Zhang, Ke-Cheng; Sa i, Adnan; Kchou i, Bilal; Bane jee, A indam; Wang, Lu
A icle
The h ee g eens: Inno a ion, inance, and axes.
Pe o mance analysis and u u e implica ions
Jou nal o Inno a ion & Knowledge (JIK)
P o ided in Coope a ion wi h:
Else ie
Sugges ed Ci a ion: Zhang, Ke-Cheng; Sa i, Adnan; Kchou i, Bilal; Bane jee, A indam; Wang, Lu
(2024) : The h ee g eens: Inno a ion, inance, and axes. Pe o mance analysis and u u e
implica ions, Jou nal o Inno a ion & Knowledge (JIK), ISSN 2444-569X, Else ie , Ams e dam, Vol. 9,
Iss. 4, pp. 1-11,
h ps://doi.o g/10.1016/j.jik.2024.100627
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The h ee g eens: Inno a ion, inance, and axes—Pe o mance analysis and
u u e implica ions
Ke-Cheng Zhang
a
, Adnan Sa i
b
, Bilal Kchou i
c
, A indam Bane jee
d
, Lu Wang
e,*
a
Depa men o Business Adminis a ion, Shandong Women’s’Uni e si y, Jinan, Shandong, China
b
School o Economics, Qingdao Uni e si y, Qingdao, Shandong, China
c
Depa men o Finance and Accoun ing, Adnan Kassa School o Business, Lebanese Ame ican Uni e si y, Bei u , Lebanon
d
SP Jain School o Global Managemen , Dubai, Uni ed A ab Emi a es
e
School o Accoun ing, Guangzhou College o Comme ce, Guangzhou, 511363, China
ARTICLE INFO
JEL Codes:
Q56
G28
O44
C53
Keywo ds:
G een inno a ion
G een axes
G een inance
SDGs
Clima e inance
Ca bon emissions
ABSTRACT
Add essing clima e challenges and achie ing he Sus ainable De elopmen Goals (SDGs) necessi a e a deep
unde s anding o how g een policies in luence ca bon emissions. The e o e, his s udy explo es he impac o
g een inance, inno a ion, and axa ion on ca bon emissions in OECD coun ies while accoun ing o economic
g ow h, enewable ene gy, and ene gy p oduc i i y. U ilizing he me hod o momen quan ile eg ession anal-
ysis, we ind ha g een inance, axes, and inno a ion signi ican ly educe emissions in OECD economies. The
impac s o g een inance and axes exhibi an inc easing end, wi h highe coe icien s a highe quan iles.
Con e sely, g een inno a ion shows a dec easing end, wi h coe icien s showing lowe magni ude a highe
quan iles. The esul s also show ha enewable ene gy and ene gy p oduc i i y signi ican ly mi iga e emissions.
In con as , economic g ow h co ela es posi i ely wi h CO₂emissions. Ou indings highligh he need o obus
policies ha in es in clean echnologies, b oaden g een inancial ins umen s, and enhance ene gy e iciency. In
line wi h he amewo ks es ablished a COP28, we emphasize he pi o al ole o bo h public and p i a e unding
in acili a ing he ansi ion o a low-ca bon economy.
In oduc ion
Coun ies a ound he wo ld, including hose in he OECD, a e acing
he massi e challenge o add essing clima e change. Despi e a ious
in e na ional ag eemen s and en i onmen al ini ia i es, he p oblem
con inues o de e io a e as ca bon emissions ise. Ca bon emissions
comp ise oughly 74% o all g eenhouse gases (Oli ie &Pe e s, 2020),
he leading con ibu o o global wa ming. This g owing c isis is high-
ligh ed by he subs an ial inances s ill di ec ed owa d ossil uels. In
2022, G-20 na ions collec i ely spen $1.3 illion on ossil uel sub-
sidies, unds ha could ha e been used o sola powe . Fossil uel
suppo in 2021 also saw a sha p ise, eaching almos $600 billion, a
14% inc ease om 2017 (Cuming &Godeme, 2023; Teske e al., 2022).
As pe Bloombe gNEF, among he bigges con ibu o s, Saudi A abia
spen $2309 pe capi a, ollowed by A gen ina, $665, Russia $492,
Aus alia a $363, and F ance a $344 pe capi a. The inc ease in ossil
uel expendi u es highligh s he pe sis en di icul ies in add essing
clima e change.
One o he mos p essing challenges o ecen yea s is clima e
change’s ha m ul impac on humani y, p ima ily d i en by ca bon
emissions (Uma e al., 2024). I is impe a i e o add ess his challenge
and implemen measu es o educe CO₂emissions o ensu e u u e
gene a ions’wel a e, as hese changing wea he pa e ns pose a signi -
ican isk o human su i al (IPCC, 2021;Zhao e al., 2022). The OECD
economies a e acing many obs acles in hei e o s o add ess he
inc easing emissions, such as he balance be ween economic g ow h and
en i onmen al sus ainabili y and he ansi ion o cleane ene gy sou -
ces. Nume ous coun ies, such as he Uni ed S a es, he Eu opean Union
(EU), he Uni ed Kingdom, and Japan, ha e ou lined mi iga ion s a e-
gies and commi ed o achie ing ne -ze o emissions by 2050. In he G-20
economies, a ew coun ies ha e employed ca bon p icing schemes o e
$40 pe me ic on, a h eshold o educing emissions (Bloombe gNEF,
2023). Mo eo e , he e ec i eness o ca bon-p icing policies ha ac-
coun o 21% o global GHG emissions is es ic ed by hei low p ices
* Co esponding au ho .
E-mail add esses: [email p o ec ed] (K.-C. Zhang), [email p o ec ed] (A. Sa i), [email p o ec ed] (B. Kchou i), [email p o ec ed]
(A. Bane jee), [email p o ec ed] (L. Wang).
Con en s lis s a ailable a ScienceDi ec
Jou nal o Inno a ion &Knowledge
jou nal homepage: www.else ie .com/loca e/jik
h ps://doi.o g/10.1016/j.jik.2024.100627
Recei ed 30 Augus 2024; Accep ed 12 No embe 2024
Jou nal o Inno a ion & Knowledge 9 (2024) 100627
A ailable online 26 No embe 2024
2444-569X/© 2024 The Au ho s. Published by Else ie España, S.L.U. on behal o Jou nal o Inno a ion & Knowledge. This is an open access a icle unde he CC
BY-NC-ND license (
h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/ ).
and concessions, including he ee emission allowances (Cuming &
Godeme, 2023). Howe e , u he s eps mus be aken o educe en i-
onmen al pollu ion and i s impac .
In o de o ackle clima e change, COP28 placed emphasis on u he
imp o ing he clima e inance amewo k by g adually mobilizing unds
o elimina e he subsidies o ossil uels and achie e he ne ze o ca bon
emissions goals se by he coun ies. Du ing COP 28, clima e inance
emained a opic o in e es , highligh ing i s signi icance o ad ancing
global clima e ac ion and ealizing he objec i es o he Pa is Ag ee-
men . Songwe e al. (2022) emphasized ha by 2030, eme ging ma ke s
and de eloping coun ies, excluding China, need o equi e an annual
in es men o $2.4 illion, a ou old inc ease om cu en le els, o
mee clima e goals. To add ess he sho all in clima e inancing,
coun ies ha e in oduced he clima e inance amewo k a COP 28.
This ini ia i e aims o mobilize public and p i a e in es men s, ocusing
on concessional inance ha needs o inc ease i e old by 2030. By ha
yea , mul ila e al de elopmen banks a e expec ed o iple hei annual
lending o $390 billion. Thei p ima y goal is o lowe capi al cos s and
enhance suppo o clima e adap a ion and mi iga ion p ojec s
(Bha acha ya e al., 2023). The e o e, he in eg a ion o clima e
inance, axa ion, and inno a ion is impo an o achie e ne ze o ca bon
emissions. Fig. 1 illus a es he ends in g een inance (GF), axa ion
(GT), and inno a ion (GI) o OECD economies, highligh ing hei col-
lec i e impac on ad ancing global clima e objec i es. GF exhibi ed a
s eady ise s a ing in 1995, eaching a peak alue o 1.41 in 2011. A e
his peak, GF showed luc ua ions and declined o 1.24 by 2020. Simi-
la ly, GI ollowed an upwa d ajec o y, a aining i s highes poin a
1.23 in 2011 be o e expe iencing sligh declines in subsequen yea s. In
con as , GT emained ela i ely s able wi h a alue o 0.4 and a sligh
dec ease a e 2010, eaching 0.36 in 2020.
Based on he abo e discussion, his s udy in ends o explo e he ole
o g een inance, inno a ion, and axa ion in ad ancing sus ainable
de elopmen wi hin selec ed OECD economies om 1990 o 2022. This
s udy’s p ima y goal is o analyze how g een inno a ion, g een inance,
axes, economic g ow h, ene gy p oduc i i y, and enewable ene gy
in luence ca bon emissions. We u ilize he me hod o momen s and
boo s ap quan ile eg ession analysis o in es iga e hese ela ionships
ac oss a ious quan iles o CO₂emissions. This comp ehensi e app oach
in eg a es mul iple ace s o CO₂emissions, including economic expan-
sion, g een echnologies, and policy-d i en inancial ins umen s like
g een inance and g een en i onmen al axes. By examining hese g een
policy indica o s’indi idual and collec i e impac s, we o e a esh
pe spec i e on s a egies ha can e ec i ely educe ca bon oo p in s in
OECD na ions.
Li e a u e e iew
This sec ion e iews he exis ing li e a u e on he ole o g een
inance, axes, and inno a ion in achie ing sus ainable de elopmen
goals (SDGs). Khan e al. (2022) and S. Li and Shao (2022) e ealed ha
g een inance can signi ican ly dec ease ca bon emissions. The eme -
gence o g een inance as a majo inancial pa adigm has a ac ed
conside able a en ion in esea ch and in e na ional policy discussions
(Zhang e al., 2019). Hu e al. (2023) emphasized ha g een inance is a
c ucial ool o achie ing a low-ca bon economic ansi ion. A s udy by
Li and Jia (2017) also a gued ha sus ainable inance ep esen s he
mos e icien app oach o mi iga ing en i onmen al deg ada ion. Gu
e al. (2024) ind ha g een inance plays a signi ican ole in add essing
en i onmen al challenges. Howe e , hei s udy also highligh s ha he
e ec i eness o g een inance hea ily depends on egional ac o s, such
as he le el o inancial de elopmen and he ma u i y o c edi and
capi al ma ke s.
Do˘
gan e al. (2022) explo ed he in luence o en i onmen al axes on
CO
2
emissions o G-7 economies and showed ha axes educe emis-
sions. They a gued ha en e p ises will ansi ion o cleane p oduc ion
p ocesses in esponse o s ic en i onmen al ax egula ions. Lin and Jia
(2019) also a gued ha imposing axes on he ossil uel sec o can
signi ican ly mi iga e emissions. Simila ly, Aydin and Esen (2018)
explo ed he impac o en i onmen al axes on CO
2
emissions in i een
Eu opean Union economies. They showed an asymme ical ela ionship
o en i onmen al axes, indica ing ha once h eshold le els o o al,
ene gy, and pollu ion axes a e su passed, hei impac on CO2 emis-
sions shi s om insigni ican ly posi i e o signi ican ly nega i e. In
addi ion, Yunzhao (2022) demons a ed ha ca bon emissions a e
educed in E-7 economies h ough he implemen a ion o en i onmen al
axes, enewable ene gy, and eco-inno a ion. The s udy conduc ed by
Mo ley (2012) on he EU and No way demons a ed a nega i e co e-
la ion be ween pollu ion and en i onmen al axa ion. Howe e , no
co ela ion was obse ed be ween axes and ene gy consump ion.
Mo e ecen ly, he li e a u e on clima e change has paid signi ican
Fig. 1. G een Finance, Inno a ion and Taxes G aph.
Sou ce: Au ho ’s own calcula ions.
K.-C. Zhang e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100627
2
conside a ion o g een inno a ion. Sun e al. (2021) asse ha g een
inno a ion educes he expense o enewable ene gy and accele a es he
ansi ion om ossil uels in economies. Fu he mo e, he s udy a gued
ha CO
2
cap u e and s o age a e enhanced by g een inno a ion. Many
s udies ha e demons a ed a simila posi i e in luence o g een inno-
a ion on en i onmen al quali y (N. Amin e al., 2023;Du e al., 2021;
Makpo che e al., 2024;Uma &Sa i, 2023;Xu e al., 2024). On he o he
hand, Weina e al. (2016) in es iga ed he co ela ion be ween CO2
emissions and g een inno a ion in 95 I alian p o inces om 1990 o
2010. The indings indica ed ha al hough g een echnology has
inc eased en i onmen al p oduc ion, i has no ye made a majo
con ibu ion o en i onmen al sus ainabili y. Simila ly, Razzaq e al.
(2021) ound ha he emissions educ ion e ec o g een inno a ion was
only no able a he highes emissions quan iles in B azil, Russia, India,
and China. Con e sely, a he lowes emissions quan iles, g een inno-
a ion was posi i ely o weakly co ela ed wi h emissions.
This s udy ills he gap in he li e a u e by in es iga ing he ela-
ionship be ween g een inno a ion, axes, inance, and consump ion-
based ca bon emissions o selec ed OECD economies om 1990 o
2022, conside ing con ol a iables o ene gy p oduc i i y, enewable
ene gy, and economic g ow h. We employ he Me hod o Momen s and
boo s ap quan ile eg ession analysis o in es iga e hese ela ionships
ac oss a ious quan iles o CO₂emission dis ibu ions.
Da a and me hodology
Da a and model speci ica ion
This s udy u ilizes da a om di e en sou ces, om 1990 o 2022,
ocusing on selec ed OECD economies. The da a o CO₂emissions ep-
esen ing consump ion-based ca bon emissions is sou ced om he
Global Ca bon A las (GCA, 2023). This di ec ly ies in o SDG 13 o
clima e ac ion, as i helps measu e he e ec i eness o policies aimed a
educing ca bon emissions. Economic G ow h (EG), measu ed by GDP
om he Wo ld Bank (2022), is essen ial o SDG 8, which deals wi h
decen wo k and economic g ow h, as i highligh s he ole o economic
ac i i ies in os e ing employmen and d i ing sus ainable g ow h. En-
e gy P oduc i i y (EP), de ined as GDP pe uni o o al ene gy supply
(TES) and sou ced om he OECD da abase, suppo s bo h SDG 8 and
SDG 7 o a o dable and clean ene gy by p omo ing mo e e icien en-
e gy use in economic ac i i ies, which is c ucial o sus aining economic
g ow h while minimizing en i onmen al impac . Renewable Ene gy
(REN), measu ed as enewable ene gy consump ion as a pe cen age o
o al ene gy consump ion aken om he Wo ld Bank (2022), is c i ical
o achie ing SDG 7, which emphasizes he ansi ion o clean and
enewable ene gy sou ces. G een Inno a ion (GI) is measu ed as he
de elopmen o en i onmen - ela ed echnologies ha align wi h SDG 9
o indus y, inno a ion, and in as uc u e. Inno a ion in g een ech-
nologies is essen ial o d i ing sus ainable indus ializa ion and
os e ing esea ch and de elopmen , which in u n also suppo s SDG 12
o Responsible Consump ion and P oduc ion by leading o mo e e icien
esou ce use and sus ainable p oduc ion p ocesses. G een Finance (GF),
measu ed by enewable ene gy public RD&D budge as a pe cen age o
o al ene gy RD&D, is a key d i e o SDG 9 and SDG 7 by unding he
de elopmen and deploymen o clean ene gy echnologies. Las ly,
G een Taxes (GT), measu ed by en i onmen ally ela ed axes as a
pe cen age o GDP, di ec ly suppo s SDGs 7, 13, 14, and 15 by
p o iding economic incen i es and enhancing he shi owa ds cleane
ene gy sou ces o educe ca bon emissions. These a iables emphasize
he ole o g een policies and inno a ion in achie ing SDG goals. The
basic econome ic models a e gi en as ollows:
CO2=β1Economic G ow h (EG) + β2Ene gy P oduc i i y (EP)
+β3Renewable Ene gy (REN) + β4G een Inno a ion (GI) + ϵ
(1)
Fig. 2. Theo e ical F amewo k.
Sou ce: Au ho s’Concep ualiza ion.
K.-C. Zhang e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100627
3
CO2=β1Economic G ow h (EG) + β2Ene gy P oduc i i y (EP)
+β3Renewable Ene gy (REN) + β4G een Inno a ion (GI)
+β5G een Finance (GF) + ϵ(2)
CO2=β1Economic G ow h (EG) + β2Ene gy P oduc i i y (EP)
+β3Renewable Ene gy (REN) + β4G een Inno a ion (GI)
+β5G een Finance (GF) + β6G een Taxes (GT) + ϵ(3)
Empi ical me hodology
In ou s udy, we unde ake a comp ehensi e analysis app oach o
examine he in luence o g een inance, axes, and inno a ion on ca bon
emissions. We commence he analysis by p o iding desc ip i e s a is ics
o o e a p elimina y insigh in o he a iables. In o de o accoun o
c oss-sec ional dependencies and he e ogeneous slopes ac oss uni s and
ensu e obus es ima ions, we inco po a e he C oss-Sec ional Depen-
dence and slope he e ogenei y analysis, as ecommended by Pesa an
(2004) and Pesa an and Yamaga a (2008), espec i ely. We employ he
uni oo es , de eloped by Im e al. (2003), o con i m he s a iona i y
o ou a iables. We subsequen ly in es iga e he long- e m equilib ium
ela ionships among he a iables using Wes e lund’s (2005) coin e-
g a ion analysis, which enables us o de e mine whe he a s able,
long- e m associa ion exis s.
We u ilize he Me hod o Momen s Quan ile Reg ession (MMQR), as
p esen ed by Machado and Sil a (2019), o ou baseline analysis. The
MMQR me hod enables us o in es iga e he ex en o which he inde-
penden a iables in luence CO₂emissions a a ious poin s in he dis-
ibu ion a he han ocussing on he mean. The equa ion o he abo e
model is gi en as:
CO2=
τ
∑[β
τ
1EG +β
τ
2EP+β
τ
3REN +β
τ
4GI]m(
τ
) + ϵ(4)
CO2=
τ
∑[β
τ
1EG +β
τ
2EP +β
τ
3REN +β
τ
4GI +β
τ
5GF]m(
τ
) + ϵ(5)
CO2=
τ
∑[β
τ
1EG +β
τ
2EP +β
τ
3REN +β
τ
4GI +β
τ
5GF +β
τ
6GT]m(
τ
) + ϵ
(6)
In he abo e equa ions, β
τ
ishows he Quan ile-speci ic coe icien s
es ima ed ia momen condi ionsm(
τ
)shows he Quan ile unc ion, ∑
τ
In eg a es ac oss all quan iles, and ϵis he e o e m. Addi ionally, as a
obus ness es analysis, we ha e aken he boo s ap quan ile eg ession
analysis.
Theo e ical amewo k
Based on he En i onmen al Kuzne s Cu e (EKC) hypo hesis, we
a gue ha al hough OECD coun ies a e de eloped economies, eco-
nomic g ow h wi hin hese na ions s ill con ibu es o inc eased ca bon
emissions. Howe e , he adop ion o g een ene gy sou ces and inno a-
i e echnologies leads o a educ ion in emissions. This indica es ha
e en in ad anced economies, p oac i e implemen a ion o sus ainable
p ac ices is essen ial o decouple economic g ow h om en i onmen al
deg ada ion (Voumik e al., 2022). The inno a ion heo y and
Resou ce-based iew heo y explain he nega i e in luence o g een
inno a ion, g een axes, g een inance, enewable ene gy, and ene gy
p oduc i i y on ca bon emissions. Based on he inno a ion heo y, he
de elopmen o ad anced, ene gy-e icien echnologies will lead o a
educ ion in emissions. The Resou ce-Based View (RBV) heo y explains
ha g een axa ion and inancial cons ain s d i e companies o adop
sus ainable p ac ices ha educe CO₂emissions. This enables coun ies
o inc ease p oduc ion e iciency while consuming less ene gy, pa ic-
ula ly h ough cleane ene gy sou ces, and con ibu es o emission e-
duc ions. These ac o s unde sco e he c i ical ole o g een policies in
achie ing a low-ca bon economy (Ouyang, Guan, &Yu, 2023; Sha ma,
Ve ma, Shahbaz, Gup a, &Chop a, 2022; Wang, Jin, Qin, Su, &Uma ,
2024). Fig. 2 o e iews he expec ed ela ionships based on li e a u e
e iew and heo e ical easoning.
Resul s
The desc ip i e s a is ics o he key a iables u ilized in his
esea ch a e all summa ized in de ail in Table 1. A mode a e a iabili y
is indica ed in he consump ion-based ca bon emissions (CO2), which
ha e a mean o 2.24 and a s anda d de ia ion o 0.449, anging om
1.597 o 3.2. A dis ibu ion ha is conside ably la e and igh -skewed
han a no mal dis ibu ion is sugges ed by he sligh posi i e skewness
(0.52) and ku osis o 1.938. The esul s o economic g ow h (EG)
demons a e a nea ly symme ic dis ibu ion wi h a small pla yku ic
endency. The da a spans om 10.475 o 12.797, wi h a mean o 11.678
and low a iabili y (s anda d de ia ion o 0.54), skewness (0.155), and a
ku osis o 2.159. G een echnological inno a ion (GI) anges om −1 o
1.959, wi h a mean o 0.935 and a g ea e s anda d de ia ion o 0.578,
indica ing no able a ia ions. A ku osis o 2.702 and nega i e skewness
(−0.531) emphasize he a iabili y in inno a ion up ake. Ene gy p o-
duc i i y (EP) has a mode a ely peaked ku osis o 3.252, a sligh ly le -
skewed dis ibu ion (−0.267), and a mean o 4.001 wi h li le a iance
(s anda d de ia ion o 0.159). Renewable ene gy consump ion (REN)
esul s ange om −0.222 o 1.763, wi h a mean o 1.031 and a s anda d
Table 1
Desc ip i e S a is ics.
Va iables Mean S d. De . Min Max Skew. Ku . Adj Chi2 (2) P ob>chi2
CO2 2.24 0.449 1.597 3.2 0.52 1.938 122.930 0.000
EG 11.678 0.54 10.475 12.797 0.155 2.159 41.557 0.000
EP 4.001 0.159 3.612 4.562 −0.267 3.252 7.327 0.026
REN 1.031 0.421 −0.222 1.763 −0.634 2.911 25.013 0.000
GI 0.935 0.578 −1 1.959 −0.531 2.702 19.241 0.000
GF 1.258 0.383 −1 1.912 −1.38 6.474 98.711 0.000
GT 0.401 0.131 0.037 0.729 −0.414 3.426 12.899 0.002
Table 2
CD and SH Analysis.
CD Analysis
Va iables CD-S a P-Value
CO2 30.89*** 0.000
EG 54.57*** 0.000
GI 52.65*** 0.000
EP 52.82*** 0.000
REN 47.29*** 0.000
GF 9.44*** 0.000
GT 10.93*** 0.000
Slope he e ogenei y (SH) analysis
Models Del a Adj Del a
Model-1 (CO2 EG EP REN GI) 19.631*** 21.954 ***
Model-2 (CO2 EG GI EP REN GI GF) 15.631*** 18.222***
Model-3 (CO2 EG GI EP REN GI GF GT) 11.740*** 14.538***
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de ia ion o 0.421. The ku osis o 2.911 and nega i e skewness
(−0.634) indica e ha mo e na ions a e adop ing enewable ene gy a
he highe end o he spec um. G een inance (GF) has a mean o 1.258,
a dis ibu ion (−1.380 skewness), a peaked ku osis o 6.474, and
minimal a iabili y (s anda d de ia ion o 0.383). Wi h a peaked
dis ibu ion (ku osis o 3.426), a wide ange om 0.37 o 0.729,
nega i e skewness (−0.414), and a mean o 0.401, en i onmen al axes
(GT) show high skewness and a iabili y. These esul s sugges ha
while some OECD economies ha e e y low o negligible en i onmen al
ax le els, o he s ha e mo e obus ax policies. All a iables exhibi
subs an ial de ia ions om no mal dis ibu ions, as shown by he Chi2
es s o no mali y, wi h p- alues o 0.000 o CO
2
, EG, REN, GI, and GF.
The p esen esea ch unde sco es he da ase ’s he e ogeneous na u e
and posi s no able luc ua ions in pi o al indica o s such as inno a ion,
enewable ene gy, and en i onmen al axa ion. These insigh s will be
c ucial in comp ehending he ela ionship be ween hese a iables and
ca bon emissions.
Table 2 shows C oss-Sec ional Dependence (CD) and Slope He e o-
genei y Analysis indings, which help explain coun y in e connec ion
and he e ogenei y. CO2, EG, REN, and GT ha e CD-s a p- alues o
0.000. These indings e eal conside able c oss-sec ional dependency,
sugges ing global issues like in e na ional comme ce and en i onmen al
acco ds a ec hese a iables ac oss na ions. G een inno a ion (GI),
ene gy p oduc i i y (EP), and G een Finance (GF) also exhibi sub-
s an ial connec ion wi h p- alues o 0.000, demons a ing in e -
connec i i y in echnology, ene gy consump ion, and inancing. The
slope he e ogenei y analysis de e mines whe he independen a iables
and CO
2
emissions a y conside ably be ween na ions. The i s model,
which includes CO
2
, EG, GI, EP, and REN, has a Del a s a is ic o 19.631
and an adjus ed Del a o 21.954, sugges ing slope a iabili y among
Table 3
Uni oo analysis.
Va iable S a is ic ( -ba ) a I(0) S a is ic (T-ba ) a I(1)
CO2 −1.1523 −5.8397***
EG −1.2493 −4.5499***
GI −1.6740 −6.1079***
EP 0.1400 −6.1113***
REN −0.0035 −5.8961***
GF −2.3192***
GT −1.0789 −4.3829***
Table 4
Coin eg a ion Analysis.
Models S a P- alue
Model-1 Va iance Ra io (CO2 EG EP REN GI) −1.5291* 0.0631
Model-2 Va iance Ra io (CO2 EG GI EP REN GI GF) −1.9869** 0.0235
Model-3 Va iance Ra io (CO2 EG GI EP REN GI GF GT) −1.7913** 0.0366
Table 5
MMQR Model-1.
loca ion scale Q(25) Q(50) Q(75) Q(90)
EG 0.839*** −0.0202*** 0.858*** 0.843*** 0.820*** 0.807***
(0.0122) (0.00707) (0.0116) (0.0119) (0.0160) (0.0192)
EP −0.905*** −0.00704 −0.899*** −0.904*** −0.912*** −0.916***
(0.0359) (0.0209) (0.0342) (0.0347) (0.0471) (0.0568)
REN −0.187*** −0.00233 −0.185*** −0.187*** −0.190*** −0.191***
(0.0134) (0.00778) (0.0127) (0.0129) (0.0175) (0.0211)
GI −0.188*** 0.0228*** −0.209*** −0.192*** −0.166*** −0.152***
(0.0110) (0.00641) (0.0105) (0.0108) (0.0145) (0.0175)
Cons an −3.559*** 0.340*** −3.875*** −3.622*** −3.231*** −3.024***
(0.179) (0.104) (0.170) (0.175) (0.235) (0.284)
Fig. 3. MMQR Model-1.
K.-C. Zhang e al. Jou nal o Inno a ion & Knowledge 9 (2024) 100627
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Table 6
Model-2.
loca ion scale Q(25) Q(50) Q(75) Q(90)
EG 0.850*** −0.0121 0.861*** 0.851*** 0.840*** 0.829***
(0.0132) (0.00762) (0.0136) (0.0131) (0.0159) (0.0210)
EP −0.703*** 0.0429* −0.744*** −0.707*** −0.668*** −0.627***
(0.0411) (0.0236) (0.0421) (0.0408) (0.0493) (0.0650)
REN −0.156*** 0.0231*** −0.178*** −0.158*** −0.137*** −0.115***
(0.0117) (0.00675) (0.0121) (0.0117) (0.0142) (0.0186)
GI −0.186*** 0.0181*** −0.203*** −0.188*** −0.171*** −0.154***
(0.0107) (0.00615) (0.0110) (0.0106) (0.0129) (0.0169)
GF −0.0605*** −0.0119 −0.0492*** −0.0594*** −0.0701*** −0.0815***
(0.0162) (0.00930) (0.0166) (0.0160) (0.0194) (0.0256)
Cons an −4.454*** 0.0281 −4.481*** −4.457*** −4.431*** −4.404***
(0.190) (0.109) (0.194) (0.188) (0.227) (0.300)
Fig. 4. MMQR Model-2.
Table 7
Model-3.
loca ion scale Q(25) Q(50) Q(75) Q(90)
EG 0.840*** −0.0257** 0.865*** 0.842*** 0.817*** 0.799***
(0.0184) (0.0101) (0.0193) (0.0185) (0.0221) (0.0266)
EP −0.698*** 0.0867*** −0.783*** −0.706*** −0.620*** −0.560***
(0.0524) (0.0289) (0.0548) (0.0527) (0.0629) (0.0756)
REN −0.163*** 0.0154* −0.178*** −0.165*** −0.150*** −0.139***
(0.0147) (0.00808) (0.0153) (0.0146) (0.0175) (0.0212)
GI −0.183*** 0.0217*** −0.204*** −0.185*** −0.164*** −0.148***
(0.0128) (0.00706) (0.0134) (0.0129) (0.0154) (0.0185)
GF −0.0497*** −0.00413 −0.0457*** −0.0494*** −0.0535*** −0.0563**
(0.0169) (0.00930) (0.0176) (0.0168) (0.0202) (0.0244)
GT −0.122** −0.0952*** −0.0295 −0.114** −0.208*** −0.274***
(0.0570) (0.0314) (0.0596) (0.0573) (0.0684) (0.0823)
Cons an −4.318*** 0.0450 −4.362*** −4.322*** −4.278*** −4.247***
(0.212) (0.117) (0.222) (0.210) (0.253) (0.307)
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na ions. The second model wi h GF has a Del a (15.631) and adjus ed
Del a (18.222) s a is ics. Simila ly, he hi d model wi h GT has educed
Del a (11.740) and adjus ed Del a (14.538) s a is ics bu is signi ican ,
demons a ing ha hese ac o s minimize slope he e ogenei y. These
indings emphasize he necessi y o accoun ing o c oss-sec ional de-
pendency and slope he e ogenei y when s udying CO
2
emissions.
Table 3 gi es he uni oo analysis. The esul s show ha all he
a iables (CO
2
EG EP REN, GI, and GT) a e s a iona y a he i s di -
e ence, whe eas GF is s a iona y a he le el. In Table 3, g een inance
(GF) is s a iona y a le el I(0), wi h signi ican -ba s a is ics o
−2.3192. Fo he emaining a iables, s a iona i y is achie ed a he
i s di e ence I(1), wi h CO
2
showing a -ba s a is ic o −5.8397,
economic g ow h (EG) a −4.5499, g een inno a ion a −6.1079, ene gy
p oduc i i y (EP) a −6.1113, enewable ene gy (REN) a −5.8961, and
en i onmen al axes (GT) a −4.3829, all signi ican a he 1% le el.
Table 4 gi es he coin eg a ion analysis and shows he p esence o
long- un ela ionships among he a iables ac oss all h ee models.
Model 1 shows signi icance a 10 pe cen (p- alue =0.0631), while
Models 2 and 3 display s onge e idence o coin eg a ion wi h p- alues
o 0.0235 and 0.0366, espec i ely. The dec easing p- alues and
consis en signi icance as mo e a iables a e added indica e ha
expanding he model enhances he de ec ion o s able long- e m e-
la ionships among he a iables.
Table 5 gi es esul s o he MMQR Model-1 and shows he in luence
o he independen a iables on consump ion-based CO₂emissions.
Economic G ow h (EG) consis en ly has a posi i e and signi ican impac
on CO₂emissions ac oss all quan iles, wi h a dec easing end as he
quan iles inc ease (0.858 a he 25 h quan ile o 0.807 a he 90 h
quan ile), indica ing ha as economic g ow h ises, he ca bon emission
diminishes. Ene gy P oduc i i y (EP) shows a s ong nega i e e ec on
CO₂emissions ac oss all quan iles, becoming mo e nega i e as quan iles
inc ease (−0.899 a he 25 h quan ile o −0.916 a he 90 h quan ile),
emphasizing he g owing impo ance o ene gy e iciency a highe
emissions le els. Renewable Ene gy (REN) also has a nega i e signi i-
can and mo e s able e ec ac oss quan iles (−0.185 a he 25 h quan ile
o −0.191 a he 90 h quan ile), indica ing consis en bene i s om
enewable ene gy adop ion ac oss all emissions le els. In e es ingly,
G een Inno a ion (GI) displays a gene ally nega i e e ec on CO₂
emissions, bu wi h a sligh ly posi i e scale coe icien (0.0228) indi-
ca ing a mino inc ease a ce ain in e media e poin s, hough i s o e all
impac emains nega i e ac oss highe quan iles (−0.209 a he 25 h
quan ile o −0.152 a he 90 h quan ile). These esul s highligh ha
while economic g ow h d i es emissions, imp o emen s in ene gy p o-
duc i i y, enewable ene gy, and g een inno a ion play c i ical oles in
educing CO₂emissions, wi h hei in luence becoming s onge a
highe quan iles. Fig. 3 isualizes hese ends, illus a ing he a ying
impac s ac oss di e en emissions le els.
The esul s o Economic G ow h (EG), Ene gy P oduc i i y (EP),
Renewable Ene gy (REN), and G een Inno a ion (GI) in Table 6 a e
consis en wi h hose in Table 5, showing simila ends ac oss all
quan iles. The ocus he e is on he e ec o G een Finance (GF) on CO₂
emissions. G een inance has a signi ican nega i e in luence on CO₂
emissions ac oss all quan iles, wi h highe coe icien s a highe quan-
iles (−0.0492 a he 25 h quan ile o −0.0815 a he 90 h quan ile).
Fig. 5. MMQR Model-3.
Table 8
Boo s ap Quan ile Reg ession (BQR) Analysis Model-1.
Dep-CO2 Q25 Q50 Q75 Q90
EG 0.863*** 0.854*** 0.838*** 0.765***
(0.0101) (0.0136) (0.0182) (0.0228)
EP −0.911*** −0.831*** −0.914*** −0.888***
(0.0309) (0.0683) (0.0525) (0.0377)
REN −0.189*** −0.154*** −0.196*** −0.241***
(0.0128) (0.0182) (0.0336) (0.0286)
GI −0.225*** −0.191*** −0.171*** −0.117***
(0.0131) (0.0183) (0.0199) (0.0198)
Cons an −3.865*** −4.075*** −3.427*** −2.624***
(0.182) (0.362) (0.296) (0.280)
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Wi h he inc ease in g een inance ini ia i es, ca bon emissions a e
educed signi ican ly wi h highe coe icien s. The inc easing impac o
GF a highe quan iles sugges s ha in es men s in g een p ojec s, such
as enewable ene gy and clean echnology, a e pa icula ly e ec i e in
high-emission con ex s whe e la ge inancial commi men s a e needed
o d i e signi ican en i onmen al imp o emen s. Fig. 4 shows he e-
sul s as a g aph, illus a ing he a ying impac s ac oss he quan iles.
The esul s o EG, EP, REN, GI, and GF in Table 7 a e consis en wi h
hose in Tables 5 and 6, showing simila ends ac oss all quan iles.
Table 7 p o ides e idence o he ela ionship be ween G een Taxes
(GT) and CO₂emissions. GT shows a nega i e and signi ican impac on
CO₂emissions, wi h i s e ec becoming s onge a highe quan iles. Fo
ins ance, he e ec anges om −0.0295 a he 25 h quan ile o −0.274
a he 90 h quan ile. This inc easing impac sugges s ha g een axes a e
pa icula ly e ec i e in educing emissions in high-emission con ex s,
whe e hey likely impose signi ican cos s on pollu ing ac i i ies,
he eby incen i izing a shi owa ds cleane p ac ices and echnologies.
The s onge e ec a highe quan iles could be a ibu ed o he g ea e
eliance on ene gy-in ensi e ac i i ies in high-emission scena ios, whe e
g een axes can lead o subs an ial educ ions by pushing i ms and
consume s owa ds mo e sus ainable al e na i es (Aydin &Esen, 2018).
Fig. 5 isualizes hese ends, illus a ing he a ying impac s ac oss
di e en le els o emissions.
The boo s ap quan ile eg ession analysis o Model-1 con i ms he
obus ness o he ea lie esul s, as p esen ed in Table 8. Economic
G ow h (EG) consis en ly shows a posi i e and signi ican e ec on CO₂
emissions ac oss all quan iles, hough i s in luence dec eases sligh ly a
highe quan iles ( om 0.863 a Q25 o 0.765 a Q90). Ene gy P oduc-
i i y (EP) main ains a s ong nega i e ela ionship wi h emissions
ac oss all quan iles, ein o cing i s c i ical ole in educing CO₂emis-
sions. Simila ly, Renewable Ene gy (REN) con inues o exhibi a nega-
i e and signi ican impac , wi h i s e ec becoming mo e p onounced a
highe quan iles (−0.189 a Q25 o −0.241 a Q90). G een Inno a ion
(GI) also e ains i s nega i e e ec on emissions, al hough i s in luence
weakens sligh ly as quan iles inc ease (−0.225 a Q25 o −0.117 a
Q90). These esul s align wi h he o iginal indings, con i ming ha he
ela ionships a e obus and consis en ac oss di e en quan ile le els.
These esul s a e suppo ed by ea ly s udies o (Amin e al., 2022;Hassan
e al., 2024). Fig. 6 g aphically illus a es hese esul s.
The obus ness analysis o G een Finance (GF) in Model-2 e eals
ha i s nega i e impac on CO₂emissions is consis en ac oss quan iles,
al hough he s eng h o he e ec a ies (See Table 9). A lowe quan-
iles (Q25 and Q50), GF shows a signi ican nega i e in luence (−0.0402
and −0.0768, espec i ely), indica ing ha g een inance plays a c ucial
ole in educing emissions in low o mid-emission con ex s. Howe e , a
highe quan iles (Q75 and Q90), while he e ec emains nega i e, he
signi icance weakens, especially a Q90, whe e he coe icien is no
s a is ically signi ican . This sugges s ha g een inance is mo e e ec i e
in educing emissions in lowe -emission scena ios bu may ha e
diminishing e u ns in highe -emission con ex s, possibly due o la ge -
scale s uc u al ac o s ha equi e mo e subs an ial in e en ions
beyond inancial in es men s alone. Fig. 7 illus a es hese esul s.
The obus ness analysis o G een Taxes (GT) in Model-3 shows
mixed esul s ac oss quan iles (See Table 10). A he lowe quan iles
Fig. 6. BQR Model-1.
Table 9
Boo s ap Quan ile Reg ession Analysis Model-2.
Dep-CO2 Q25 Q50 Q75 Q90
EG 0.857*** 0.856*** 0.842*** 0.797***
(0.0132) (0.0163) (0.0346) (0.0379)
EP −0.801*** −0.640*** −0.669*** −0.664***
(0.0366) (0.0565) (0.0869) (0.0880)
REN −0.178*** −0.139*** −0.114*** −0.140**
(0.00616) (0.0111) (0.0277) (0.0552)
GI −0.226*** −0.175*** −0.188*** −0.127***
(0.0118) (0.0161) (0.0194) (0.0198)
GF −0.0402*** −0.0768*** −0.104** −0.0526
(0.0136) (0.0266) (0.0409) (0.0367)
Cons an −4.188*** −4.792*** −4.422*** −3.929***
(0.223) (0.155) (0.263) (0.508)
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