design ...................................................................................................................... 146
Table A. 3: Bioclus e classi ica ion a he 3 digi le el ........................................................................... 146
x i
Lis o abb e ia ions
BMBF
Ge man Fede al Minis y o Educa ion and Resea ch
BMEL
Ge man Fede al Minis y o Food and Ag icul u e
BMELV
Ge man Fede al Minis y o Food, Ag icul u e and Consume P o ec ion
BMU
Ge man Fede al Minis y o he En i onmen , Na u e Conse a ion and Nuclea Sa e y
CAP
Common Ag icul u al Policy
EC
Eu opean Commission
EFA
Ecological Focus A ea
Eu os a
Eu opean S a is ical O ice
EEA
Eu opean En i onmen Agency
DEA
Da a en elopmen analysis
DDD
Di e ence in Di e ence in Di e ences
SDiD
S agge ed Di e ence in Di e ences
PSM-SDiD
P opensi y Sco e Ma ching- S agge ed Di e ence in Di e ences
FAO
Food and Ag icul u e O ganiza ion
GHG
G eenhouse Gas
GTFP
G een o al ac o p oduc i i y
IPCC
In e go e nmen al Panel on Clima e Change
NCE
Ne ca bon emissions
NUTS
Nomencla u e o e i o ial uni s o s a is ics
R&D
Resea ch and De elopmen
SD
Sys em Dynamic
Sd
S anda d de ia ion
SDG
Sus ainable De elopmen Goal
SDM
Spa ial Du bin Model
1
1 In oduc ion
De eloping he bioeconomy has become a key s a egy o acili a ing he ansi ion owa ds sus ainabili y
in he Eu opean Union (EU) and in many o he egions wo ldwide. Many coun ies ha e launched
bioeconomy s a egies, anging om dedica ed bioeconomy s a egies o be- ela ed s a egies and dedica ed
be-s a egies (see Figu e 1.1). Figu e 1.1 displays an o e iew o he dis ibu ion o bioeconomy s a egies
a ound he wo ld, indica ing coun ies whe e s a egies a e al eady in place o a e unde de elopmen .
Ge many is included in he igu e as one o he coun ies ha has al eady launched a dedica ed bioeconomy
s a egy.
Figu e 1.1: Bioeconomy s a egies in place o unde de elopmen a ound he wo ld
Sou ce: bioökonomie a (2018). “In e na ional bioökonomies a egien”.
h ps://bioökonomie a .de/bioökonomie/in e na ional.
2
Al hough he e m “bioeconomy” i s s a ed o become popula in he ea ly 2000s, he concep o he
bioeconomy is s ill ela i ely new and i is conside ed o s ill be in i s ea ly g ow h s age. The concep o
he bioeconomy has a mul i ace ed b ead h and dep h o meaning, a ying ac oss pa adigms, disciplines,
and coun ies. While de ini ions o he bioeconomy may di e , hey ypically sha e many simila i ies
(Wessele and on B aun, 2017). Acco ding o he Uni ed Na ions Food and Ag icul u e O ganisa ion
(FAO), he bioeconomy e e s o “ he p oduc ion, use, and conse a ion o biological esou ces, including
ela ed knowledge, science, echnology, and inno a ion o p o ide in o ma ion, p oduc s, p ocesses, and
se ices o all economic sec o s wi h he aim o mo ing owa ds a sus ainable economy” (FAO, 2018). The
bioeconomy was de ined by McCo mick and Kau o (2013) as an economy whe e he basic building blocks
o ma e ials, chemicals, and ene gy a e de i ed om enewable biological esou ces. In he policy
amewo k o he Eu opean Union, he bioeconomy is ega ded as a key componen o a aining sma and
g een g ow h (EC, 2012). Acco ding o he Eu opean Commission, he bioeconomy “encompasses he
p oduc ion o enewable biological esou ces and hei con e sion in o ood, eed, bio-based p oduc s, and
bioene gy. This includes ag icul u e, o es y, ishe ies, ood, pulp, and pape p oduc ion, as well as pa s
o he chemical, bio echnological, and ene gy indus ies” (EC, 2012). This de ini ion is widely accep ed by
academic and poli ical communi ies all a ound he wo ld. Wi h he goals o ensu ing ood secu i y,
managing deple ing na u al esou ces sus ainably, educing he dependence on non- enewable esou ces,
adap ing o clima e change, and c ea ing job oppo uni ies, he bioeconomy aims o con ibu e o in elligen ,
sus ainable, and inclusi e g ow h ha will allow he ansi ion owa ds a g een economy (OECD, 2011a, b;
2016).
1.1 Backg ound: The ansi ion owa ds bioeconomy in Ge many
1.1.1 The bioeconomy in Ge many
The de elopmen o he bioeconomy in Ge many is being d i en by policy ini ia i es aimed a mode nizing
he economy in a sus ainable, en i onmen ally esponsible and socie ally sensi i e manne . Wi h he aim
o de elop a c oss-sec o al, knowledge and bio-based economy, he bioeconomy in Ge many began wi h

3
he es ablishmen o he Bioeconomy Council in Janua y 2009. The council was es ablished by he Fede al
Minis y o Educa ion and Resea ch (BMBF) and he Fede al Minis y o Food, Ag icul u e, and Consume
P o ec ion (BMELV) and was ega ded as an independen ad iso y boa d o he Ge man Fede al
Go e nmen . In 2010, Ge many published he Na ional Resea ch S a egy Bioeconomy 2030, which was
designed by he Bioeconomy Council, becoming one o he i s coun ies o ou line i s na ional bioeconomy
s a egy. In 2013, Ge many implemen ed he Na ional Policy S a egy Bioeconomy, se ing ano he
impo an miles one o he bioeconomy. In ea ly 2020, Ge many launched he new Na ional Bioeconomy
S a egy, which laid down guidelines o policies on he bioeconomy as well as he measu es o
implemen a ion. Wi h hese and la e ini ia i es, Ge many has se a pionee ing pace as one o he i s
coun ies o o mally se ou and pu sue a bioeconomy s a egy in line wi h he EU F amewo k P og amme
o Resea ch and Inno a ion and la e in 2012, he EU Bioeconomy S a egy.
Al hough e olu iona y in hei ambi ion o sus ainably ans o m he en i e socie y, hese s a egies s a ed
wi h a s ep backwa ds, namely by e isi ing he po en ial o plan -based biomass. In Ap il 2009, he
Na ional Biomass Ac ion Plan was launched (BMELV/BMU, 2009; Go en and Pa one, 2015; Hagemann
e al., 2016). Alongside 2009 amendmen s o he Renewable Ene gy Sou ces Ac and a boom in enewable
elec ici y up ake, his plan de ined o es and ag icul u al biomass as one o he mos p omising domes ic
enewable ene gy sou ces ha could signi ican ly con ibu e o alue c ea ion, especially in u al a eas
(T oos e al., 2015). The plan en isaged a la ge-scale expansion o bio-based ene gy, including ag icul u al
uels. Recen Eu opean Union s a egy pape s on biodi e si y (EC, 2020b) and ood sys ems (EC, 2020c),
along wi h ecommenda ions by he Na ional Academy o Sciences (Leopoldina, 2020), ha e u he
cla i ied he ole o biomass in he bioeconomy.
Ala med by he inc easingly pessimis ic p ojec ions o clima e de elopmen , soil, wa e , and ai quali y,
as well as by he no iceable consequences o unsui able de elopmen pa hs, ecen deba e has begun o shi
he ocus om al e na i e models o economic g ow h o p io i izing en i onmen al p o ec ion. The
Biodi e si y and Food o Fo k s a egies (EC, 2020b; 2020c), he co e componen s o he Eu opean G een
4
Deal (EC, 2019), se ime-bound a ge s o he expansion o na u e conse a ion a eas, wi h he aim o
achie ing a mland biodi e si y and land deg ada ion neu ali y wi hin a decade. The Leopoldina s esses
he majo ole o ag icul u e in educing ca bon emissions and biodi e si y loss and ecommends e en mo e
p o ound and immedia e ac ions a e needed (Leopoldina, 2020). These ecommenda ions, including
minimizing he use o e ilize s, pes icides, and he bicides, a la ge-scale shi o o ganic a ming, and
limi ing a mland o bio uels and animal eed p oduc ion, ha e di ec implica ions o non- ood biomass
p oduc ion.
Despi e he con lic ing goals o sus ainabili y and economic g ow h e lec ed in egional, na ional, and
sup ana ional agendas, he bioeconomy is gaining momen um along a ious dimensions (Bell e al., 2018).
In 2005, he sha e o he bioeconomy in Ge many accoun ed o 3.9% o g oss alue added and 5.2% o he
labou o ce, while in 2019 hese igu es had isen o 19.9% and 13.5%, espec i ely (Bioeconomy Council,
2010; BMBF, 2020). This g ow h has been d i en by signi ican esea ch unding (EUR 20.3 billion in
2020, c . BioS ep (2016)), in es ed in “mapping and enginee ing he uncha ed e i o ies” o he echnical
and bio echnological knowledge and making hem ma ke able (Aguila e al., 2018). The ans o ma ion o
he economy and especially o he chemical sec o away om ossil-based esou ces (Schü e, 2018), along
wi h he p omo ion o bioclus e s and echnology pa ks (Sca la e al., 2015; BioSTEP, 2016), could
accele a e his p ocess by demanding mo e high-quali y biomass (Budzinski e al., 2017; E ken e al., 2016).
Howe e , he en isaged p oduc ion o high- alue biomass-based goods and ma e ials wi h economic and
non-economic bene i s may p o e an una ainable ision (B a e al., 2013), conside ing he al eady high
impo s o biomass o ma e ial and ene gy use (Leopoldina, 2012).
1.1.2 Technological inno a ion in he ag icul u al sys em in Ge many
In he con ex o Ge many, he ole o echnological inno a ion in educing ag icul u al ca bon emissions
is s ill unclea , as he ag icul u al sys em is complex and he p oduc i i y o R&D is di icul o es ima e.
By compa ison, while Ge many’s business R&D spending has inc eased by 3.3 % pe yea o e he las
decades, R&D p oduc i i y has allen by an a e age o 5.2% pe yea (Boeing and Hüne mund, 2020). This
5
aligns wi h he indings ha R&D p oduc i i y is dec easing in Ge many, pa icula ly ou side he
bioeconomy con ex (Schä e , 2014; Ugu e al., 2016). In 2016, he o al public R&D unding o he
bioeconomy in Ge many amoun ed o a ound EUR 120 million (Imbe e al., 2017). Th ough R&D
in es men s, a numbe o local bio ech inno a ion ne wo ks and bioeconomy clus e s in Ge many ha e
in eg a ed biomass p oduce s wi hin a ci cula economy o suppo inno a ion and coupled subsys ems
(Kaise and P ange, 2004; Mennicken e al., 2016; Wilde and He mans, 2021). This highligh s he
in e ac ion be ween inno a ion and coupled subsys ems in he Ge man plan -based bioeconomy. In addi ion,
Ge many aims o ul il all i s elec ici y needs om enewable sou ces by 2035, wi h wo- hi ds expec ed
o come om bioene gy (F ondel e al., 2010; Mohmmed e al., 2019). The p ojec ed dec ease in ene gy-
based emissions by 2030 wi h his ene gy ansi ion due o he poli ical incen i es p omo ing enewable
ene gy (754.883 M CO2, c . Mohmmed e al., 2019) unde sco es he ele ance o coupled subsys em o
enewable ene gy and he ag icul u al p oduc ion subsys em in he plan -based bioeconomy.
1.1.3 Fo es -based bioeconomy in Ge many
The o es -based bioeconomy, encompassing he en i e o es alue chain, is conside ed o be a key playe
in he a ena o p omo ing he bioeconomy o achie ing deca boniza ion o he economy. In Ge many, i
has exhibi ed g ea po en ial o clima e change mi iga ion by educing ca bon emissions (Hagemann e al.,
2016; Pu kus e al., 2018). Up o 2016, 570 policy documen s linked o ca bon-mi iga ion s a egies,
co e ing he whole alue chain o he o es -based bioeconomy, ha e been launched a he EU le el (Ri e a
León e al, 2016). I is a gued ha he o es -based bioeconomy can play bo h di ec and indi ec oles (e.g.,
ca bon seques a ion by he o es and soil, and subs i u ion e ec s om bioene gy eplacing ossil uel,
espec i ely) in ca bon emission educ ion (Seppälä e al., 2019; Jonsson e al., 2020; Kumeh e al., 2021).
Fo es s, a key esou ce inpu and suppo sys em o he o es -based bioeconomy, a e a main sou ce o
ca bon sinks. I has been epo ed ha o es s and ha es ed wood p oduc s oge he seques e he equi alen
o ci ca 10% o he EU’s g eenhouse gas emissions (EU, 2022). Apa om o es s and adi ional wood
p oduc s, he o es -based bioeconomy also co e s e o s di ec ed owa ds bioene gy, biochemicals,
6
ex iles, cellulose and lignocellulosic bioplas ics, packaging p oduc s, e c. (Wol slehne e al., 2016). I can
also con ibu e o clima e change mi iga ion by p omo ing he use o wood o subs i u e ossil uels and
o he ma e ials (EU, 2022). Howe e , his subs i u ion p ocess occu s a he sac i ice o mo e o es biomass.
I is e iden ha wi hou su icien a o es a ion and o es esou ces, he mi iga ion s a egies ocusing on
enhanced ca bon s o age in wood p oduc s and he subs i u ion o ossil uels and ene gy-in ensi e ma e ials
equi e biomass emo als; he eby, in mos cases, dec easing he ca bon seques a ion po en ial o he
o es s (Lindne e al., 2017). Thus, he eal mi iga ion e ec s o he o es -based bioeconomy on ca bon
emissions equi e a be e unde s anding.
1.1.4 Bioclus e s in Ge many
Bioclus e s a e a special kind o clus e s ha ope a e wi h he explici goal o p omo ing sus ainable
de elopmen by os e ing he ansi ion o a bioeconomy (He mans, 2018). Bioclus e s can play an
impo an ole in he sus ainable ansi ion o bioeconomy, especially in Ge many. A he same ime,
bioclus e s a e cha ac e ized by coupled p oduc ion sys ems, leading o s onge ho izon al and e ical
implica ions o indus ial in eg a ion (Wessele and on B aun, 2017). The ci cula p oduc ion mode is
p e alen in bioclus e s ca ies a high expec a ion o linking inno a ion wi h clima e neu ali y (Bibe ‐
F eudenbe ge e al., 2020). This can help o an o m he p e alen linea p oduc ion mode o a no-linea
p oduc ion mode o he whole o socie y as well. Fu he mo e, close coope a ion among bio ech companies,
esea ch ins i u es, echnology pa ks, e c., can c ea e su icien scien i ic ou pu s and inno a ions o suppo
he eme ging bioeconomy. Especially o Ge many, bioclus e s, cha ac e ized by he hea y concen a ion
o s akeholde s and o ganiza ions, a e p o ing o be impo an new echnology impulse gi e s in his espec
(Do ocki, 2014). The h i ing bioclus e s in Ge many, wi h mo e han 770 bio echnology companies
in ol ed in 2021, ha e c ea ed subs an ial scien i ic ou pu s, o e ing oppo uni ies o p omo e g een
e iciency and p oduc i i y a la ge (FMEACA, 2022).
13
As biomass sou ces mainly o igina e om ag icul u e and o es , he mi iga ion e ec s o o es and
ag icul u e sec o s on ca bon emissions a e dis inguished.
Resea ch ques ion: Will he ansi ion o a o es -based bioeconomy educe ca bon emissions”?
Gi en ha he e is cu en ly limi ed unde s anding o he impac o he o es -based bioeconomy on ca bon
emissions in Ge many, his disse a ion aims o es ima e he spa ial impac o he o es -based bioeconomy,
especially he ole o echnological inno a ion in he o es -based bioeconomy on ca bon emissions. Using
an ex ended Spa ial Du bin Model and 401 NUTS-3 le el panel da a om 2000 o 2021, his disse a ion
measu es he in a- egional and spillo e e ec s o echnological inno a ion, he size o he bioeconomy,
indus ial upg ading and hei in e ac ions on ca bon emissions empi ically.
Resea ch Objec i e 3 (Chap e IV): The hi d objec i e o his disse a ion is o measu e he impac o
bioclus e s on sus ainable pe o mance whe e ca bon emissions a e conside ed as an undesi ed ou pu .
Resea ch ques ion: Will he es ablishmen o bioclus e s p omo e g een p oduc i i y”?
Gi en he esea ch gap abou he mechanisms o ins i u ional inno a ion’s e ec s on g een p oduc i i y,
his disse a ion, ocusing on Ge many a he NUTS-3 le el, aims o es ima e he causal e ec s o
bioclus e s on g een p oduc i i y media ed by echnological inno a ion. The disse a ion uses a quasi-
na u al expe imen , including a se ies o me hods, like di e ence in di e ences (DiD), S agge ed
DiD(SDiD), PSM-SDiD, and di e ence in di e ence in di e ences (DDD), and a media ing model o
es ima e he impac o es ablishing bioclus e s on g een o al ac o p oduc i i y (GTFP) in Ge many.
1.4 S uc u e o he disse a ion
This disse a ion examines he implica ions o de eloping he bioeconomy o p omo ing sus ainable
de elopmen in Ge many, analysing i om wo pe spec i es and h ee in luencing mechanisms. One
pe spec i e is ob ained om an impac e alua ion. Using NUTS-3 panel da a om 2000 o 2021, his
disse a ion examines he impac s a bioeconomy in Ge many would ha e on ca bon emission educ ion and

14
g een p oduc i i y empi ically, p o iding empi ical e idence ega ding he e iciency o he bioeconomy
s a egy o policymake s and con ibu ing o he ele an body o li e a u e. The o he pe spec i e is
ob ained om a scena io simula ion p ocess. This disse a ion an icipa es he po en ial mi iga ion e ec o
echnological inno a ion on ag icul u al ca bon emissions as well as he dynamic in e ac ions among
subsys ems om 2020 o 2050, wi h he simula ion pe iod chosen o i in Ge many’s goal o a aining
ca bon neu ali y by 2050. Th ee in luencing mechanisms a e p oposed co e ing s uc u al changes in he
p oduc ion ac o s, namely changes in land use and labou , and indus ial s uc u e; echnological
inno a ion and i s spa ial di usion; and ins i u ional incen i es in he bioeconomy.
The emainde o he disse a ion is o ganized as ollows. Chap e Ⅱ and Ⅲ discuss he po en ial mi iga ion
e ec s o ag icul u e and o es y unde he ansi ion o bioeconomy on ca bon emissions, espec i ely.
Chap e Ⅳ analyses he in luences o ins i u ional inno a ions in he bioeconomy on he g een o al ac o
p oduc i i y, while he inal chap e p esen s he conclusion pa (Chap e Ⅴ) wi h some policy sugges ions
and me hodological implica ions. The s uc u e o he disse a ion is illus a ed in Figu e 1.2.
Biomass supply
Technological
inno a ion
Ins i u ional
inno a ion
Bioeconomy
Case o Ge many
Chap e Ⅰ
In oduc ion
Policy
implica ions
Me hodological
implica ions
Implica ions
Ge many and o he
coun ies
Chap e Ⅴ
Syn hesis
Chap e Ⅱ
Mi iga ion e ec o
ag icul u e on ca bon
emissions
Sys em dynamic modeling;
E ec s o R&D in es men s and
dynamic in e ac ions
Chap e Ⅲ
Mi iga ion e ec o
o es -based
bioeconomy
In a- egional and spillo e
e ec s o echnological
inno a ion;
Spa ial Du bin Model
Chap e Ⅳ
Implica ion o
bioclus e s on g een
p oduc i i y
Causal e ec s o bioclus e s on
g een p oduc i i y;
Quasi-na u al expe imen ;
Media ing model
Sec o
NUTS-3
Figu e 1.2: S uc u e o he disse a ion
Sou ce: Own ope a ions.
15
2 Po en ial mi iga ion e ec s o echnological inno a ion in he plan -based
bioeconomy on ca bon emissions
1
2.1 Objec i es and heo e ical amewo k
2.1.1 Backg ound and o ganiza ion
Ag icul u e is he co ne s one o he Eu opean Union’s bioeconomy due o i s ole as a p ima y biomass
supplie and as an impo an sec o wi h g ea po en ial o ca bon emission educ ion. Ag icul u e can bo h
gene a e ca bon emissions h ough a ming ac i i ies and indus ial p ocesses and a he same ime ac as a
ca bon sink ia plan pho osyn hesis and land use changes (Pa aki e al., 2006; Wang e al., 2012; Gu zle
e al., 2015). This dual ole complica es he measu emen o ag icul u e’s ac ual impac on ca bon emissions.
The in oduc ion o echnological inno a ion, which is achie ed h ough R&D in es men and is an
impo an pilla in he bioeconomy (Schü e, 2018), u he adds o he di icul y. This is because such
inno a ion can educe ca bon emissions by imp o ing esou ce e iciency and enhancing ca bon
seques a ion, bu i can also inc ease emissions due o he ebound e ec .
So a , he e is li le knowledge abou whe he and how R&D in es men s can mi iga e ag icul u al ca bon
emissions wi h he ansi ion o he bioeconomy. Many s udies sugges ha echnological inno a ion has
he po en ial o educe ca bon emissions by imp o ing ag icul u al p oduc i i y, educing he need o
cul i a ed land, enhancing inno a ion e iciency, and p omo ing a ci cula economy (Xiong e al., 2016;
F ank e al., 2019; Balsalob e-Lo en e e al., 2019; Nwakae e al., 2020). R&D in es men , o en used as
an indica o o echnological inno a ion, has he po en ial o educe ca bon emissions by ad ancing
bio echnological inno a ions, imp o ing he p oduc ion e iciency o bio e ine ies, and p omo ing
1
Au ho s a emen : Lanjiao Wen (concep ualiza ion, me hodology, so wa e, w i ing-o iginal d a , and e ision); D .
Zhanli Sun (concep ualiza ion, e ision and supe ision); D . Lioudmila Cha alo a (concep ualiza ion and e ision);
P o . D . Anlu Zhang (concep ualiza ion); P o . D . Al ons Balmann ( e ision and supe ision).
16
bioene gy (e.g., biogas). Howe e , some ag icul u al li e a u e a gues ha he inc ease in ca bon emissions
caused by echnological inno a ion may exceed he educ ions hey o e . This is because he applica ion
o ce ain echnologies, such as bio echnology and b eeding, equi es mo e biomass and ene gy o suppo
he ansi ion o a bioeconomy (Henle e al., 2008; Flei e e al., 2012; I is and Lam, 2019). The g owing
demand o biomass can also lead o land use con lic s, biodi e si y loss, and an o e use o chemicals and
ene gy (Deininge , 2013; Liobikiene e al., 2020).
Wi h he ansi ion o he bioeconomy, he ag icul u al ca bon emission sys em becomes mo e complex,
comp ising he land use subsys em, ag icul u al p oduc ion subsys em, inno a ion subsys em, coupled
subsys em (including esou ce ecycling and upg ading use), and he socioeconomic subsys em.
Speci ically, in he plan -based bioeconomy, he p oduc i i y o echnological in es men s can be a ec ed
by many ac o s, like he inpu -ou pu ela ionship, indus ial in eg a ion, biomass ecycling/upcycling use,
and inno a ion e iciency. This no only makes he ag icul u al ca bon sys em mo e complex and
challenging o quan i y, bu also highligh s he oles o he inno a ion subsys em and coupled p oduc ion
subsys em, as well as he dynamic in e ac ions among subsys ems in ca bon emission educ ion.
Fu he mo e, he dec easing R&D p oduc i i y has been epo ed in Ge many (Schä e , 2014; Ugu e al.,
2016), which makes he ole o echnological inno a ion in educing ca bon emissions wi hin he
ag icul u al sys em e en less clea . The e o e, unde s anding how R&D in es men s impac ca bon
emissions h ough he dynamic in e ac ions among subsys ems in he ag icul u al sys em is a majo ques ion
ha needs o be answe ed.
The p esen s udy aims o con ibu e o p ojec ing ag icul u al emissions by de ailing he impac o R&D
in es men s on ca bon emissions. Focusing on he ag icul u al sec o in Ge many, his s udy applies a
sys em dynamics modelling app oach o simula e he po en ial impac o R&D in es men s on ca bon
emissions h ough conside ing he dynamic in e ac ions among he ag icul u al ca bon subsys ems. To
p esen he ne e ec o R&D in es men s on ca bon emissions, his s udy akes ca bon sinks and ca bon
17
emission educ ion in o accoun , including, e.g., ca bon seques a ion om land use and plan p oduc ion,
and ca bon educ ion om e-/upcycling o ag icul u al esidues and by-p oduc s.
The es o his chap e is o ganized as ollows. Sec ion 2.1.2 ou lines he ela ionship among ca bon
emissions, ca bon sinks, and ca bon emission educ ion in he ag icul u al sys em. Sec ion 2.2 in oduces
he da a and me hodology used o simula ing ne ca bon emissions and he dynamic in e ac ions in he
ag icul u al sys em. Sec ion 2.3 desc ibes he design o scena ios o he simula ion. The esul s and policy
implica ions a e summa ized and discussed in sec ion 2.4, while he las sec ion 2.5 concludes he analysis.
2.1.2 Theo e ical amewo k
Figu e 2.1 illus a es an o e iew o he subsys ems conside ed unde he nexus be ween ag icul u al
sus ainabili y and ca bon emissions. C op p oduc ion and animal husband y a e he main ag icul u al
ac i i ies and ca bon sou ces as well. Thei p oduc ion in ol es inpu and ou pu lows and is associa ed
wi h ca bon emissions om bo h he a m p ocesses and li es ock, while ca bon seques a ion sa ed by he
g een landscape and he ecycling and euse o biomass can educe he emissions o some deg ee, o ming
an ag icul u al ca bon cycle. Since he ag icul u al ca bon cycle is associa ed wi h he p oduc ion p ocess,
economic en i onmen , land use co e change, echnological le el, and p oducing s uc u e (Lu and
Guldmann, 2012; Gu e al., 2019), i e subsys ems in he plan -based bioeconomy a e de ined and modelled
in he p esen s udy. The i e subsys ems, namely he land use subsys em, ag icul u al p oduc ion
subsys em, inno a ion subsys em, coupled p oduc ion subsys em (including esou ce ecycling and
upg ading use), and he socioeconomic subsys em (see Figu e 2.1), closely in e ac wi h each o he .
18
Ag icul u al ca bon emission sys em
Ca bon sink
G een landscape
(pho osyn hesis,
ca bon seques a ion)
C op p oduc ion Animal husband y
Ca bon emissions
Plan a ming
(i iga ion, e ilize ,
pes icides,
ploughing…… )
Ca bon cycle
Inpu ac o s
Li es ock
(animal manu e…… )
Recycling use
(ag icul u al esidues,
biogas p oduc ion) Ou pu s
Subsys ems in he plan -based bioeconomy
Socioeconomic
subsys em
Ag icul u al
p oduc ion
subsys em
Land use
subsys em
Coupled
p oduc ion
subsys em
Inno a ion
subsys em
GDP; social
in es men ;
R&D
in es men ……
Fa mland;
g assland;
Fallow land;
EFA……
R&D
in es men ;
bio echnology;
bioclus e s……
Bio e ine y;
ag icul u al
esidues;
animal
manu e……
Fa mland &
g assland;
e ilize ;
bio echnology
……
Figu e 2.1: O e iew o he subsys ems
Sou ce: Own ope a ions.
R&D in es men can signi ican ly imp o e echnological inno a ion and indus ial in eg a ion (e.g.,
e ical in eg a ion) (Wessele e al., 2015; Wessele and on B aun, 2017). Thus, he inno a ion subsys em
is c ucial in he plan -based bioeconomy. Along wi h indus ial in eg a ion, alue chain in eg a ion h ough
cascading o ci cula esou ce u iliza ion can acili a e ag icul u al p oduc ion by swi ching om a
adi ional linea mode o a non-linea mode, making he in e ac ions be ween he ag icul u al p oduc ion
subsys em and coupled p oduc ion subsys em mo e ele an o each o he . As R&D in es men can p omo e
a cascading use e iciency o biomass, e.g. by p omo ing i s use o biogas p oduc ion, while new pa en s
and bio e ine ies can con ibu e o seconda y GDP (GDP-2) (So da e al., 2013; G ando e al., 2017), hey

19
closely link he ci cula economy wi h R&D in es men in he plan -based bioeconomy (Theue l e al.,
2019; Ka dung e al., 2021). This implies ha he inno a ion subsys em closely in e ac s wi h he coupled
p oduc ion subsys em and socioeconomic subsys em.
In addi ion o he inno a ion subsys em and coupled p oduc ion subsys em, he ag icul u al p oduc ion
subsys em, socioeconomic subsys em and land use subsys em a e he basic componen s in he ag icul u al
ca bon sys em. Re e ing ag icul u al land use wi h na u al/pe ennial ege a ion ( o ins ance, allow land)
is ega ded as one o he mos e icien ways o accumula e o ganic ca bon in soil (Pos and Kwon, 2000;
Schulp e al., 2008). Thus, sus ainable a mland policies, such as he Common Ag icul u al Policy (CAP)
and g eening e o m o he CAP, ha e been adop ed in Ge many o imp o e he biodi e si y o a mland.
Especially, he g eening e o m o he CAP in oduced ecological ocus a eas (EFAs). E en hough EFAs
ha e no ye been e idenced o ha e a posi i e e ec on imp o ing biodi e si y as esea che s expec a ed
(Pe'E e al., 2017), hey ha e p o en o be e ec i e o ca bon seques a ion (O oy e al., 2018). The e o e,
he EFA is conside ed in he ag icul u al ca bon emission sys em.
In his s udy, he land use subsys em mainly includes a mland (c opland and g assland) and g een land.
G een land is he sum o he allow land, ecological ocus a ea (EFA), and g assland, which is associa ed
wi h a ne ca bon sink. As a able land is associa ed wi h he socioeconomic and ag icul u al p oduc ion
subsys ems, he land use subsys em di ec ly in e ac s wi h he socioeconomic subsys em and ag icul u al
p oduc ion subsys em. The ag icul u al p oduc ion subsys em co e s he plan ing and li es ock, and also
hei associa ed ca bon emissions. Ploughing and i iga ion, which a e posi i ely ela ed wi h he a able
land a ea, oge he wi h he capi al inpu s in ol ed in plan ing ( e ilize , pes icides, and diesel) a e
posi i ely ela ed o ca bon emissions. Ag icul u al p oduc ion subsys em in e ac s wi h all he o he
subsys ems di ec ly. This is because ag icul u al p oduc ion p o ides biomass and ag icul u al esiduals o
he coupled p oduc ion subsys em, and R&D in es men o e all (R&D es ) and R&D in es men
speci ically in ag icul u e (AR&D) om he inno a ion subsys em a e bene icial o imp o ing ag icul u al
p oduc i i y. Addi ionally, he basic inpu ac o s, such as capi al (ag icul u al in es men ) and labou , a e
20
associa ed wi h he socioeconomic subsys em, while he ag icul u al land is pa o he land use subsys em.
The inno a ion subsys em is mainly ep esen ed by R&D in es men o e all (R&D es ), R&D in es men
speci ically in ag icul u e (AR&D), R&D s a , and pa en s. The coupled p oduc ion subsys em includes
bioene gy p oduc ion and o he seconda y indus ies ha use bio e ine ies o p oduc ion. As i depends on
he biomass supply and echnological in es men s, his subsys em di ec ly in e ac s wi h he inno a ion
subsys em and ag icul u al p oduc ion subsys em. The socioeconomic subsys em is deno ed by GDP,
seconda y GDP (GDP-2), e ia y GDP (GDP-3), labou , popula ion, social in es men (In es ) and
ag icul u al in es men (Ain es ). I di ec ly in e ac s wi h he ag icul u al p oduc ion subsys em, land use
subsys em and inno a ion subsys em. This subsys em may no p oduce ag icul u al ca bon emissions
di ec ly, bu he ac i i ies associa ed wi h o he subsys ems can gene a e ca bon emissions and seques e
ca bon a he same ime.
The e ec s o he dynamic in e ac ions among R&D in es men s, land use change and inno a ion e iciency
on ag icul u al ca bon emissions a e s udied in ou scena ios in addi ion o he base scena io, namely (1)
land e ec , (2) s uc u e e ec , (3) echnological e ec and (4) hei combined e ec . All o hem a e
simula ed o he pe iod om 2020 o 2050 (see Figu e 2.2).
21
Sus ainabili y-guided bioeconomic s a egies
Ne ca bon emissions simula ion
SD scena io schemes
S1-Land e ec :
sus ainable land use
managemen
S2-S uc u al e ec :
ci cula economy
S3-Technological
e ec : echnological
in es men s
His o ical ajec o y o
ca bon emissions
Model alida ion
Subsys ems
Modi y Ag icul u al
p oduc ion
subsys em
Socioeconomic
subsys em
Coupled
p oduc ion
subsys em
Inno a ion
subsys em
land use
subsys em
Ca bon emissions p ojec ion om 2020 o 2050
S4-Combined e ec
Figu e 2.2: Simula ion p og ess o ne ca bon emissions
Sou ce: Own ope a ions.
2.2 Me hodology
Wi h a sys em dynamics (SD)app oach, he ne ca bon emissions om 2020 o 2050 and he dynamic
in e ac ions in he sys em a e simula ed. The ne ca bon emissions o e hese h ee decades a e p ojec ed
as Ge many aims o a ain ca bon neu ali y by 2045.
22
2.2.1 Da a sou ce and assump ions
The ca bon emissions da a used in his s udy we e calcula ed on he basis o land use da a and emission
pa ame e s o di e en land use ypes and di e en ag icul u al ac i i ies a he ede al le el in Ge many.
The his o ical pe iod was om 2000 o 2019 since he concep o knowledge-based bioeconomy o igina ed
in he 2000s (Pa e mann and Aguila , 2018). The emission pa ame e s we e de i ed om he
In e go e nmen al Panel on Clima e Change (IPCC) (2019) and ela ed s udies. The land use da a om
2000 o 2019 we e collec ed om he s a is ics o he Ge man Fede al Minis y o Food and Ag icul u e
(BMEL), Fede al O ice o S a is ics and Thünen-Ins i u ü Ländliche Räume. The o he seconda y da a,
such as socioeconomic da a, o 2000–2019 we e collec ed om he Eu opean S a is ical O ice (Eu os a ),
Fede al O ice o S a is ics, and BMEL.
This s udy assumes ha R&D in es men s ha e a mi iga ion e ec on ag icul u al ca bon emissions mainly
h ough in e nal in e ac ions wi h ega d o he land use subsys em and inno a ion subsys em. In addi ion,
R&D in es men s a e assumed o be posi i e wi h social in es men and GDP. Besides, ag icul u al R&D
in es men s a e assumed o be posi i e wi h R&D in es men s.
2.2.2 S uc u e o he SD model
The SD model de eloped by Jay W. Fo es e is a decision-making ool ha has been widely used o
simula e he complica ed beha iou and eedback o eal sys ems (Fo es e , 1970). I in ol es he use o
s ocks, lows and eedback loops o ep esen he in e dependencies wi hin a sys em. As an ad anced
simula ion ool, i p o ides enhanced capabili ies o isualiza ion, scena io analysis, and use in e ac i i y,
suppo ing mul i-me hod modelling and combining sys em dynamics wi h agen -based modelling. Owing
o he complexi y discussed p e iously, he SD model is employed in his s udy o simula ing ca bon
emissions o e he pe iod om 2020 o 2050. The eason o choosing his model is ha i o e s ad an ages
o in eg a ed and quan i a i e simula ion in he sho and medium e m (Fong e al., 2009; Fu e al., 2015;
Gu e al., 2019).
29
In scena io 2, he s uc u al e ec is e lec ed by he de elopmen o ci cula economy. Due o he
signi ican ole o biomass in he ci cula economy and he a ge o minimize ag icul u al esidues in
ag icul u e (She wood, 2020; Sha ma e al., 2021), he de elopmen o he ci cula economy is ep esen ed
by he inc easing supply o biomass and inc easing sha e o ag icul u al esiduals sen o he bio e ine y. In
he i s subcase (S uc u e 2-1), all he plan s a e assumed o ha e as e inc ease a es han ha in he base
scena io. Alao maize, which usually gene a es a g ea numbe o ag icul u al esidues, is se o ha e he
highes g ow h a e (3%) om 2020 o 2050 among he plan s o biogas p oduc ion. In he second subcase
(S uc u e 2-2), o highligh he ole o bio e ine ies as aw ma e ials o he indus y sec o , i is assumed
ha he sha e o ag icul u al esiduals o biogas would educe om 0.4 in 2020 o 0.2 in 2050. Fu he mo e,
in S uc u e 2-3, bo h cases a e conside ed.
Di ec R&D in es men can no only imp o e p oduc ion e iciency in he ag icul u al p oduc ion
subsys em, bu also p omo e he scale o he ci cula economy by ele a ing he le el o esidues
p e ea men and he esou ce use in ensi y o bio e ine ies (Amidon e al., 2011; Tayeh e al., 2020). To
display he ole o R&D in es men in he ag icul u al p oduc ion subsys em and coupled p oduc ion
subsys em, R&D in es men in ag icul u e (AR&D) and he gene al R&D in es men (R&D) a e aken in o
accoun in scena io 3. In Tech 3-1, he sha e o ag icul u al R&D (AR&DR) is se o inc ease o 6% in
2020. A he same ime, in Tech 3-2, he sha e o R&D in es men in GDP (R&DR) is assumed would
inc ease o 0.06 in 2020. Combining Tech 3-1 and Tech 3-2, AR&DR and R&DR a e se o inc ease in
Tech 3-3. The scheme in he Combine 4 scena io includes he Land scena io, S uc u e 2-3, and Tech 3-3.

30
Table 2.3: Scena io schemes o di e en scena ios
Scena io
Schemes
Base scena io
Base: Ra ios a e se as same as ha in 2019
Scena io 1-Land e ec
Land: Inc easing he a io o allow land o 0.05 in 2020
Scena io 2-S uc u al
e ec
S uc u e 2-1: Inc easing he supply o ag icul u al biomass;
S uc u e 2-2: Inc easing he sha e o ag icul u al was es o bio e ine ies
(dec easing he sha e o was e o biogas om 0.4 in 2020 o 0.2 in 2050);
S uc u e 2-3: Inc easing bo h ag icul u al biomass and he sha e o
ag icul u al was es o bio e ine ies
Scena io 3-
Technological e ec
Tech 3-1: Inc easing he sha e o ag icul u al R&D in R&D in es men
(AR&DR) o 0.05 in 2020;
Tech 3-2: Inc easing he sha e o R&D in es men in GDP (R&DR) o
0.06 in 2020;
Tech 3-3: Inc easing bo h AR&DR and R&DR
Scena io 4- Combined
e ec
Combine: Inc easing he inc emen a io o allow land o 0.05 in 2050,
inc easing bo h ag icul u al biomass and he sha e o bio e ine y, and
inc easing bo h AR&DR and R&DR
2.4 Resul s and analysis
2.4.1 His o ical endency o ne ca bon emissions
Figu e 2.5 shows he his o ical endencies o he Ne ca bon emissions (NCE), ca bon emissions (CE),
ca bon sinks (CS), and ca bon emissions educ ion (CR) in Ge man ag icul u e du ing he pe iod 2000 o
2019 a ying by yea s. Despi e he ag icul u al ca bon emissions luc ua ing om 10.5 million ons o 9
million ons om 2000 o 2019, an o e all downwa d end could be obse ed. This is likely because o he
educing use o e ilize and ploughing and he dec easing numbe o ca le and sheep due o echnological
31
imp o emen s in he p oduc ion o a ming and husband y (Jan ke e al., 2020). Fu he mo e, he
ag icul u al ca bon sink amoun dec eased sligh ly om 1.25 million ons in 2000 o 1.07 million ons in
2013. A e 2014, his igu e inc eased g adually; especially a e 2015, whe eby i inc eased apidly o 1.36
ons in 2019. Biogas had a mino eplacemen e ec on ca bon emission educ ion a he beginning o he
pe iod (2000-2005) when he biogas elec ici y p oduc ion was ela i ely low (445 Mio. kWh in 2000 and
1696 Mio. kWh in 2005), bu such p oduc ion g ew obus ly a e 2006, eaching 29,245 Mio. kWh in 2017.
This con ibu ed o a apid g ow h in ca bon emissions educ ion, inc easing om 0.28 million ons in 2006
o 2.41 million ons in 2019 (see Figu e 2.5).
Ne ca bon emissions (NCE) in Ge man ag icul u e kep dec easing in he pe iod 2000 o 2019. This
endency was simila o he endency o ca bon emissions om 2000 o 2006, as he ca bon sink and ca bon
emissions educ ion emained nea ly cons an du ing his pe iod. Since 2006, NCE has declined g adually
bu in he opposi e di ec ion wi h ha o ca bon emissions due o he apid inc ease in ca bon emission
educ ion. Due o he policy e o ms p omo ing biogas, such as gua an eed eed-in a i s, bio e ine ies a e
encou aged o p oduce biogas. Du ing he pe iod om 2006 o 2019, he ne ca bon emissions dec eased
by almos a hi d, om 7.79 million ons in 2006 o 5.34 million ons in 2019.
32
Figu e 2.5: Ne ag icul u al ca bon emissions, ca bon emissions, ca bon sink and ca bon emissions
educ ion om 2000 o 2019
2.4.2 Scena io analysis
Using he SD model, he ne ca bon emissions in he ag icul u al sys em a e simula ed unde di e en
scena ios o he pe iod 2020 o 2050 (see Figu e 2.6). Acco ding o Figu e 2.6, ne ca bon emissions a e
p ojec ed o dec ease apidly du ing he pe iod om 2020 o 2050. Ne ca bon emissions unde he S uc u e
2-1 (inc ease ag icul u al biomass), S uc u e 2-3 (inc ease bo h ag icul u al biomass and bio e ine y), and
Combine (combined e ec ) scena ios will emain posi i e du ing 2020 o 2050. The esul s om S uc u e
2-1, S uc u e 2-3, and Combine imply ha inc easing biomass p oduc ion may suppo he ci cula
economy, bu he inc eased ca bon emissions du ing he p oduc ion p ocess canno be o se by he educed
ca bon emissions h ough R&D in es men alone. Compa ed wi h he Base scena io, he ne ca bon
33
emissions unde he Land (inc ease he a io o allow land), S uc u e 2-2 (inc ease he sha e o
bio e ine ies), Tech 3-2 (inc ease he sha e o R&D in es men ), and Tech 3-3 (inc ease bo h ag icul u al
R&D in es men and R&D in es men ) scena ios a e smalle . The esul o ne ca bon emissions unde
Tech 3-2 is he lowes (-2.82 M in 2050). This highligh s he ole o R&D in es men in mi iga ing ca bon
emissions di ec ly. While he esul s o Land and S uc u e 2-2 no only indica e ha inc easing he amoun
o allow land and de eloping he ci cula economy can educe ca bon emissions, bu also imply ha R&D
in es men can indi ec ly mi iga e ca bon emissions h ough imp o ing he p oduc ion e iciency o
bio e ine ies and by inc easing he amoun o g een land.
The ag icul u al ca bon emissions calcula ed in his s udy co espond o nea ly one six h o ca bon dioxide
equi alen s epo ed by he Thünen ins i u e (61.8 M in 2019, c . Rösemann e al., 2021). This big
di e ence may esul om wo aspec s: Once, as men ioned p e iously, he calcula ion by Rösemann e al.
(2021) includes all he GHG emissions om Ge man ag icul u e, wi h all kinds o ag icul u al ac i i ies
and animals o li es ock aken in o conside a ion, whe eas ewe p oduc ion managemen ac i i ies (e.g.,
e ilize , ploughing and i iga ion) and only h ee kinds o animals (caw, sheep, and pig) a e conside ed in
his s udy; Second, he ca bon emissions calcula ed by Rösemann e al. is based on ca bon dioxide
equi alen s, while ou esul s a e based on ca bon equi alen s.
34
Figu e 2.6: Ne ag icul u al ca bon emissions unde di e en scena ios om 2000 o 2050
The ag icul u al ca bon emissions and hei causal ee du ing 2000 o 2050 a e shown in Figu e 2.7. The
ob ained esul s show ha ag icul u al ca bon emissions will g adually dec ease om 2020 o 2050. The
simila endencies o CE-1 (ca bon emissions om plan a ming) and ca bon emissions in he causal ee
(in he igh o Figu e 2.7) indica e ha ag icul u al ca bon emissions a e mainly caused by a ming
ac i i ies. This adds o he body o he ele an li e a u e by unco e ing he mechanism o he echnological
e ec s on emissions educ ion. Speci ically, i shows ha R&D and AR&D in es men s can signi ican ly
lowe ag icul u al ca bon emissions. The amoun o ca bon emissions unde S uc u e 2-1 is he lowes
(dec easing o 4.33 M in 2050), and he igu es unde Combine (4.44 M in 2050) and S uc u e 2-3 (4.78
M in 2050) a e less han ha unde he Base case (5.12 M in 2050), illus a ing hei g ea e impac s on
ca bon emissions om he imp o ed ci cula economy wi h he inc easing biomass supply.

35
Figu e 2.7: Ca bon emissions and hei causal ees unde di e en simula ion scena ios om 2000 o
2050
Ca bon sinks, as s imula ed in S uc u e 2-2, scena io 3 (Tech 3-1, 3-2, and 3-3), and in he Base case, a e
p edic ed o con inuously inc ease om 2020 o 2050 (see Figu e 2.8). While ca bon sinks unde scena io
1 (Land) and 4 (Combine) i s g ow apidly a e 2020, hey a e hen o ecas o show a sha p decline in
2033, be o e g adually inc easing again om 2034 o 2050. This sha p change is mainly ela ed o he
ca bon sinks by g een land (CS-1), acco ding o he causal ee. Due o he maximum es ic ion o he EFA,
he simula ion equa ion o he EFA changes a e 2033 (see he equa ions in Table A.1). The highe alues
in he Land scena io (6.83 M in 2050) sugges ha sus ainable land use managemen and inc easing
biomass supply will aid he amoun o ca bon sinks a ailable.
36
Figu e 2.8: Ca bon sinks and hei causal ees unde di e en simula ion scena ios om 2000 o 2050
The obse a ions om he p ojec ed ca bon emissions educ ion om biogas suppo he indings ha
biogas p oduc ion has he po en ial o imp o e ca bon emission educ ion i R&D in es men leads o
cleane p oduc ion (Meye e al., 2012; E soy and Ugu lu, 2020). The p ojec ed ca bon educ ion dec eases
g adually a e 2020. This a ises om he eplacemen e ec o bioene gy on he ca bon emission educ ion.
Compa ison among he di e en scena ios indica es ha he combined e ec has he g ea es impac on
ca bon emissions educ ion (1.87 M in 2050). The alues unde Tech 3-2 and Tech 3-3 show s able
dec ease (1.3 M in 2050). This implies ha R&D in es men can p omo e he ca bon emission educ ion
a la ge (see Figu e 2.9).
37
Figu e 2.9: Ca bon emissions educ ion unde di e en simula ion scena ios om 2000 o 2050
2.4.3 Sensi i i y analysis
The sensi i i y o R&D in es men ’s impac on ne ca bon emissions is shown in Figu e 2.10. In o de o
es he sensi i i y, he change a e o he sha e o R&D in GDP (R&D- o-GDP a io) is se o inc ease and
dec ease by 25%, 15%, 10%, and 5%. The change a es o ne ca bon emissions ep esen he pe cen age
di e ence be ween ne ca bon emissions a a ying R&D- o-GDP a ios and ne ca bon emissions unde
he base scena io. Figu e 2.10 shows ha mos o he ne ca bon emissions a e simila when he change a e
o R&D- o-GDP a io is changed om -25% o 25%. This implies ou esul s a e ela i ely eliable. The
nega i e ela ionship be ween he change a es o ne ca bon emissions and he change a es o he R&D-
o-GDP a io illus a es ha ne ca bon emissions a e sensi i e o R&D in es men . Rega ding he endency
o he change a es o ne ca bon emissions, he endency showed a dec ease om 2000 o 2011 and hen
an inc ease a e wa ds. Among simula ed a es, he change a e is he la ges when he change a e o R&D-
o-GDP a io inc eased o 25%, eaching 290% in 2011 and e u ning o 8.7% in 2019. This may be because
o he model es ic ion o ca bon emissions educ ion.
38
Figu e 2.10: Resul o he sensi i i y analysis
2.5 Discussion and conclusions
2.5.1 Discussion
The abo e analysis shows ha R&D in es men can con ibu e o a educ ion in ag icul u al ca bon
emissions du ing 2020 o 2050, helping ealize ca bon neu ali y a he sec o le el ahead o 2045. The
indings ega ding he decline in ca bon emissions simula ed by he SD model unde di e en scena ios
add o he body o ele an li e a u e by p ojec ing he impac o R&D in es men s on ca bon emissions
and unco e ing he dynamic in e ac ions among he a ious subsys ems in he plan -based bioeconomy.
Me hodologically, he SD model is a cu ing-edge app oach o unde s anding and managing he
complexi ies o ag icul u al p ac ices and hei impac on ca bon emissions. Unlike gene al p ojec ion
models, such as ime se ies eg ession (e.g. ARIMA) and back-p opaga ion ne wo ks, which equi e s ic
45
emissions in he egional eco-economic sys em om 2000 o 2021 in Ge many? 2) How does he di usion
o echnological inno a ion in he o es -based bioeconomy a ec ca bon emission a he coun y le el? 3)
How does he spa ial spillo e e ec o echnological inno a ion in he o es -based bioeconomy de e mine
he emissions educ ion po en ial and di ec ion? By add essing hese poin s, he p esen s udy aims o
con ibu e o he cu en ly limi ed knowledge abou he impac o he o es -based bioeconomy on ca bon
emissions in Ge many.
The emainde o his chap e is o ganized as ollows. Sec ion 3.1.2 ou lines he ela ionship be ween ca bon
emissions and he o es -based bioeconomy in Ge many. Sec ion 3.2 in oduces he me hods and da a
sou ces. The esul s a e summa ized in Sec ion 3.3. The las sec ion (3.4) concludes his pa o he wo k.
3.1.2 Concep ual amewo k: Ca bon emissions and he o es -based bioeconomy in Ge many
Human ac i i ies ely on land- ela ed ecosys em se ices, necessa ily a ec ing he ecosys em’s ca ying
capaci y. An h opogenic impac s– om una oidable changes in land co e o c ea ing li ing and
p oduc ion space o a oidable en i onmen al ha ms–a e associa ed wi h ca bon emissions (Pa aki e al.,
2006). Land use change d i en by socioeconomic dynamics such as u baniza ion and indus ializa ion is
one o he la ges con ibu o s o ca bon emissions oday. A he same ime, i di ec ly a ec s he
ecosys em’s capaci y o seques e ca bon in soils, he o es s, and geological o ma ions (Bocks ael e al.,
1995; Pa aki e al., 2006). The ca bon cycle is u he a ec ed by physical p ocesses in he li hosphe e,
which a e, howe e , la gely ou side o human con ol. Figu e 3.1 illus a es ca bon emissions p oduc ion
and egula ion in a s ylized eco-economic sys em, ep esen ing he economic ac i i ies ha may occu in a
egional eco-economic sys em.
In line wi h he Eu opean Commission, Ge many has an ambi ion o de elop a bioeconomy ha depends
la gely on he o es -based sec o (Giu ca and Spä h, 2017). The o es -based bioeconomy in Ge many no
only p oduces adi ional wood p oduc s, such as woodwo k, pulp and pape , and wood o bioene gy
(Jochem e al., 2015), bu also aims o maximize alue inc emen in he whole alue chain, which should
esul in high- alue p oduc s and o e ing mo e job oppo uni ies. Technological inno a ion is ega ded as

46
a key pilla o he o es -based bioeconomy. Bo h policy make s and schola s acknowledge ha slow
echnological de elopmen can hinde he de elopmen o he o es -based bioeconomy (BMBF, 2011) and
hus con ine many ele an echnological de elopmen s o he labo a o y and pilo scale (Hagemann e al.,
2016).
Acco ding o he heo y o endogenous g ow h, knowledge spillo e h ough lea ning by doing has a
signi ican posi i e e ec on economic g ow h (A ow, 1962). As an endogenous inpu ac o , echnological
inno a ion in he o es -based bioeconomy oge he wi h o he p oduc ion ac o s, such as labou , land, and
capi al, can p omo e new esou ce alloca ion e iciency and economic g ow h. This will inc ease he
p oduc i i y and compe i i eness o local indus ies in he alue chain o he bioeconomy. The knowledge
spillo e e ec d i en by echnological inno a ion among he alue chain will also s imula e indus ial
in eg a ion and labou di ision h ough op imizing he ac o subs i u ion e iciency, acili a ing bioclus e s
and spa ial indus ial pa e ns o de elop.
Due o he spa ial di usion o inno a ions, a highe deg ee o echnological inno a ion can also imp o e
he compe i i eness o adjacen a eas (Vai sos, 1978). Indus ial clus e s can accele a e indus ial upg ading
by inc easing he compe i i eness o he in ol ed indus ies and hei capaci ies o alue-added gene a ion,
economic di e si ica ion, and employmen c ea ion. F om his pe spec i e, in a-clus e compe i i eness
no only con ibu es o economic g ow h, bu also leads o he educ ion o ca bon emissions (Gau am, 2014;
Cui e al., 2021). While adjacen coun ies may p o ide abundan u ban land o indus ial g ow h (Guas ella
e al., 2017; Gao e al., 2020), hey ha e he po en ial o o e ex a job oppo uni ies oo, hus a ac ing
labou o mo e o he a eas.
47
Ecological economic sys em
Biological p ocesses and human
ac i i ies Physical p ocesses
Ecological subsys em
(pho osyn hesis o o es s,
ca bon seques a ion, e c.)
Economic subsys em
(p oduc ion &consump ion)
Li hosphe e
( olcanic e up ion, ca bon
p ecipi a ion, clima e
change, e c.)
Fo es -based bioeocnomy
Ca bon cycle
Ca bon emissions
Ca bon sink
Labo mobili y Value added
E ec s o spa ial agglome a ion
on ca bon emissions
Indus ies spa ial
pa e ns Labo di ision
Indus ial
es uc u ing Technological
inno a ion
Indus ial
compe i ion Technology
di usion
Indus ial
es uc u ing Labo mobili y
egions adjacen egionslocal
Technological inno a ion
Figu e 3.1: Ca bon cycle in a egionally in eg a ed land use sys em
Sou ce: Own ep esen a ion.
3.2 Me hodology
In his esea ch, he analysis en ails h ee s eps: Fi s , he ne ca bon emissions a e calcula ed o he pe iod
om 2000 o 2021. Nex , he size o he bioeconomy a each coun y/ci y is quan i ied o assess he egional
de elopmen o bioeconomy. Then, a Spa ial Du bin Model is employed o es ima e he impac s o he
o es -based bioeconomy on ne ca bon emissions.
48
3.2.1 Es ima ions o he ne ca bon emissions a he coun y-le el
Ne ca bon emissions a e calcula ed as he sum o ca bon emissions and ca bon sinks associa ed wi h he
use o a able land and cons uc ion land, which ha e he highes ene gy consump ion (Zhao e al., 2015;
Zhang e al., 2015). In he p esen s udy, ca bon emissions o cons uc ion land a e calcula ed indi ec ly as
he p oduc o he ene gy consump ion pe uni o GDP (𝑇𝑖) and GDP o he seconda y and e ia y
indus ies (𝑀𝑖) in coun y i (Wen e al., 2021). Al hough c ops p oduced on a able land can, o some ex en ,
abso b ca bon emissions, he use o e ilize s, ag icul u al machine y, and i iga ion sys ems gene a e high
ne emissions (Yang e al., 2016). The e o e, ca bon emissions (ECi) o each coun y/ci y a e de ined as:
𝐸𝐶𝑖=𝜂𝑎∙𝐶𝑖+𝑇𝑖∙𝑀𝑖 (3-1)
whe e 𝜂𝑎 is he ca bon emissions pa ame e o a able land and Cii is he a able land size.
The ca bon sink (ESi) o coun y i comp ises he ca bon seques a ion in o es s, g assland soils, and wa e
a eas, calcula ed as he p oduc o he ca bon emissions pa ame e 𝛿𝑗 and land size Sij o each land use ype
j:
𝐸𝑆𝑖=∑𝛿𝑗∙𝑆𝑖𝑗 (3-2)
whe e j=1,2,3 and ep esen o es , g assland and wa e , espec i ely. The ne ca bon emission (NECi) o
coun y i is hen:
𝑁𝐸𝐶𝑖=𝐸𝐶𝑖−𝐸𝑆𝑖 (3-3)
3.2.2 Measu ing he size o he bioeconomy
The size o he bioeconomy a ies depending on he de ini ions and app oaches aken. As he majo i y o
s udies ega ding bioeconomy a e quali a i e concep ual pape s wi h di e en de ini ions, he
measu emen s o he size o he bioeconomy di e acco dingly. The di e se de ini ions and lack o
ha monized app oaches o compa ison a e majo challenges o quan i a i e analysis o he con ibu ion
o he bioeconomy owa ds sus ainabili y. Kuosmanen e al. (2020) concluded ha he e a e basically ou
49
ypes o app oaches o measu e he size o he bioeconomy, namely he ou pu -based app oach by no a-
JRC, Finnish bioeconomy s a is ics, he physical supply and use app oaches de eloped by JRC, S a is ics
Ne he lands–CBS, and he Thünen Ins i u e’s me hodology. Among hese, Thünen’s app oach no only
o e s he ad an age in highligh ing he ole o esou ce-based ma e ials lows in he p ocess o p oduc ion,
which is consis en wi h he de ini ion p o ided by he Ge man bioeconomy s a egy (BMEL, 2014), bu
also has he ad an age o e lec ing he di ec socioeconomic con ibu ion o he bioeconomy as i ocuses
on he sec o al le el. Fo his, his s udy adop s he Thünen Ins i u e’s app oach o measu e he size o he
bioeconomy in Ge many. Since “ alue added” has been p o en o be p e e able o ha o g oss ou pu o
a oid epea ed calcula ions (Kuosmanen e al., 2020), his s udy measu es he size o he bioeconomy by
employing he Thünen Ins i u e’s app oach as epo ed by Ios e al. (2019) and conside ing he indica o s
g oss alue added and employmen .
Acco ding o Ios e al. (2019), he ag icul u al sec o , including ag icul u e, o es y, and ishing, is
conside ed o be 100% bio-based. Fo he manu ac u ing sec o , he bio-based sha e used in his s udy is
he a e age (𝑏𝑚




) o he di e en bio-based sha es o he sub-manu ac u ing sec o s (a he 4-digi le el),
including ood and eed, ex ile, lea he , wood and wood p oduc s, pape and pape boa d, p in ing,
chemicals, pha macy, plas ics, u ni u e, and o he s. As i is di icul o b ing he se ice sec o da a in line
wi h he NACE sec o s, he a e age bio-based sha e (𝑏𝑜



) o o he expe imen al de elopmen s based on
na u al science and enginee ing is used as he bio-based sha e o he se ice sec o ( he bio-based sha es
o ele an NACE sec o s a he 4-digi le el a e shown in Table A.3). Two dimensionali ies o he size o
bioeconomy in Ge many a e calcula ed as below:
𝐵𝑉𝑖=𝑉𝐴𝑖+𝑉𝑀𝑖∗𝑏𝑚




+𝑉𝑂𝑖∗𝑏𝑜



(3-4)
𝐵𝐸𝑖=𝐸𝐴𝑖+𝐸𝑀𝑖∗𝑏𝑚




+𝐸𝑂𝑖∗𝑏𝑜



(3-5)
whe e 𝐵𝑉𝑖 and 𝐵𝐸𝑖 a e he alue added o bioeconomy and he numbe o employees in he bioeconomy
o coun y i espec i ely; VA, VM and VO deno e he alue added o he ag icul u e sec o , manu ac u ing
50
sec o , and se ice sec o espec i ely; EA, EM and EO p esen s he numbe o employees in he ag icul u e
sec o , manu ac u ing sec o , and se ice sec o espec i ely.
3.2.3 The Spa ial Du bin Model
A Spa ial Du bin Model (SDM) is de eloped o es ima e he impac o a o es -based bioeconomy on ca bon
emissions. P io o modelling, he global spa ial au oco ela ion index (Mo an’s I) is calcula ed o es o
spa ial au oco ela ion and spa ial he e ogenei y (Odland,1988; Geniaux and Ma ine i, 2018; Feng and
Chen, 2018):
𝑀𝑜𝑟𝑎𝑛′𝑠 𝐼 =∑ ∑ 𝑊𝑖𝑘
𝑛
𝑘=1 (𝑁𝐶𝐸𝑖
𝑛
𝑖=1 −𝑁𝐶𝐸






)(𝑁𝐶𝐸𝑘−𝑁𝐶𝐸






)/𝑉2∑ ∑ 𝑊𝑖𝑘
𝑛
𝑘
𝑛
𝑖=1 (3-6)
wi h he mean (𝑁𝐶𝐸 −𝑁𝐶𝐸






), a iance o ne ca bon emissions (𝑉) and spa ial weigh ma ix (Wik). The
alues o Mo an’s I index a e wi hin he ange o [-1, 1], indica ing ei he posi i e o nega i e spa ial
co ela ion among coun ies (Bai e al., 2012; Anselin, 2013; Gao e al., 2020). I he alue is ze o, hen he
coun ies a e no spa ially co ela ed.
Nume ous s udies sugges ha ca bon emissions, being a ec ed by he na u al en i onmen and human
ac i i ies, ha e egional spillo e e ec s (Jun e al., 2017; Wang e al., 2018; Wang e al., 2019). The ange
o his e ec , howe e , a ies depending on he model in use. The ad an age o he SDM is ha –o he han
he spa ial lag model (SLM) and he spa ial e o model (SEM)–i can cap u e he spa ial co ela ion o
dependen a iables and he spa ial spillo e e ec s o independen a iables (LeSage and Pace, 2010).
Fu he mo e, he SDM usually has a highe le el o goodness-o - i compa ed wi h o he spa ial panel
models (Wen and Liao, 2019). Since he null hypo hesis o andom e ec s is ejec ed (P ob>chi2=0.000
acco ding o Hausman’s es ), a SDM wi h ixed e ec s is applied.
The impac o he o es -based bioeconomy on ca bon emissions is wo old. Apa om he size o he
bioeconomy, he numbe o pa en s in he o es -based bioeconomy and i s a e o applica ion (as p oxy o
echnological inno a ion), as well as hei in e ac ions wi h he size o bioeconomy a e selec ed as he co e
a iables. In acco dance wi h G ossman and K uege (1995), wo dimensionali ies o he size o he

51
bioeconomy in Ge many, namely he alue added o bioeconomy (BV) and numbe o employees (BE) in
he bioeconomy, a e used o es ima e he impac o he bioeconomy scale on ca bon emissions. The numbe
o pa en s (Numbe ) in he o es -based bioeconomy and hei applica ion a e in he cu en yea (Ra e) a e
used o p esen wo aspec s o echnological inno a ion: he o me deno es he in ensi y o echnological
inno a ion and he la e deno es he ans o ma ion e iciency o scien i ic achie emen s, espec i ely
(Popp e al., 2003; Ha ahill e al., 2023). By accoun ing o he socioeconomic con ol a iables, namely
indus ial upg ading (S uc u e), which is he a io o GDP o he e ia y sec o s o GDP o he indus ial
sec o s, he labou densi y (Labou ), which is he amoun o labou pe ha, he size o u ban cons uc ion
a ea (U ban) and pe capi a GDP (Pe GDP), he basic SDM in he p esen analysis can be w i en as SDM
1:
𝑙𝑛𝑁𝐶𝐸 =𝜌𝑊𝑙𝑛𝑁𝐶𝐸+𝜕1𝑙𝑛𝐵𝑉+𝜕2𝑙𝑛𝐵𝐸+𝜕3𝑙𝑛𝑁𝑢𝑚𝑏𝑒𝑟+𝜕4𝑙𝑛𝑅𝑎𝑡𝑒+𝜕5𝑙𝑛𝐿𝑎𝑏𝑜𝑢𝑟+
𝜕6𝑙𝑛𝑃𝑒𝑟𝐺𝐷𝑃+𝜕7𝑙𝑛𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒+𝜕7𝑙𝑛𝑈𝑟𝑏𝑎𝑛+𝜑1𝑊𝑙𝑛𝐵𝑉+𝜑2𝑊𝑙𝑛𝐵𝐸+𝜑3𝑊𝑙𝑛𝑁𝑢𝑚𝑏𝑒𝑟+
𝜑4𝑊𝑙𝑛𝑅𝑎𝑡𝑒+𝜑5𝑊𝑙𝑛𝐿𝑎𝑏𝑜𝑢𝑟+𝜑6𝑊𝑙𝑛𝑃𝑒𝑟𝐺𝐷𝑃+𝜑6𝑊𝑙𝑛𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒+𝜑6𝑊𝑙𝑛𝑈𝑟𝑏𝑎𝑛+𝛾𝑙𝑛+𝜀 (3-7)
Wi h he spa ial au oco ela ion coefficien gi en by 𝜌, spa ial weigh ma ix 𝑊, spa ial lag o he dependen
a iable 𝑊𝑙𝑛𝑁𝐶𝐸, spa ial lag o he explana o y a iables 𝑊𝑙𝑛𝑋, ma ix o he explana o y a iables 𝑋, an
n×1 ec o o ones 𝑙𝑛, ec o s o espec i e eg ession coefficien s 𝜕, 𝜑, 𝛾 o X, 𝑊𝑙𝑛𝑋 and 𝑙𝑛 and he e o
e m 𝜀.
G ow h-pole heo y and empi ical obse a ions sugges ha he mo e de eloped an a ea is, he s onge i s
spa ial agglome a ion e ec on neighbou ing egions (in eg a ion e ec ), because a highe de elopmen
le el c ea es cen ipe al o ces on capi al, echnology and labou (Wen e al., 2016). The e ec o
echnological inno a ion on ne ca bon emissions can, he e o e, be media ed h ough he in e ac ing inpu
and ou pu ac o s ( esou ce alloca ion). In he ex ended model (SDM 2), his can be cap u ed by accoun ing
o in e ac ions be ween he Numbe and media ing a iables BV, BE, S uc u e, and PGDP:
52
𝑙𝑛𝑁𝐶𝐸 =𝜌𝑊𝑙𝑛𝑁𝐶𝐸+𝜕1𝑙𝑛𝐵𝑉+𝜕2𝑙𝑛𝐵𝐸+𝜕3𝑙𝑛𝑁𝑢𝑚𝑏𝑒𝑟+𝜕4𝑙𝑛𝑅𝑎𝑡𝑒+𝜕5𝑙𝑛𝐿𝑎𝑏𝑜𝑢𝑟+
𝜕6𝑙𝑛𝑃𝑒𝑟𝐺𝐷𝑃+𝜕7𝑙𝑛𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒+𝜕7𝑙𝑛𝑈𝑟𝑏𝑎𝑛+𝜷𝟏𝒍𝒏𝑵𝒖𝒎𝒃𝒆𝒓∗𝒍𝒏𝑩𝑽+𝜷𝟐𝒍𝒏𝑵𝒖𝒎𝒃𝒆𝒓∗𝒍𝒏𝑩𝑬+
𝜷𝟑𝒍𝒏𝑵𝒖𝒎𝒃𝒆𝒓∗𝒍𝒏𝑺𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆+𝜷𝟒𝑳𝒏𝑵𝒖𝒎𝒃𝒆𝒓∗𝒍𝒏𝑷𝒆𝒓𝑮𝑫𝑷+𝜑1𝑊𝑙𝑛𝐵𝑉+𝜑2𝑊𝑙𝑛𝐵𝐸+
𝜑3𝑊𝑙𝑛𝑁𝑢𝑚𝑏𝑒𝑟+𝜑4𝑊𝑙𝑛𝑅𝑎𝑡𝑒+𝜑5𝑊𝑙𝑛𝐿𝑎𝑏𝑜𝑢𝑟+𝜑6𝑊𝑙𝑛𝑃𝑒𝑟𝐺𝐷𝑃+𝜑6𝑊𝑙𝑛𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒+
𝜑6𝑊𝑙𝑛𝑈𝑟𝑏𝑎𝑛+𝛾𝑙𝑛+𝜀 (3-8)
whe e 𝛽 is a eg ession coefficien ec o o he in e ac ions.
Conside ing he scale e ec s o GDP and labou on ca bon emissions, he expec ed signs o BV and BE
a e posi i e (+). Technological inno a ion, deno ed as he numbe o pa en s (Numbe ) in he o es -based
bioeconomy and he ans o ma ion a e (Ra e) a e assumed o ha e a nega i e impac on ca bon emissions.
Simila ly, u ban agglome a ion (U ban and Labou ) and economic g ow h (PGDP) will a he inc ease
ca bon emissions (Nakiceno ic, 2000), sugges ing he signs o U ban, Labou and PGDP a e expec ed o
be posi i e (+). Upg ading he indus ial s uc u e (S uc u e), by con as , may lead o lowe ca bon
emissions (-). Table 3.1 gi es an o e iew o all he model a iables.
Table 3.1: Desc ip ions o he a iables
Name
Uni s
Obs
Mean
S d.De
Min
Max
Sign
NCE
106 ons
8822
610291.2
951385.7
62372.67
10600000
BV
million Eu o
8822
1371.585
2056.334
133.294
27605.67
+
BE
103 pe sons
8822
24.739
27.5
3.831
357.031
+
Numbe
-
8822
17.64
25.68
0
271
-
Ra e
%
8822
0.18
0.218
0
1
-
S uc u e
%
8822
0.520
0.327
0.037
3.994
-
53
Labou
103 pe sons
8822
24.903
14.777
2.421
73.560
+
PGDP
103 Eu o
8822
31.280
14.646
11.209
195.809
+
U ban
ha
8822
11978.92
7479.399
1212
62906
+
3.2.4 Da a sou ce
The p esen s udy combined in o ma ion om mul iple da a sou ces. The ne ca bon emissions, including
di ec ca bon emissions and indi ec ca bon emissions as well as ca bon sinks, we e calcula ed on he basis
o he land use da a and emission pa ame e s o di e en land use ypes a he coun y le el. The emission
pa ame e s we e de i ed om he In e go e nmen al Panel on Clima e Change (IPCC) (2021) da a and
ela ed s udies. The land use da a om 2000 o 2021 we e collec ed om he Thünen Land A las and
Regional Da abase Ge many. Due o he adminis a i e di ision adjus men , some coun ies ha e been
dele ed and adjus ed acco ding o he coun ies/dis ic s in 2021. Fo ins ance, Os e ode am Ha z has been
adjus ed as a municipali y in he coun y o Gö ingen since 2016, The annual socioeconomic da a o 401
coun ies om 2000 o 2021 we e ga he ed mainly om he Fede al O ice o S a is ics o Ge many,
Regional Da abase Ge many, Eu os a Da abase, and Fede al Agency o Ag icul u e and Food (BMEL).
The pa en da a in he o es -based bioeconomy om 2000 o 2021 we e collec ed om he O ganiza ion
o Economic Co-ope a ion and De elopmen (OECD) s a is ics.
3.3 Resul s and analysis
3.3.1 Spa io empo al dis ibu ion o ne ca bon emissions
Figu e 3.2 summa izes he esul s o ne ca bon emissions (NCE) in Ge many and o i s di ision in Eas e n
and Wes e n Ge many in he pe iod om 2000 o 2021. The esul s show a gen le downwa d end in ne
emissions in Ge many. A apid d op in ca bon emissions in 2009 ollowed by a ise again in 2010 may be
because o he impac o economic dep ession caused by he global inancial c isis a he ime. La e , due
o he economic decline esul ing om he Co id-19 pandemic, ca bon emissions d opped du ing he
pandemic bu climbed again signi ican ly in 2021 o e ase he ea lie d op. Fu he , he much highe ca bon
54
emissions in Wes e n Ge many han in Eas e n Ge many e lec s he highe economic g ow h and indus ial
de elopmen in he o me , and hence i s highe associa ed NCE. Wes e n Ge many sha ed he same
endency o ca bon emissions wi h Ge many while Eas e n Ge many had a ela i ely small and s able
ac ion o ne ca bon emissions.
Figu e 3.2: Ne ca bon emissions in Ge many du ing he pe iod 2000-2021
Sou ce: Own ep esen a ion.
The spa ial dis ibu ion o ne ca bon emissions a he NUTS-3 le el, as shown in Figu e 3.3, changed in
in ensi y o e ime. In addi ion o he ci y-s a es, like Be lin and Hambu g, he highes NCE (> 2000×103
ons) was p oduced in he wes e n coun ies, indica ing clus e ing pa e ns in Wes e n Ge many, while he
lowes NCE (< 500×103 ons) was p oduced in he eas e n coun ies. No ewo hy, he apid decline o NCE
61
2020
0.071
-0.003
0.029
2.569
0.005
2021
0.072
-0.003
0.029
2.614
0.004
Sou ce: Own calcula ion.
Table 3.3 summa izes he esul s o he pa ame e es ima ions by using wo e sions o he spa ial Du bin
model, as desc ibed in sec ion 3.2.3. The i s model (SDM 1) includes he size o he bioeconomy,
echnological inno a ion, and socioeconomic pa ame e s (c . Equa ion 3-7), while i s ex ensions con ol
co espondingly o he e ec s o he in e ac ion e ms (SDM 2, c . Equa ions 3-8). Va ying he a iables
in wo models e eal only insigni ican e ec s, indica ing he models’ s abili y and obus ness.
Table 3.3: Es ima ion esul s om he Spa ial Du bin Model and i s ex ensions
Main e ec on NCE (ρWlnNCE+∂lnX)
Spillo e e ec o X on NCE (φWlnX)
SDM 1
SDM 2
In e ac ion e ec
SDM 1
SDM 2
In e ac ion e ec
lnBV
0.198***
(0.006)
0.131***
(0.007)
0.055***
(0.013)
0.004
(0.013)
lnBE
0.055***
(0.046)
0.116***
(0.007)
0.084***
(0.014)
0.100***
(0.015)
lnNumbe
0.002***
(0.005)
-0.053***
(0.005)
-0.002*
(0.001)
-0.021**
(0.010)
lnRa e
0.001***
(0.000)
0.001*
(0.000)
0.001
(0.001)
0.001
(0.001)
lnPGDP
0.680***
(0.008)
0.024***
(0.002)
-0.518***
(0.142)
0.007
(0.004)
lnS uc u e
-0.025***
(-3.24)
-0.026***
(0.002)
-0.013*
(0.007)
-0.0113**
(0.004)
lnLabou
0.007***
(0.000)
-0.009***
(0.002)
0.001
(0.002)
0.004
(0.003)
lnU ban
-0.102***
-0.001
0.115***
0.003**

62
(0.007)
(0.001)
(0.012)
(0.001)
LnNumbe * lnBV
0.716***
(0.008)
-0.465***
(0.016)
LnNumbe * lnBE
-0.0157***
(0.003)
-0.032***
(0.006)
LnNumbe * lnPGDP
0.00591***
(0.001)
0.001
(0.002)
LnNumbe * lnS uc u e
-0.076***
(0.007)
0.063***
(0.011)
ρ
0.420***
(0.016)
0.429***
(0.0135)
R2
0.622
0.609
lg _ he a
-4.304***
(0.392)
-4.302***
(0.392)
sigma2_e
0.001***
(1.07e-5)
0.001***
(9.86e-6)
Log-Likelihood
19610.264
19719.822
Sou ce: Own calcula ion.
No e: -s a is ics in pa en heses; *s a is ical signi icance on p<0.10 le el, ** p<0.05 le el, *** p<0.01
le el; 8822 obse a ions.
The esul s show ha wi hin SDM 1 he es ima ed ca bon emissions (NCE) a e posi i ely co ela ed wi h
he alue added o he bioeconomy (BV), employees in he bioeconomy (BE), pe capi a GDP (PGDP),
and labou densi y (Labou ). This inding is in line wi h exis ing li e a u e, a guing ha economic ac o s
a e he main d i e s o highe emissions (Wang e al., 2018; Zhang e al., 2020). An inc ease in u ban
cons uc ion land (U ban), in con as , educes NCE (-0.102***), because Ge many is highly de eloped
and has an ad anced indus ial di ision. Ge man ac o ies wi h high ca bon emissions end o be he less
labou -in ensi e indus ies, while u ban a eas in Ge many al eady ha e a high le el o land de elopmen
(Li e al., 2020). Indus ial upg ading (S uc u e), as expec ed, d i es down emissions signi ican ly (-
63
0.025***), because i s imula es indus ial ansi ion owa ds g ea e sus ainabili y (Bai e al., 2023;
Mehmood e al., 2024).
Con a y o expec a ions, an inc ease in he numbe o pa en s in he o es -based bioeconomy (Numbe )
and he a io o pa en s applied o in he cu en yea (Ra e), seems o gi e ise o he highe ca bon
emissions, espec i ely (0.002*** and 0.001***, espec i ely). This can be explained by he ac ha
echnological inno a ion o o es -based bioeconomy in Ge many con ibu es o economic g ow h, which
ul ima ely lead o highe NCE le els (Khan e al., 2023).
Fu he , he coe icien s o he spa ially lagged independen a iables sugges ha he alue added o
bioeconomy (BV), employees in bioeconomy (BE), labou densi y (Labou ) and u ban cons uc ion land
(U ban) ha e signi ican ly posi i e spillo e e ec s in e ms o highe emissions on neighbou ing
coun ies. The spillo e e ec s o Numbe , S uc u e, and PGDP a e signi ican ly nega i e, indica ing ha
coun ies wi h high le els o GDP and echnological inno a ion, and hus mo e de eloped indus ies
a ac mo e echnological in es men and na u al esou ces om neighbou ing coun ies (Gao e al.,
2020).
The esul s o SDM 2 sugges ha echnological inno a ion in he o es -based bioeconomy can educe
ne ca bon emissions, gi en a s onge alue added o he bioeconomy, and mo e jobs o employees in
he bioeconomy, a highe pe capi a GDP, and indus ial upg ading. The signi ican ly nega i e spillo e
e ec o Numbe highligh s he ole o spa ial di usion o echnological inno a ion in he o es -based
bioeconomy in educing ca bon emissions. As shown in Table 3.3, he e is a s ong in e ac ion be ween
Numbe and BV, BE, PGDP, and S uc u e compa ed o SDM 1. The signi ican ly nega i e in e ac ions
be ween Numbe and BE and S uc u e e eal ha , conside ing he impac s o BE and S uc u e on ca bon
emissions, an inc ease in echnological inno a ion can s a o educe ne ca bon emissions. The
signi ican posi i e in e ac ions be ween Numbe and BV and PGDP indica e an opposi e e ec on
emissions. P omo ing echnological inno a ion in he o es -based bioeconomy can consequen ly mi iga e
ca bon emissions when combined wi h a g ea e numbe o employees in he bioeconomy and indus ial
64
upg ading. The nega i e spillo e e ec s o he in e ac ions be ween Numbe and BV and BE on ca bon
emissions imply ha an inc ease o echnological inno a ion in he o es -based bioeconomy gi en he
inc ease o BV and BE can educe he ca bon emissions in neighbou ing coun ies, while he posi i e
spillo e e ec o he in e ac ion be ween Numbe and S uc u e e lec s he opposi e case.
Table 3.4 displays he di ec e ec , indi ec e ec and he o al e ec o he pa ame e s in SDM 1 and
SDM 2. The o al e ec o Numbe on ca bon emissions is nega i e whe he in SDM 1 o SDM 2. This
con i ms he mi iga ion e ec o echnological inno a ion in he o es -based bioeconomy on ca bon
emissions. Fu he , he consis en di ec ion o he di ec e ec s o in e ac ion be ween Numbe and BV,
BE, PGDP, and S uc u e wi h hei o al e ec s no only implies hei highe di ec e ec s han di ec
e ec s bu also s esses he combined ac ion o echnological inno a ion in he o es -based bioeconomy,
labou s uc u al change and indus ial upg ading on ca bon emissions.
Table 3.4: Di ec e ec , indi ec e ec , and o al e ec o he model pa ame e s
SDM 1
SDM 2
Di ec
Indi ec
To al
Di ec
Indi ec
To al
lnBV
0.207***
(0.006)
0.230***
(0.019)
0.436***
(0.019)
0.137***
(0.007)
0.098***
(0.019)
0.235***
(0.022)
lnBE
0.061***
(0.004)
0.177***
(0.022)
0.238***
(0.023)
0.130***
(0.007)
0.253***
(0.022)
0.382***
(0.024)
lnNumbe
0.002***
(0.001)
-0.002
(0.002)
-0.000
(0.003)
-0.056***
(0.006)
-0.074***
(0.016)
-0.130***
(0.018)
lnRa e
0.001***
(0.000)
0.002
(0.002)
0.003
(0.002)
0.001*
(0.000)
0.002
(0.001)
0.003
(0.002)
lnPGDP
0.664***
(0.009)
-0.387***
(0.024)
0.278***
(0.025)
0.025***
(0.002)
0.029***
(0.007)
0.054***
(0.007)
lnS uc u e
-0.027***
-0.037***
-0.064***
-0.028***
-0.038***
-0.066***
65
(0.003)
(0.010)
(0.011)
(0.002)
(0.007)
(0.008)
lnLabou
0.007***
(0.001)
0.006
(0.004)
0.013***
(0.004)
-0.009***
(0.002)
-0.001
(0.005)
-0.010
(0.006)
lnU ban
-0.098***
(0.007)
0.121***
(0.017)
0.022
(0.017)
-0.001
(0.001)
0.004*
(0.002)
0.003
(0.003)
LnNumbe * lnBV
0.701***
(0.009)
-0.262***
(0.022)
0.439***
(0.024)
LnNumbe * lnBE
-0.019***
(0.003)
-0.064***
(0.008)
-0.083***
(0.009)
LnNumbe * lnPGDP
0.006***
(0.001)
0.005**
(0.003)
0.012***
(0.003)
LnNumbe * lnS uc u e
-0.074***
(0.008)
0.050***
(0.017)
-0.023
(0.017)
No e: -s a is ics in pa en heses; *s a is ical signi icance a p<0.10 le el, ** p<0.05 le el, *** p<0.01
le el; 8822 obse a ions.
3.4 Discussion and conclusions
3.4.1 Discussion
The analysis shows ha echnological inno a ion in he o es -based bioeconomy can con ibu e o
decoupling economic de elopmen om emissions p oduc ion. The ob ained esul s a e in line wi h ecen
s udies, which ound ha echnological inno a ion nega i ely a ec s ca bon emissions (E doğan e al.,
2020; Zhao e al., 2021). The esul s con ibu e o he body o he ele an li e a u e by showing ha he
o es -based bioeconomy, combined wi h echnological inno a ion in he o es -based bioeconomy and he
numbe o employees in he bioeconomy, can imp o e he ne ca bon emissions pe o mance wi hin a
egion and h ough spa ial spillo e e ec s empi ically. As shown by he alues o coe icien s, he numbe
o pa en s in he o es -based bioeconomy (-0.053***), i s in e ac ions (LnNumbe *lnBE and
LnNumbe *lnS uc u e), indus ial upg ading (-0.108***), and labou in ensi y (-0.009***) can signi ican ly
66
lowe ne ca bon emissions. Likewise, he alue added o bioeconomy (0.131***), numbe o employees in
he bioeconomy (0.116***), applica ion a e o pa en s in he o es -based bioeconomy (0.001*), and PGDP
(0.024***) can all aid in educing emissions. This is consis en wi h he la es indings sugges ing ha he
g ow h o he bioeconomy esul s in a highe demand o biomass, while ca bon ax will accele a e ma ke
oppo uni ies o bio-based al e na i es (Philippidis e al., 2024). Technological inno a ion in he o es -
based bioeconomy also s eng hens indus ial upg ading (-0.026***), and enhances indus y compe i ion and
labou di ision, con ibu ing in his way o a mo e sus ainable ansi ion o indus y and socie y (Bai e al.,
2023; Mehmood e al., 2024).
The key e ec o he o es -based bioeconomy on ca bon emissions is shown o be wo old, de e mined by
he subs i u ion/complemen a i y o he esou ces exchanged and echnological di usion among he
coun ies. Resou ce subs i u ion can d i e indus ial upg ading and he op imiza ion o esou ce alloca ion
in coun ies wi h high le els o echnological inno a ion and la ge numbe s o employees in he bioeconomy.
The esul ing nega i e e ec on ca bon emissions spills o e o hei neighbou ing coun ies. Resou ce
complemen a i y, o i s pa , weakens adminis a i e ba ie s and s eng hens egional coope a ion, which
explains he inconsis en spa ial di usion o ca bon emissions (Figu e 3.4) and alue added o bioeconomy
(Figu e 3.6). This equi es an e icien egula ion o echnological inno a ion and esou ce alloca ion in he
bioeconomy o p e en hei nega i e ex e nali ies (Zilbe man e al., 2013).
O he han he s udies by Jonssen e al. (2021), in which he clima e-change mi iga ion e ec o he o es -
based bioeconomy is in es iga ed by conside ing he inc eased ca bon s o age in ha es ed wood p oduc s
(HWP) a he EU le el, he p esen analysis sugges s ha he con ibu ion o he o es -based bioeconomy
o ca bon mi iga ion can no only be e lec ed by he ca bon sinks in HWP bu also h ough echnological
inno a ion as well as i s spillo e e ec s. The combina ions o echnological inno a ion and he numbe o
employees in he bioeconomy and indus ial upg ading u he highligh hei o e lapping e ec s on ca bon
emissions. These obse a ions allow concluding ha an alignmen o echnological inno a ion wi h

67
indus ial upg ading and a s uc u al change in employmen is needed o boos he o es -based bioeconomy
o educe ca bon emissions (Halonen e al., 2022; He emäki e al., 2022).
3.4.2 Conclusions
The o es -based bioeconomy, accompanied by he demanding o es biomass, echnological inno a ion
and alue-added p oduc ion, a ec s bo h he ca bon oo p in o economic ac i i ies and he ca bon sink
capaci y o he ecological en i onmen . Boos ing he o es -based bioeconomy o bene i om i s po en ial
o p omo e ca bon emissions educ ion is a p io i y in he se ies o bioeconomy s a egies in Ge many. The
s udy es ima ed he spa ial impac o a o es -based bioeconomy, especially echnological inno a ion in he
o es -based bioeconomy, on ca bon emissions. The analysis used he Spa ial Du bin Model and coun y-
le el panel da a o 401 coun ies/ci ies and a i ed a ou main conclusions, as desc ibed below.
Fi s , o he obse ed pe iod 2000 o 2021, he ca bon emissions o 401 coun ies/ci ies in Ge many ha e
been ound o be spa ially au oco ela ed and exhibi clus e ing pa e ns in Wes e n Ge many, la gely
e lec ing he egional economic de elopmen . Second, echnological inno a ion in he o es -based
bioeconomy e eals a signi ican nega i e spillo e e ec on ca bon emissions, indica ing a ole o
echnological di usion in educing ca bon emissions om he local coun y/ci y o he pe iphe y. Ye , he
inconsis en di usion ajec o y o ca bon emissions and he numbe o pa en s in he o es -based
bioeconomy implies a high emissions educ ion po en ial o a o es -based bioeconomy. Thi d,
echnological inno a ion in he o es -based bioeconomy can educe ca bon emissions h ough indus ial
upg ading and inc easing job oppo uni ies in he bioeconomy. Fou h, i can also lowe ca bon emissions
h ough he nega i e spillo e e ec o indus ial upg ading and inc easing he size o he bioeconomy.
68
4 Impac s o ins i u ional inno a ion in he bioeconomy on g een p oduc i i y
3
4.1 Backg ound and objec i es
4.1.1 Ins i u ional backg ound o bioclus e s in Ge many
Ge man bioclus e is an inno a i e s a egy, se ing as an ins i u ional incen i e o s imula ing bio ech
indus ies and economic ans o ma ion. Bioclus e s in Ge many can da e back o he 1970s when
policymake s wo ldwide s a ed o ocus on bio echnology as a key inno a ion s a egy (Fo nahl e al., 2011;
Do ocki, 2014). Despi e Ge many c ea ing a na ional law on gene ic modi ica ions in 1978 o suppo
bio echnology, i e en ually ell well behind he global leade s in he ollowing decades. I has been a gued
ha Ge many was he leas bio echnology de elopmen - iendly coun y in he Wes e n wo ld in he ea ly
1990s (Dohse and S aehle , 2008). Howe e , in 1995, he Ge man Fede al Minis y o Educa ion and
Resea ch (BMBF) announced he BioRegio compe i ion o speed up he la e-s a ing bio ech indus y. A
ha momen , he e we e only 70 bio ech companies in Ge many (BMBF, 2004). In his p og amme,
winning egions could ge p e e en ial access o ede al unding o ealize hei bio ech in es men plans
(Dohse, 2000). A e his ini ial p og amme, se e al o he s ollowed, like BioFu u e, BioP o ile and
BioChance (Fo nahl e al., 2011). The launch o a se ies o s a egies ega ding de eloping bioclus e s has
helped Ge many eclaim i s leading ole in he bioeconomy. The Ge man Bio echnology Repo 2011
poin ed ou ha he Ge man bio ech indus y was back on a g ow h pa h in 2010, wi h 400 bio ech
companies and 809 million eu os R&D expendi u e (E ns & Young, 2011).
To be e o ganize he Bio egions, he Council o BioRegions in Ge many (AK-BioRegio) (also known as
he alliance o he Ge man Bio echclus e s) was o icially ounded a he beginning o 2004 in Leipzig. The
3
Au ho s a emen : Lanjiao Wen (concep ualiza ion, me hodology, so wa e, w i ing-o iginal d a , and e ision); D .
Zhanli Sun (concep ualiza ion, e ision and supe ision); D . I . F ans He mans (concep ualiza ion, da a cu a ion and
e ision); P o . D . Al ons Balmann ( e ision and supe ision).
69
asks o AK-BioRegio includes i e pa s, namely me a-ne wo king, bio ech pa ne ing, de eloping a end
ada (analysis o ends in bio echnology), inno a ion p omo ion, he p o ision o know-how o poli ical
decision-make s, and bes p ac ice exchange. Be o e 2004, bioclus e manage s in he BioRegio
compe i ion we e compe i o s o public unding. Bu a e 2004, wi h he es ablishmen o AK-BioRegio,
hey es ablished a ne wo k o sha ing expe iences and mu ual lea ning. This u he s eng hened
inno a ion di usion and coope a ion among companies, ins i u es, uni e si ies, and o he s akeholde s in
he alue chain o he bioeconomy. A he same ime, he unding sou ce was b oadened o include p i a e
R&D in es men . Now 24 membe s om Bio egions ha e come oge he o op imize and coo dina e hei
egional ac i i ies in he in e es s o Ge man bio echnology.
4.1.2 Objec i es and o ganiza ion
The bioclus e s, as an ins i u ional inno a ion, c ea e sui able ecosys ems o he g ow h o he bioeconomy
by linking bio ech companies, esea ch ins i u es and uni e si ies, echnology pa ks, and ele an
s akeholde s in a geog aphic egion oge he . As such, bioclus e s can os e collabo a ion, inno a ion,
knowledge exchange, and supply chain in eg a ion. Thus, bioclus e s can g ea ly con ibu e o he
de elopmen o he bioeconomy and acili a e he sus ainable ansi ion o he bioeconomy. To da e, bo h
na ional and supe na ional s a egies ha e ended o ocus on he sus ainabili y o he bioeconomy, such as
he EU G een Deal (“F om Fa m o Fo k”, “Ci cula Economy Ac ion Plan”, e c.). Howe e , he e is s ill
li le empi ical e idence on whe he and how he es ablishmen o bioclus e s a ec s g een p oduc i i y.
To ill in his esea ch gap, his chap e , ocusing on Ge many a he NUTS-3 le el, aims o es ima e he
causal e ec s o bioclus e s on g een p oduc i i y media ed by echnological inno a ion. We a emp o
answe he ollowing esea ch ques ions: 1) Does he es ablishmen o bioclus e s inc ease g een
p oduc i i y? I so, by how much? 2) How does he p esence o bioclus e s a ec g een p oduc i i y
inc eases h ough echnological inno a ion in he bioeconomy, and how can his e ec be assessed h ough
pa en da a?
70
The emainde o he pape is o ganized as ollows. Sec ion 4.2 ou lines he backg ound o bioclus e s in
Ge many and p o ides he heo e ical analysis and hypo heses. Sec ion 4.3 in oduces he s udy a ea, da a,
and me hodology. The esul s a e summa ized and discussed in sec ion 4.4. Finally, he discussion and
policy implica ions a e p o ided in sec ion 4.5.
4.2 Theo e ical analysis and hypo heses
This sec ion discusses he di ec e ec s and indi ec e ec s o bioclus e s on g een o al ac o p oduc i i y,
whe e he indi ec e ec s include echnological, agglome a ion, and s uc u al e ec s. As he pu pose o
implemen ing bioclus e s is o cul i a e new dynamics o economic g ow h and o p omo e g een
de elopmen h ough inno a ion, bioclus e s may ha e a di ec impac on g een p oduc i i y.
Simul aneously, he es ablishmen o bioclus e s can con ibu e o p omo ing echnological inno a ion,
clus e ing inno a ion ac o s, and he ans o ma ion o indus y s uc u es. The e o e, echnological,
agglome a ion, and s uc u al e ec s a ise ha can indi ec ly in luence g een p oduc i i y (see Figu e 4.1).
Bioclus e Technological
inno a ion di usion
MRT
ROT
Indus ial
upg ading &
es uc u ing
Talen
agg ega ion
Capi al
agg ega ion
Fac o s
low
Ene gy consump ion
P oduc ion cos
R&D in es men
Ca bon emissions
Economic g ow h
Indus y
s uc u e
Labo
s uc u e
Fac o s
alloca ion
G
T
F
P
Agglome a ion
e ec
S uc u al e ec
Cos o inpu s
Desi able
ou pu
Undesi able
ou pu
No e: GTFP=G een o al ac o p oduc i i y; MRT=Ma ginal a e o ans o ma ion; ROT=Re u n on in es men
+
+
Di ec e ec
Di ec e ec
Scien i ic &
echnological
suppo
Func ions o
bioclus e s
Resou ce
in eg a ion
Agen s
coope a ion
Inpu -ou pu
sys em
Figu e 4.1: Di ec and indi ec e ec s o bioclus e s on GTFP
Sou ce: Own ep esen a ion.
77
and Food (BMEL). The pa en da a in he o es -based bioeconomy om 2000 o 2021 we e collec ed om
O ganiza ion o Economic Co-ope a ion and De elopmen (OECD) S a is ics. The eco ds o bioclus e s
we e om he Ge man T ade and In es su ey (2022), which is suppo ed by he Minis y o Educa ion
and Resea ch and Fede al Minis y o Economic A ai s and Clima e Ac ion, and he Eu opean Clus e
Collabo a ion Pla o m.
4.3.2 Measu ing egional g een p oduc i i y wi h Supe -e iciency SBM
A supe -e iciency Slacks-based measu e (supe -e iciency SBM) model wi h undesi able ou comes was
employed o es ima e he GTFP. Based on Tone(2002), he model is speci ied as below.
𝐺𝑇𝐹𝑃 =𝑚𝑖𝑛 1
𝑛∑𝑥𝑖
𝑥𝑖𝑜
⁄
𝑛
𝑖=1
1
𝑐1+𝑐2(∑𝑦𝑟
𝑑
𝑐1
𝑟=1 𝑦𝑟𝑜
𝑑
⁄+∑𝑦𝑙𝑛𝑑
𝑐2
𝑙=1 𝑦𝑙𝑜
𝑛𝑑
⁄ )
𝑠.𝑡. ∑ 𝛾𝑥𝑖
𝑛
𝑖=1,≠0 ≤𝑥; ∑ 𝛾𝑦𝑖𝑑
𝑛
𝑖=1,≠0 ≥𝑦𝑟
𝑑; ∑ 𝛾𝑦𝑖𝑑
𝑛
𝑖=1,≠0 ≤𝑦𝑙𝑛𝑑;
𝑥 ≥𝑥𝑜; 𝑦𝑑≤𝑦𝑜
𝑑; 𝑦𝑛𝑑 ≤𝑦𝑜
𝑛𝑑; 𝑦𝑑≥0,𝛾 ≥0 (4-1)
whe e 𝐺𝑇𝐹𝑃 is he u ban land g een use e iciency, and o is he p oduc ion decision uni (401 decision uni s
in o al). Each decision uni has n inpu s, c1 desi ed ou pu s, and c2 non-desi ed ou pu s. xio p esen s he
inpu i o decision uni o; 𝑥 deno es he edundancy o inpu ; 𝑦𝑟𝑜
𝑑 and 𝑦𝑙𝑜
𝑛𝑑 a e he desi ed and undesi ed
ou pu s o he decision uni o, espec i ely. 𝑦𝑑and 𝑦𝑛𝑑a e he edundancy o he desi ed and non-desi ed
ou pu s, espec i ely, and 𝛾 is he weigh ec o .
The inpu s and ou pu s a e shown in Table 4.1. The sizes o he u ban a eas, employmen popula ion and
ene gy consump ion a e used o p esen he ixed capi al inpu , labou inpu and ene gy inpu , espec i ely.
G oss domes ic p oduc (GDP) is he desi ed ou pu and ca bon emissions a e he undesi ed ou pu . In his
s udy, he ca bon emissions a e ne ca bon emissions and a e calcula ed as he sum o ca bon emissions and
ca bon sink associa ed wi h land use a he coun y le el (Wen e al., 2021).

78
Table 4.1: Inpu -ou pu indica o s o GTFP
Inpu s
Desc ip ions
Capi al
Size o he u ban a eas, as he sum o he se lemen a ea and anspo a eas,
in each coun y.
Labou
To al employmen popula ion in each coun y.
Ene gy consump ion
Ene gy use o companies in he manu ac u ing sec o a he NUTS-3 le el.
Desi ed ou pu s
GDP
G oss domes ic p oduc a he NUTS-3 le el.
Undesi ed ou pu
Ca bon emissions
The ne ca bon emission (NECi) o coun y i is shown as below:
𝑁𝐸𝐶𝑖=𝜂𝑎∙𝐴𝑖+𝑇𝑖∙𝑀𝑖−∑𝜂𝑗∙𝑆𝑖𝑗 , whe e 𝜂𝑎is he ca bon emissions
pa ame e o a able land; Aii is he a able land size; j=1,2,3 and ep esen
espec i ely o es , g assland and wa e ; Sij is he land size o each land use
ype j; 𝜂𝑗 is he p oduc o ca bon emissions pa ame e collec ed om he
IPCC (2021); 𝑇𝑖 is he p oduc o he ene gy consump ion pe uni o GDP;
and 𝑀𝑖 is he GDP o he seconda y and e ia y indus ies in coun y i.
4.3.3 Measu ing he impac o Bio egions on g een p oduc i i y wi h a s agge ed DiD
The di e ence in di e ences (DiD) me hod is a widely-used quasi-expe imen al echnique o es ima ing
he e ec o a speci ic in e en ion o ea men by compa ing he changes in ou comes be o e and a e he
in e en ion (Goodman-Bacon, 2021). Conside ing he bioclus e s es ablished in di e en yea s, a
s agge ed di e ence in di e ences (SDiD) is employed o compa e he ne e ec on GTFP be o e and a e
he de elopmen o bioclus e s. This app oach add esses he limi a ion o adi ional DiD ha equi es i o
sa is y a s able uni ea men alue assump ion (SUTVA) while igno ing spillo e s. To measu e he
ea men e ec , coun ies/ci es wi h bioclus e s (Bio egions and g een clus e s in his s udy) a e conside ed
79
as he ea men g oup and coun ies/ci es wi hou bioclus e s, excluding hose su ounding he
coun ies/ci ies wi h bioclus e s, a e ega ded as he con ol g oup wi h conside a ion o he spa ial spillo e
e ec . I assumes ha he ea men and con ol g oups display a pa allel end— he wo g oups would ha e
ollowed simila ends o e ime (Slaugh e , 2001).
Acco ding o He mans (2018), bioclus e s a e clus e s ha specialize in a ious ields o he bioeconomy
wi h he explici goal o p omo ing sus ainable de elopmen . To be e p omo e close coope a ion among
bio ech companies, esea ch ins i u es, and echnology pa ks, Ge many, one o he bes en i onmen s o
bio echnology R&D wo ldwide, has es ablished he Council o BioRegions (AK-BioRegio) since 2004.
The Bio egions o Ge many a e egional ini ia i es se up o he ad ancemen o mode n bio echnology in
Ge many. Up o 2022, he e we e 24 ac i e membe s o Bio egions, anging om hose a he local le el
o s a e le el. In he basic model shown in he ollowing eg ession, coun ies/ci es wi h bioclus e s om
Bio egions a e he ea men g oup:
𝐺𝑇𝐹𝑃 = 𝜕0+𝜕1𝐵𝑖𝑜𝑟𝑒𝑔𝑖𝑜𝑛𝑖𝑡 +𝜕𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (Model 4-1)
wi h dummy a iable Bio egion o show whe he coun y/ci y i has a bioclus e om Bio egions o no ;
con ol a iable con ol; cons an e m 𝜕0; es ima ed pa ame e s (𝜕1 and 𝜕𝑐); indi idual e ec 𝜇𝑖, ime
e ec 𝜏𝑖, and andom pe u ba ion e m 𝛿𝑖𝑡. Bio egion is deno ed as Bio egion=d ×du, whe e du de e mines
whe he hey a e quali ied as inno a i e ci ies (yes=1, no=0), and d de e mines whe he hey ha e al eady
been designa ed as inno a i e ci ies (yes=1, no=0). Speci ically, policy assigns a alue o 1 o a ci y in yea
and onwa d i i a ains ecogni ion as an inno a i e pilo ci y; o he wise, i ecei es a alue o 0.
In model 4-2, he alue added o bioeconomy (BV), and employees in bioeconomy (BE) a e in oduced o
model 4-1 o u he examine he he e ogeneous e ec s o Bio egions on GTFP, as shown below wi h he
es ima ed pa ame e s (𝜕2 and 𝜕3 ).
𝐺𝑇𝐹𝑃 = 𝜕0+𝜕1𝐵𝑖𝑜𝑟𝑒𝑔𝑖𝑜𝑛𝑖𝑡 +𝜕2𝐵𝑉𝑖𝑡 +𝜕3𝐵𝐸𝑖𝑡 +𝜕𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (Model 4-2)
80
The pa en applica ion a e in he cu en yea (Ra io) and egional sha e ( eg_sha e) a e used o p esen
he cha ac e is ics o echnological inno a ion (see Model 4-3). The pa en applica ion a e in he cu en
yea (Ra io) is he p opo ion o he numbe o pa en s applied o in he cu en yea , which is ele an o
assessing he ans o ma ion e iciency o scien i ic esea ch and achie emen s (Ha ahill e al., 2023). The
egional sha e ( eg_sha e) means he sha e o add esses o in en o s in cases whe e an add ess is alloca ed
o mo e han one egion, he e o e indica ing egional coope a ion (Ma au e al., 2008; Ma au and
Ma ínez, 2014).
𝐺𝑇𝐹𝑃 =𝜕0+𝜕1𝐵𝑖𝑜𝑟𝑒𝑔𝑖𝑜𝑛𝑖𝑡 +𝜕2𝐵𝑉𝑖𝑡 +𝜕3𝐵𝐸𝑖𝑡 +𝜕4𝑅𝑒𝑔_𝑠ℎ𝑎𝑟𝑒𝑖𝑡 +𝜕5𝑅𝑎𝑡𝑖𝑜𝑖𝑡 +𝜕𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+
𝜏𝑖+𝛿𝑖𝑡 (Model 4-3)
4.3.4 Measu ing he impac o g een clus e s on g een p oduc i i y wi h a s agge ed DiD
As bioclus e s a e he e ogeneous en i ies, a ying widely in s uc u e, e olu ion, and goals (Zechendo ,
2011), he impac o di e en ial ypes o bioclus e s on egional g een p oduc i i y is conside ed in his
s udy. To be speci ic, he g een clus e s ha wo k in g een sec o s and/o echnologies a e selec ed om
he Eu opean Clus e Collabo a ion Pla o m. Acco ding o He mans (2018; 2021), g een clus e s ope a e
wi h he goal o sus ainable de elopmen , so hey can be included as bioclus e s and classi ied in o ou
ypes, namely ag icul u al agglome a ion, g een chemis y clus e s, bioeconomy dis ic s and li e science
clus e s. The e a e 29 g een clus e s chosen om he da a Eu opean Clus e Collabo a ion Pla o m.
The basic model o he SDiD is shown as below, whe e coun ies/ci es wi h g een clus e s a e he ea men
g oup.
𝐺𝑇𝐹𝑃 = 𝛽0+𝛽1𝐺𝑐𝑙𝑢𝑠𝑡𝑒𝑟+𝛽𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (Model 4-4)
wi h dummy a iable Gclus e o show whe he coun y/ci y i has a g een clus e o no . Gclus e is deno ed
as Gclus e =d ×du, whe e du de e mines whe he hey a e quali ied as inno a i e ci ies (yes=1, no=0), and
d de e mines whe he hey ha e al eady been designa ed as inno a i e ci ies (yes=1, no=0). Speci ically,
81
policy assigns a alue o 1 o a ci y in yea and onwa d i i a ains ecogni ion as an inno a i e pilo ci y;
o he wise, i ecei es a alue o 0.
In he ex ended model 4-5, he alue added o bioeconomy (BV) and he numbe o employees in he
bioeconomy (BE) a e in oduced.
𝐺𝑇𝐹𝑃 = 𝛽0+𝛽1𝐺𝑐𝑙𝑢𝑠𝑡𝑒𝑟+𝛽2𝐵𝑉𝑖𝑡 +𝛽3𝐵𝐸𝑖𝑡 +𝛽𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (Model 4-5)
When conside ing he echnological inno a ion by adding he pa en applica ion a e in he cu en yea
(Ra io) and egional sha e ( eg_sha e) in model 4-6, ge he ollowing o m.
𝐺𝑇𝐹𝑃 = 𝛽0+𝛽1𝐺𝑐𝑙𝑢𝑠𝑡𝑒𝑟+𝛽2𝐵𝑉𝑖𝑡 +𝛽3𝐵𝐸𝑖𝑡 +𝛽4𝑅𝑎𝑡𝑖𝑜𝑖𝑡 +𝛽5𝑅𝑒𝑔_𝑠ℎ𝑎𝑟𝑒𝑖𝑡 +𝛽𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+
𝜏𝑖+𝛿𝑖𝑡 (Model 4-6)
4.3.5 Medi a ing model
Based on Böcke man and Ilmakunnas (2009), a media ing model is de eloped o es ima e he indi ec e ec
o bioclus e s on GTFP. Wi h he in oduc ion o he media ing a iable 𝑀𝑖𝑡 (Equa ion 4-4) in o model 4-
3, he ex ended SDiD model 4-7 is shown as below.
𝑀𝑖𝑡 =𝛼0+𝛼1𝐵𝑖𝑜𝑟𝑒𝑔𝑖𝑜𝑛+𝛼𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (4-4)
𝐺𝑇𝐹𝑃 =κ0+𝜅1𝐵𝑖𝑜𝑟𝑒𝑔𝑖𝑜𝑛𝑖𝑡 +𝜅2𝐵𝑉𝑖𝑡 +𝜅3𝐵𝐸𝑖𝑡 +𝜅4𝑅𝑒𝑔_𝑠ℎ𝑎𝑟𝑒𝑖𝑡 +𝜅5𝑅𝑎𝑡𝑖𝑜𝑖𝑡 +𝜅𝑚𝜕𝑐𝑀𝑖𝑡 +
𝜅𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (Model 4-7)
Whe e 𝛼 and 𝜅 a e he espec i e eg ession coefficien s. Simila ly, adding 𝑀𝑖𝑡
′
(Equa ion 4-5) in o model
4-6 gi es he ex ended SDiD, as shown in model 5-8, wi h ec o s o he espec i e eg ession
coefficien s 𝜉.
𝑀𝑖𝑡
′
=𝜁0+𝜁1𝐺𝑐𝑙𝑢𝑠𝑡𝑒𝑟+𝜁𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (4-5)
𝐺𝑇𝐹𝑃 =𝜉0+𝜉1𝐺𝑐𝑙𝑢𝑠𝑡𝑒𝑟+𝜉2𝐵𝑉𝑖𝑡 +𝜉3𝐵𝐸𝑖𝑡 +𝜉4𝑅𝑎𝑡𝑖𝑜𝑖𝑡 +𝜉5𝑅𝑒𝑔_𝑠ℎ𝑎𝑟𝑒𝑖𝑡 +𝜉𝑚𝑀𝑖𝑡
′
+𝜉𝑐𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑖𝑡 +
𝜇𝑖+𝜏𝑖+𝛿𝑖𝑡 (Model 4-8)
82
The indi ec e ec o bioclus e s on GTFP encompasses h ee dis inc e ec s: echnological, agglome a ion,
and s uc u al, deno ed espec i ely by echnological inno a ion (Pa en ), ma ke capaci y (MC), and
indus ial s uc u e (ST). Usually, he numbe o pa en s (Pa en ) is deno ed as he in ensi y o echnological
inno a ion, while he applica ion a e o each pa en can deno e he ans o ma ion e iciency o scien i ic
esea ch and achie emen (Popp e al., 2003; Ha ahill e al., 2023). The numbe o pa en s (Pa en ) is used
o signi y he echnological e ec , as pa en applica ions can se e as indica o s o bo h he quan i y and
quali y o inno a ions wi hin a coun y/ci y du ing a speci ic ime ame (Liu e al., 2023). The a io o he
e ia y indus y o he seconda y indus y (ST) se es as an indica o o indus ial upg ading, whe eby a
highe a io signi ies a mo e ad anced indus ial s uc u e wi hin he coun y/ci y. Ma ke capaci y (MC) is
u ilized o ep esen he agglome a ion e ec , gi en ha en e p ises o en g a i a e owa ds egions wi h
obus ma ke po en ial, os e ing he clus e ing o esou ces wi hin such coun ies/ci ies (Wu and Shao,
2016). MC is calcula ed using Equa ion 4-6.
𝑀𝐶𝑖𝑡 =𝑆𝑇𝐺𝐷𝑃𝑖𝑡
𝑑𝑖𝑡 +∑𝑆𝑇𝐺𝐷𝑃𝑘𝑡
𝑑𝑖𝑘
𝑖≠𝑘 ;𝑑𝑖𝑡 =2
3(𝑎𝑟𝑒𝑎𝑖
𝜋)1
2 (4-6)
whe e 𝑀𝐶𝑖𝑡 deno es he ma ke po en ial o coun y/ci y i. 𝑆𝑇𝐺𝐷𝑃𝑖𝑡 and 𝑆𝑇𝐺𝐷𝑃𝑘𝑡 deno e he ou pu alue
o seconda y and e ia y indus ies in coun y/ci y i and k espec i ely in yea . 𝑑𝑖𝑡 deno es he in e nal
dis ance o coun y/ci y i in yea , and a eai deno es he u ban a ea o ci y coun y/ci y i. 𝑑𝑖𝑘 is he dis ance
be ween coun y/ci y i and k, calcula ed using la i ude and longi ude da a.
4.3.6 Va iables
Conside ing he posi i e con ibu ion o economic ou pu , he expec ed signs o BV and BE a e posi i e
(+). As he highe le el o bioclus e s, he mo e compe i i e he clus e s a e, he sign o he local le el o
biolcus e s (Le el-1) is expec ed o be nega i e, while o he egional and s a e le el he signs a e posi i e.
Technological inno a ion in he bioeconomy is assumed o ha e a posi i e impac on egional g een
p oduc i i y, whe e he signs o he numbe o pa en s (Pa en ) and pa en s’ ans o ma ion a e (Ra io) a e
posi i e, while ha o egional sha e (Reg_sha e) is nega i e. In e ms o he ype o g een clus e s, all

83
ypes a e assumed o ha e a posi i e impac on g een p oduc i i y. Among hem, g een chemical clus e s
(Type-2) may ha e he highes con ibu ion. Simila ly, upg ading he indus ial s uc u e (S uc u e), and
numbe o De-domain (Domain) a he inc ease he egional p oduc i i y (Die ckx and S oeken, 1999),
sugges ing he signs o S uc u e and Domain a e expec ed o be posi i e (+). The de elopmen o he
In e ne has a signi ican e ec on p omo ing imp o emen s o GTFP in his egion and he su ounding
a eas, bu also sugges s ha he long- e m e ec is g ea e han he sho - e m e ec (Yu, 2022). Table 4.2
gi es an o e iew o all he model a iables.
Table 4.2: Desc ip i e s a is ics o a iables
Name
Uni s
Mean
S d.De
Min
Max
Sign
GTFP
-
0.205
0.125
0.062
1.354
BV
10 million Eu o
1371.585
2056.334
133.294
27605.67
+
BE
103 pe sons
24.739
27.5
3.831
357.031
+
Bio egion
0.041
0.198
0
1
+
Le el-1
0.004
0.065
0
1
+
Le el-2
0.019
0.138
0
1
+
Le el-3
0.031
0.174
0
1
+
Gclus e
0.037
0.190
0
1
+
Type-1
0.002
0.040
0
1
+
Type-2
0.007
0.084
0
1
+
Type-3
0.033
0.177
0
1
+
Pa en
19.659
29.506
0
389
+
84
Ra io
%
0.181
0.217
0
1
+
Reg_sha e
%
0.664
0.310
0
1
-
S uc u e
%
0.520
0.327
0.037
3.994
+
Domain
27566.59
45225.02
0
630403
+
4.4 Resul s and analysis
4.4.1 Spa io empo al dis ibu ion o ne ca bon emissions
Figu e 4.3 summa izes he esul s o he a e age annual GTFP in Ge many in he pe iod 2000 o 2021.
The esul s show an upwa d end in GTFP o e he s udy pe iod, indica ing an inc easing g ow h in
egional g een p oduc i i y. Howe e , he a e age alue o he GTFP s ays below 0.5 du ing he s udy
pe iod, showing a ela i ely low g een p oduc i i y in Ge many. Speci ically, he a e age GTFP om 2000
o 2015 g ew slowly, albei wi h a sligh d op in 2009 due o he delayed impac o he global inancial
c isis. A e 2015, he a e age GTFP inc eased mo e apidly, inc easing om 0.21 in 2015 o 0.441 in 2021.
In pa icula , du ing he pe iod om 2018 o 2021, he GTFP g ew a an e en highe a e, despi e he
economic decline a e he Co id-19 pandemic. The ob ained esul s a e no su p ising, gi en he se ies o
s a egies om he go e nmen a ge ed p ima ily a boos ing he bioeconomy o deal wi h he inc easing
clima e c isis (BMBF, 2020). In addi ion, he COVID-19 pandemic also con ibu ed o he educ ion o
ca bon emissions, leading o he sha p inc ease o GTFP.
85
Figu e 4.3: A e age annual GTFP in Ge many du ing he pe iod 2000–2021
The spa ial dis ibu ion o GTFP a he coun y le el, as shown in Figu e 4, changed in in ensi y o e ime.
Wes e n and sou he n coun ies end o ha e highe GTFPs. Coun ies in eas e n Ge many (e.g. Wi enbe g
and Salzlandk eis) ha e lowe bu inc easing GTFPs obse ed. No ewo hy, he e, mos coun ies
su ounding de eloped municipali ies, like F ank u and Munich, ha e la ge de elopmen po en ial and a
ela i ely high abso bing capaci y o in es men s om mo e de eloped economic cen es). As a esul , he
numbe o coun ies wi h he lowes GTFP (<0.2) declined s eadily o e he obse ed ime pe iod. In all
o he ca ego ies (> 0.2), he e was a clea inc ease end o e ime. By o e lapping he spa ial dis ibu ion
o GTFP wi h bioclus e s, we ound ha mos coun ies wi h bioclus e s (e.g., Po sdam) end o ha e highe
GTFPs. And hey showed spillo e e ec s on su ounding coun ies om 2000 o 2021. Fo ins ance, he
86
numbe o ci ies/coun ies a ound Düsseldo wi h a GTFP g ea e han 0.2 g adually inc eased om 2000
o 2021. F om 2015 o 2021, bo h he GTFP and i s spa ial spillo e e ec s con inued o g ow sha ply.
Figu e 4.4: Spa ial coun y-le el dis ibu ion o GTFP in 2000, 2005, 2010, 2015, and 2021
4.4.2 SDiD eg ession esul s
Table 4.2 summa izes he esul s o he pa ame e es ima ions using h ee e sions o he SDiD model,
de eloped and desc ibed in sec ion 4.3.2. The i s model (Model 4-1) only includes he ea men g oup
(Bio egion) and he con ol a iable (Domain), while i s ex ensions con ol co espondingly o he e ec s
o he size o he bioeconomy (Model 4-2) and o echnological inno a ion (Model 4-3). Va ying he
a iables, e eals ela i ely insigni ican e ec s, indica ing he models’ s abili y and obus ness.
93
ha es ablishing bioclus e s can imp o e he GTFP. O he coe icien s show simila alues, indica ing a
obus esul .
Table 4.5: PSM-SDiD eg ession esul s
Va iables
GTFP
Bio egion
0.020***
(3.090)
Gclus e
0.025***
(3.240)
BV
0.0001***
(34.630)
0.0002***
(45.390)
BE
-0.005***
(-12.170)
-0.009***
(-23.970)
Ra e
0.075***
(17.930)
0.071***
(18.150)
Reg_sha e
-0.035***
(-8.940)
-0.032***
(-9.18)
Domain
3.09e-7***
(6.590)
6.70e-7***
(12.190)
Fe
Yes
Yes

94
N
6226
6494
R2
0.146
0.278
No e: ***p<0.01, **p<0.05, *p<0.1. Bio egion deno es he key explana o y a iable. Fe indica es ime
ixed and indi idual ixed. N indica es he o al sample size. R2 deno es he coe icien o de e mina ion.
4.4.5 Media ing e ec s o bioclus e s on GTFP
The h ee media o s o echnological, agglome a ion, and s uc u al e ec s in Table 4.6 (0.008, 0.121, and
-0.004, espec i ely) show ha he agglome a ion e ec is much g ea e han he o he wo. I means
bioclus e s p ima ily p omo e GTFP by enhancing echnological inno a ion and ma ke capaci y. Column
(1) in Table 4.5 displays a posi i e and signi ican impac o bioclus e s on local GTFP in he esul s o he
SDiD base eg ession, p o iding a basis o he es s o he media ing e ec s ( om column 2 o column 7).
All he media ing e ec s passed he boo s ap es s. In column (3), he signi ican coe icien s o Bio egion
and Pa en indica e he e is a s ong media ion e ec (0.01), sugges ing ha bioclus e s can p omo e
echnological inno a ion in he local ci y. Despi e he coe icien o Pa en being nega i e, he esul s om
he boo s ap es con i m i s alidi y ([0.201,0.275]). This con ibu es o he p omo ion o g een p oduc ion
echnology and pollu ion con ol echnology, minimizing emissions, and consequen ly imp o ing he GTFP.
Thus, his inding suppo s H2. The media ing e ec o indus ial upg ading is nega i e because o he
nega i e coe icien o ST in column (7).
Table 4.6: Media ing eg ession esul s o Bio egions
Media ing model o Bio egions
Va iables
GTFP
(1)
Pa en
(2)
GTFP
(3)
MC
(4)
GTFP
(5)
ST
(6)
GTFP
(7)
Bio egion
0.020***
-0.236
0.022***
175.498***
-0.009*
-0.053***
0.014**
95
(3.090)
(-0.220)
(3.090)
(8.07)
(-1.650)
(-5.680)
(2.170)
Pa en
-0.001***
(-11.73)
MC
0.0002***
(51.230)
ST
-0.117***
(-12.910)
BV
BE
Media ing
e ec
0.008
0.121
-0.004
Con ol
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Fe
Yes
Yes
Yes
Yes
Yes
Yes
Yes
N
6226
6226
6226
6226
6226
6226
6226
R2
0.146
0.090
0.159
0.084
0.175
0.099
0.156
No e: ***p<0.01, **p<0.05, *p<0.1. Bio egion deno es he key explana o y a iable. Fe indica es ime
ixed and indi idual ixed. N indica es he o al sample size. R2 deno es he coe icien o de e mina ion.
The h ee media o s o echnological, agglome a ion, and s uc u al e ec s in Table 4.7 (-0.002, 0.03, and
0.005, espec i ely) show a simila esul , whe eby he agglome a ion e ec is much g ea e han he o he
wo. I means bioclus e s p ima ily p omo e GTFP by enhancing ma ke capaci y. Unlike he esul s in
96
Table 4.6, he coe icien o Gclus e in column (6) is nega i e. This implies ha he es ablishmen o g een
clus e s can es ic indus ial upg ading. This may be due o he ex e nal economies gene a ed by g een
clus e s, which boos he ou pu alue o seconda y indus ies and inc ease hei sha e in he o e all
economy. The coe icien o Gclus e in column (2) is signi ican ly posi i e, indica ing ha g een
bioclus e s can p omo e he in ensi y o echnological inno a ion. Howe e , he media ing e ec o
echnological inno a ion on GTFP is nega i e, meaning ha he o ce o g een clus e s ha p omo e
echnological inno a ion is g ea e han i s media ing e ec h ough echnological inno a ion on GTFP.
These esul s align wi h he indings o G a and B oekel (2020), who highligh he impac o Bio egion
ini ia i es in p omo ing echnological inno a ion only du ing he unding pe iod. This may be because a
simple inancial injec ion in o p ojec s o suppo echnological inno a ion is no able o cause he
eme gence o clus e s o decoupling economic g ow h om pollu ion (Kama h e al., 2022).
Table 4.7: Media ing eg ession esul s o g een clus e s
Media ing models o g een clus e s
Va iables
GTFP
(1)
Pa en
(2)
GTFP
(3)
MC
(4)
GTFP
(5)
ST
(6)
GTFP
(7)
Gclus e
0.018***
(2.320)
3.685***
(2.630)
0.021***
(2.780)
133.318**
(4.98)
-0.003
(-0.45)
-0.044***
(-3.87)
0.013*
(1.70)
Pa en
-0.001***
(-13.07)
MC
0.0002***
(50.62)
ST
-0.111***
(-13.03)
Media ing
e ec
-0.002
0.030
0.005
97
Con ol
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Fe
Yes
Yes
Yes
Yes
Yes
Yes
Yes
N
6512
6512
6512
6512
6512
6512
6512
R2
0.260
0.069
0.203
0.085
0.137
0.011
0.219
No e: ***p<0.01, **p<0.05, *p<0.1. Bio egion deno es he key explana o y a iable. Fe indica es ime
ixed and indi idual ixed. N indica es he o al sample size. R2 deno es he coe icien o de e mina ion.
4.4.6 He e ogenei y analysis wi h di e ence-in-di e ence-in-di e ences (DDD)
In his s udy, he le el o Bio egions and ypes o g een clus e s, a e in oduced o u he explo e he
he e ogeneous implica ions o bioclus e s on egional g een p oduc i i y. The DDD model is employed in
he p esen s udy o iden i y causal e ec s by compa ing di e ences in changes in ou come a iables be o e
and a e he in e en ion be ween ea men and con ol g oups. Th ough s uc u ing a hi d dimension o
he ea men g oup (Bio egion*Le el and Gclus e *Type, espec i ely in his s udy), DDD is used o
iden i y he he e ogeneous ea men e ec s o in e en ion policies ac oss g oups. Fo Bio egions, h ee
le els o Bio egions, namely local le el (Le el-1), egional le el (Le el-2), and s a e le el (Le el-3) a e
in oduced. The esul s shown in Table 4.8 illus a e ha e en hough all le els o Bio egions can con ibu e
o he inc ease o GTFP, he egional Bio egions ha e a signi ican ly posi i e impac on GTFP. The highe
he le el o he bioclus e s, he lowe he pa ame e is. This implies a lowe le el o bioclus e s may in ol e
a close coope a ion be ween i ms and esea ch ins i u ions as bioclus e s highly depend on indus y–
uni e si y– esea ch in eg a ion (You ie and Shapi a, 2008; Jeong e al., 2023).
Table 4.8: Reg ession esul s o DDD model o Bio egions
Va iables
GTFP
Bio egion*Le el-1
0.046
(1.54)
98
Bio egion*Le el-2
0.032**
(2.91)
Bio egion*Le el-3
0.010
(1.25)
BV
0.0001***
(35.63)
0.0001***
(35.38)
0.0001***
(35.03)
BE
-0.004***
(-12.09)
-0.004***
(-12.06)
-0.005***
(-12.12)
Ra io
0.076***
(18.13)
0.076***
(18.04)
0.076***
(18.06)
Reg_sha e
-0.0348***
(-8.99)
-0.035***
(-8.95)
-0.035***
(-8.99)
Domain
3.23e-7***
(6.91)
3.13e-7***
(6.69)
3.20e-7***
(6.83)
Fe
Yes
Yes
Yes
N
6226
6226
6226
R2
0.142
0.144
0.141
No e: ***p<0.01, **p<0.05, *p<0.1. Bio egion deno es he key explana o y a iable. Fe indica es ime
ixed and indi idual ixed. N indica es he o al sample size. R2 deno es he coe icien o de e mina ion.

99
Fo g een clus e s, h ee ypes o g een clus e s, namely ag icul u al agglome a ion (Type-1), g een
chemis y clus e s (Type-2), and bioeconomy dis ic s (Type-3) a e in oduced o es ima e he he e ogenei y.
The esul s shown in Table 4.9 illus a e ha all kinds o bioclus e s can p omo e GTFP. Among hem, he
highe coe icien o g een chemis y clus e s (Type-2) indica es a g ea e con ibu ion. The coe icien s o
he o he a iables a e qui e simila , showing he esul s a e ela i ely obus .
Table 4.9: Reg ession esul s o DDD model o g een clus e s
Va iables
GTFP
Gclus e *Type-1
0.019***
(1.31)
Gclus e *Type-2
0.077***
(3.78)
Cclus e *Type-3
0.018***
(2.66)
BV
0.0001***
(41.91)
0.0001***
(42.11)
0.0001***
(41.95)
BE
-0.009***
(-24.93)
-0.009***
(-25.19)
-0.009***
(-25.08)
Ra e
0.078***
(19.65)
0.078***
(19.60)
0.078***
(19.69)
Reg_sha e
-0.032***
(-8.94)
-0.032***
(-8.88)
-0.032***
(-8.95)
Domain
3.48e-7
(6.83)
3.45e-7
(6.78)
3.27e-7
(6.36)
Fe
Yes
Yes
Yes
100
N
6512
6512
6512
R2
0.226
0.263
0.262
No e: ***p<0.01, **p<0.05, *p<0.1. Bio egion deno es he key explana o y a iable. Fe indica es ime
ixed and indi idual ixed. N indica es he o al sample size. R2 deno es he coe icien o de e mina ion.
4.5 Discussion and conclusions
4.5.1 Discussion
The analysis shows ha he de elopmen o bioclus e s, as an ins i u ional inno a ion, can con ibu e o
imp o ing egional g een p oduc i i y. The ob ained esul s a e in line wi h ecen s udies, which ound
ha echnological inno a ion caused by egula ion policies can be a new engine o s imula e economic
g ow h and p o ec he en i onmen a he same ime (Al a ez-He anz e al., 2017; Balsalob e-Lo en e e
al., 2020). The esul s con ibu e o he body o he ele an li e a u e by showing ha he policy o
de eloping bioclus e s may imp o e he g een o al ac o p oduc i i y di ec ly and indi ec ly h ough
media ing e ec s om echnological inno a ion, ac o agglome a ion, and indus ial upg ading. As shown
by he alues o coe icien s in Table 4-5, bo h Bio egion (0.02***) and Gclus e (0.025***) as well as he
alue added o bioeconomy (0.0001*** and 0.0002***, espec i ely) and pa en applica ion a e in he
bioeconomy (0.075*** and 0.071***, espec i ely) can signi ican ly p omo e he inc ease o GTFP. The
media ing e ec o ac o agglome a ion is ound o be he bigges , as shown in Tables 4-6 (0.121) and 4-7
(0.03).
Di e ing om Du and Li (2022), whe e he media ing e ec includes go e nmen s a egic leade ship in
addi ion o echnological inno a ion and indus y upg ading, his s udy ins ead conside s he agglome a ion
e ec s. Speci ically, he agglome a ion e ec is ound o be he s onges media ing o ce when bioclus e s
a ec GTFP. This highligh s he oles o p oduc ion ac o lows and esou ce alloca ion in g een u ban
e iciency, con ibu ing new insigh s o he li e a u e in his ield (Rusiawan e al., 2015; A esagaoglu e al.,
2017; Liu and Xin, 2019). Compa ed wi h p e ious s udies whe e echnological inno a ion is deno ed by
he numbe o pa en s o R&D in es men (Du and Li, 2019; Luo e al., 2022), he p esen analysis sugges s
101
ha he egional sha e o pa en s and he a io o pa en applica ions ha a e highly linked wi h bioclus e s
can be ex ended o indica e he le el o echnological inno a ion. These obse a ions allow he conclusion
ha an alignmen o bioclus e s wi h echnological inno a ion, egional ma ke capaci y, and indus ial
s uc u e is needed o p omo e g een economic g ow h and sus ainable ansi ion o bioeconomy (Chen e
al., 2021; Van Lancke e al., 2016; Wilde and He mans, 2024).
The he e ogenei y analysis shows ha he impac o bioclus e s on GTFP a ies wi h he ypes o bioclus e s.
The ob ained esul s show ha all kinds o bioclus e s can p omo e GTFP, bu chemical g een clus e s make
he g ea es con ibu ion, which is in line wi h ecen s udies, which ound ha g een echnological
inno a ion can p omo e GTFP (Liu e al., 2024; Zhang and Wang, 2022). Me hodologically, his s udy
add esses he a ia ion in ea men iming when employing DiD (Callaway and San ’Anna, 2021) by
combining SDiD and PSM in he exogenei y es and DDD in he he e ogeneous analysis (Du and Li, 2022;
Guo and Zhong, 2022). In his way, he mechanism and syne gies behind egional g een p oduc i i y, no
de ec able om ede alo s a e-le el da a (Gu ney e al., 2019; Li e al., 2020; Wang and Jiang, 2020), can
be illumina ed. In addi ion, he obse ed inding ha a lowe le el o bioclus e s has a la ge impac on
GTFP also implies ha lowe -le el bioclus e s a e mo e e icien o imp o ing egional GTFP. Using da a
a he NUTS-3 le el can in o m he de elopmen o ailo ed, egionally speci ic policy ins umen s o
GTFP imp o emen .
4.5.2 Conclusions
In he con ex o he inc easing demands o en i onmen al and economic alignmen in a ious coun ies
wo ldwide, boos ing he bioeconomy o imp o e egional p oduc i i y h ough echnological inno a ion
and ins i u ional inno a ion is expec ed o be an e icien pa hway owa ds u u e sus ainable de elopmen .
Focusing on he 401 NUTS-3 le el (coun ies/ci ies) in Ge many om 2000 o 2021, he s udy es ima es he
impac o bioclus e s on g een p oduc i i y media ed by echnological inno a ion, indus ial upg ading, and
ma ke capaci y. We used a se ies o DiD models (SDiD, PSM-SDiD, and DDD) and media ing models,
and a i ed a ou main conclusions.
102
Fi s , o he obse ed pe iod 2000 o 2021, he egional p oduc i i y o he 401 coun ies/ci ies we e ound
o be spa ially au oco ela ed and exhibi ed clus e ing pa e ns, la gely e lec ing he o e lap be ween
bioclus e s and egional p oduc i i y. Second, de eloping bioclus e s, no ma e as Bio egions o g een
clus e s, has a posi i e e ec on GTFP, bo h in a di ec manne and indi ec manne h ough echnological
inno a ion and ma ke agglome a ion. Thi d, di e en ypes o bioclus e s ha e he e ogeneous impac s on
GTFP, wi h chemical g een clus e s making he g ea es con ibu ion. Fu he mo e, he la ge he le el o
he bioclus e s, he less con ibu ion he bioclus e s makes o GTFP. Fou h, egional GTFP can be
imp o ed h ough he posi i e e ec o he alue added o bioeconomy, while he e a e nega i e e ec s o
he numbe o employees in he bioeconomy, egional sha e o pa en s in he bioeconomy, and he le el o
bioclus e s.
The indings poin o a high po en ial o es ablishing bioclus e s o imp o e he egional g een o al ac o
p oduc i i y pe o mance. This po en ial can be ealized i policy measu es accoun o he egional
he e ogenei y in economic s eng h and esou ce endowmen a a possibly easonable spa ial scale. The
s udy hus ad oca es o he ansi ion o a bioeconomy, while highligh ing he ole o di e en egional
d i e s o GTFP. S ill, due o he limi ed a ailabili y o some coun y-le el da a, especially o R&D
in es men , and sec o al di e si y, he indings need o be e i ied by u he esea ch, which should also
accoun o he impac o he mo e ecen p og ammes o bioclus e s.
109
compensa ion and non- ood biomass supply in he decision making o a me s and hei impac s on he
s uc u al change o ag icul u e should be measu ed in he u u e. Adding a me s’ beha iou s unde he
ansi ion o bioeconomy in o he g een e o m o he CAP will acili a e a mo e cohesi e and e ec i e
bioeconomic ansi ion, bu also p omo e a uni ied app oach o sus ainable de elopmen .
Thi d, compa a i e s udies be ween Ge many and o he coun ies wi h di e en bioeconomic s a egies will
be use ul and yield aluable insigh s in o bes p ac ices and po en ial pi alls. This disse a ion mainly
ocuses on he case o Ge many, he leading coun y in p omo ing bioeconomy. Howe e , many ansi ion
coun ies, like China and India, ha e launched bioeconomy s a egies. Speci ically, he bioeconomy in
China go o o a la e s a and had a ela i ely small scale, bu de eloped apidly ( he a e age annual
g ow h a e has exceeded 20%). Up o 2018, China is he hi d la ges bioe hanol p oduce (2050 housand
ons in 2018) in he wo ld a e he Uni ed S a e (44100 housand ons in 2018) and B azil (21280 housand
on in 2018) (FAO, 2018). In pa icula , he e a e 11 p o inces ha ha e been selec ed as pilo a eas o
p omo e he p oduc ion and applica ion o bioe hanol. The e o e, a compa ison be ween Ge many and
China may p o ide he e ogeneous expe iences o o he coun ies a ound he wo ld.

110
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