Influence of countries’ institutional profile on voluntary carbon disclosures

INFLUENCE OF COUNTRIES’ INSTITUTIONAL
PROFILE ON VOLUNTARY CARBON
DISCLOSURES
An onio Jesús Ma eo Má quez
Doc o al hesis
A hesis submi ed in ul ilmen o he equi emen s o he doc o al deg ee a
he Uni e sidad de Se illa.
Delhi, India, he mos pollu ed ci y in he wo ld, acco ding o he Wo ld Heal h
O ganiza ion. (h ps://cen.acs.o g/en i onmen /pollu ion/Sea ching-solu ions-
Delhis-ai -pollu ion/97/i7)
INFLUENCE OF COUNTRIES’ INSTITUTIONAL PROFILE ON
VOLUNTARY CARBON DISCLOSURES
DOCTORAL THESIS
SEVILLA, NOVEMBER 2020
PHD CANDIDATE
D. ANTONIO JESÚS MATEO MÁRQUEZ
SUPERVISORS
DR. JOSÉ MARÍA GONZÁLEZ GONZÁLEZ
DR. CONSTANCIO ZAMORA RAMÍREZ
PROGRAM
DOCTORADO EN CIENCIAS ECONOMICAS
EMPRESARIALES Y SOCIALES (2011)
LOCATION
DEPARTAMENTO DE CONTABILIDAD Y ECONOMÍA
FINANCIERA, UNIVERSIDAD DE SEVILLA
Acknowledgemen s
Fi s , I would like o hank my supe iso s Associa e P o esso Cons ancio Zamo a
Ramí ez and Associa e P o esso José Ma ía González González o hei
guidance, help and eedback h oughou my PhD. The comple ion o his p ojec
has been e y in luenced by hei suppo i e supe ision s yle.
Second, I would like o exp ess my since e g a i ude o he Uni e sidad de Se illa
o accep ing me as PhD s uden , as well as o p o iding he acili ies ha
enabled me o comple e his hesis. Specially, I would like o hank his ins i u ion
o unding my PhD h ough he VI Plan P opio de In es igación y T ans e encia
de la Uni e sidad de Se illa. Thanks a e also gi en o he communi y o he
Facul ad de Ciencias Económicas y Emp esa iales o he Uni e sidad de Se illa o
hei suppo and help du ing my PhD s udies.
Thi d, I would also like o acknowledge o The Uni e si y o Edinbu gh Business
School and he Uni e si y o O ago o p o iding me he oppo uni y o doing a
esea ch s ay. The quali y o he esea ch me hods used in his hesis imp o ed a
lo om hese esea ch s ays.
Fou h, I would like o exp ess my since e app ecia ion o Ma i Alcaide Cas iñei a,
Financial Risk Managemen and T easu y a Ibe ia and P o esso a IEB, o he
help in managing Bloombe g New Ene gy Finance.
Las bu no leas , I would like o hank my pa en s o hei p aye s and suppo
du ing my PhD jou ney. I would also like o hank o my g andmo he , Juana, o
p o iding me heal hy ood while I was ou o my home own which has
con ibu ed o keep me heal hy and ocused on he p ojec . In addi ion, I would
like o hank my b o he , Manuel Da id, and my uncles, Juan José and Bea iz, o
hei cons an encou agemen . Finally, I would like o ex end my since e g a i ude
o e e yone who suppo ed me du ing my doc o al esea ch.

7
Table o Con en s
Lis o abb e ia ions ............................................................................................. 9
Lis o ables ........................................................................................................ 11
Lis o igu es ...................................................................................................... 13
Chap e 1. In oduc ion ..................................................................................... 15
1.1. Resea ch mo i a ions ................................................................................................ 17
1.2. Resea ch objec i es ................................................................................................... 20
1.3. Theo e ical amewo k .............................................................................................. 21
1.4. Me hodology ............................................................................................................... 21
1.5. Resea ch con ibu ions ............................................................................................. 22
1.6. S uc u e o he hesis ............................................................................................... 23
Chap e 2. Li e a u e e iew and de elopmen o hypo heses ..................... 25
2.1. In oduc ion .................................................................................................................. 27
2.2. Socio-poli ical heo ies o disclosu e .................................................................. 27
2.3. Economics-based heo ies o disclosu e ........................................................... 29
2.4. Ins i u ional heo y ..................................................................................................... 30
2.4.1. The h ee pilla s o ins i u ions ....................................................................... 32
2.4.2. The impo ance o he egula i e pilla ....................................................... 42
2.4.3. Componen s o he egula i e pilla ............................................................. 45
Chap e 3. Me hodology .................................................................................... 49
3.1. Sample ............................................................................................................................ 51
3.2. Sou ces ........................................................................................................................... 51
3.3. Empi ical models......................................................................................................... 53
8
3.4. Va iables ........................................................................................................................ 59
3.4.1. Dependen a iables .......................................................................................... 59
3.4.2. Independen a iables ....................................................................................... 61
3.4.3. Con ol a iables ................................................................................................. 64
Chap e 4. Resul s ............................................................................................... 67
4.1. B eakdown o clima e- ela ed ins i u ional p o ile by coun y .................. 69
4.2. Desc ip i e s a is ics .................................................................................................. 73
4.3. Co ela ion analysis .................................................................................................... 76
4.4. Reg ession esul s ...................................................................................................... 78
4.5. Robus ness checks ..................................................................................................... 88
Chap e 5. Conclusions .................................................................................... 105
5.1. Key indings and main con ibu ions o he hesis...................................... 107
5.2. Implica ions o p ac ice ....................................................................................... 111
5.3. Limi a ions and u u e esea ch di ec ions ..................................................... 112
Re e ences ........................................................................................................ 115
Appendices ....................................................................................................... 133
Appendix A – Example o o e all disclose s in I aly ................................................ 135
Appendix B – Lis o publica ions .................................................................................. 137
9
Lis o abb e ia ions
BNEF
Bloombe g New Ene gy Finance
CDP
Ca bon Disclosu e P ojec
CSR
Co po a e Social Repo ing
EPSI
En i onmen al Policy S ingency Index
ETS
Emissions T ading Schemes
EU
Eu opean Union
GDP
G oss Domes ic P oduc
GHG
G eenhouse gas
GICS
Global Indus y Classi ica ion S anda d
GLOBE
Global Leade ship O ganiza ional Beha iou E ec i eness
IPCC
In e go e nmen al Panel on Clima e Change
NGER
Na ional G eenhouse and Ene gy Repo ing
NIS
New Ins i u ional Sociology
OECD
O ganisa ion o Economic Co-ope a ion and De elopmen
OLS
O dina y Leas Squa es
ROA
Re u n on asse s
TCFD
Task Fo ce on Clima e-Rela ed Financial Disclosu es
VIF
Va iance In la ion Fac o

17
1.1. Resea ch mo i a ions
An h opogenic g eenhouse gas (GHG) emissions ha e been iden i ied as he
dominan cause o obse ed wa ming since he p e-indus ial pe iod. Global
wa ming has al eady caused unp eceden ed isks o na u al and human sys ems
such as inc eases in d ough s, hea wa es, hea y ain e c. (IPCC, 2018). Clima e
change has led o an inc ease in conce n o e companies’ le els o GHG
emissions, and hei con ibu ion o global wa ming (Hahn e al., 2015).
Social conce n ega ding clima e change and i s consequences has de eloped
in o a ele an ma e o o ganiza ions in bo h he public and p i a e sec o .
Mo e speci ically in he case o he la e , in es o s ha e inc eased hei demands
o in o ma ion conce ning impac s, isks and s a egies ela ed o clima ic
change (Luo, 2019). As a esul , o ganisa ions ind hemsel es unde p essu e
om di e en s akeholde s o epo on hei s a egies o clima e change, as
well as on he isks and oppo uni ies i en ails (F eedman & Jaggi, 2010), so ha
said s akeholde s may inco po a e his in o ma ion in o hei decision-making
p ocess (Luo e al., 2013). In 2000, a g oup o ins i u ional in es o s c ea ed he
Ca bon Disclosu e P ojec (he eina e CDP). The CDP is a olun a y ini ia i e ha
is used by se e al global companies o he disclosu e o ca bon in o ma ion
(Depoe s e al., 2016). Since i s c ea ion, he numbe o companies disclosing hei
ca bon in o ma ion h ough CDP has con inued o inc ease. In his sense, CDP
epo s ha e become an impo an pa o companies’ olun a y ca bon epo ing
(Depoe s e al., 2016; Kolk e al., 2008).
As clima e change is an inc easingly impo an social and economic issue,
unde s anding he de e minan s o ca bon disclosu e is a wo hy opic. P e ious
s udies ha e p o ided some e idence on he de e minan s o co po a e ca bon
disclosu es (P. M. Cla kson e al., 2008; Co e & Najah, 2012; Ji a & To el, 2013;
Luo, 2019; Luo e al., 2012; P ado-Lo enzo e al., 2009; Rankin e al., 2011; Reid &
To el, 2009; S anny, 2013). They ound ha a ious ac o s a ec co po a e
18
ca bon disclosu es such as i ms’ cha ac e is ics (e.g. p o i abili y, le e age, size),
disclosu e- ela ed (e.g. co po a e sus ainabili y epo s, i ms’ CDP pa icipa ion),
en i onmen - ela ed (e.g. ca bon emissions, ca bon-in ensi e indus y), as well as
coun y-le el ac o s (e.g. he s ingency o en i onmen al egula ions, common-
law coun ies, p esence o emissions ading schemes (he eina e ETS),
a i ica ion o he Kyo o P o ocol).
Volun a y ca bon disclosu e has al eady been he subjec o conside able
esea ch, bu i none heless me i s u he in es iga ion, especially as ega ds i s
link o he h ee clima e- ela ed ins i u ional pilla s ( egula i e, no ma i e and
cul u al-cogni i e). I has been a gued ha he ins i u ional con ex plays a c ucial
ole in mode a ing olun a y ca bon disclosu e (Hahn e al., 2015; Luo, 2019; Luo
e al., 2012). The majo i y o p e ious s udies ha conside he in luence o
ins i u ional ac o s on ca bon disclosu e ha e ocused on a single dimension o
ins i u ions such as egula i e (e.g. Rankin e al., 2011; Reid and To el, 2009),
cul u al (e.g. Luo and Tang, 2016), o on ins i u ions as a whole (e.g. Luo e al.,
2012). Mo eo e , hese s udies ha e used gene ic ac o s o measu e ins i u ional
p essu es, o example, Luo e al. (2012) used he a i ica ion o he Kyo o P o ocol
and he na u e o he gene al legal sys em o p oxy o he egula i e pilla o
ins i u ions. While hese measu es may include some e e ence o clima e change,
hey a e somewha gene ic in na u e. In e ms o cul u al p essu es, Luo and Tang
(2016) analysed whe he na ional cul u e in luences olun a y ca bon epo ing.
Howe e , hey employed na ional cul u e ac o s ha a e a he gene al in na u e,
such as unce ain y a oidance, powe dis ance o long- e m o ien a ion. In his
sense, i is o in e es o examine how ins i u ional p essu es a ec i ms’
disclosu e beha iou by conside ing ins i u ional ac o s ha a e di ec ly ela ed
o clima e change such as clima e- ela ed laws o le els o social conce n abou
clima e change.
19
Pe aul -C aw o d and Cla k-Williams (2010) sugges ed ha coun ies’
ins i u ional con ex s may be a key d i e o olun a y ca bon disclosu es.
Al hough hey conside ed he h ee ins i u ional pilla s, hei analyses canno be
ex apola ed o o he coun ies o indus ies since hey only conside ed banking
companies om wo coun ies (F ance and he Uni ed S a es o Ame ica). They
called o u he empi ical esea ch ha would mo e beyond ins i u ional heo y
as a whole and conside a la ge sample o coun ies and sec o s. Fu he mo e, i
is no ye clea whe he he clima e- ela ed no ma i e and cul u al pilla s o
ins i u ions impac on olun a y ca bon epo ing. Hence his hesis add esses
hese p oblems by aking he h ee ins i u ional pilla s ela ed o clima e change
in o accoun , and by building on a wide sample o companies and indus ies.
In addi ion, many coun ies exe p essu e on companies by es ablishing
egula ions ha equi e hem o measu e and educe hei GHG emissions
(Depoe s e al., 2016). The Kyo o P o ocol ep esen ed an impo an s ep o wa d
since i es ablished emissions educ ion a ge s o he majo i y o indus ialised
coun ies (UN, 2018). Indeed, he Kyo o P o ocol has been used in se e al
p e ious s udies o measu e he in luence o a coun y’s egula i e con ex on
companies’ ca bon epo ing. Howe e , no consis en esul s ha e been ob ained:
while ce ain au ho s ha e de ec ed a posi i e ela ionship be ween he wo (e.g.
F eedman & Jaggi, 2005; Ji a & To el, 2013; P ado-Lo enzo e al., 2009), o he s
ha e no been able o ind a signi ican ela ionship (e.g. B ouhle and Ha ing on,
2010; Luo e al., 2012). In ecen imes, many coun ies ha e inc eased hei
en i onmen al egula ions in o de o espond o he challenges o clima e
change, and hey ha e passed speci ic laws o he educ ion o GHG emissions
(Nachmany e al., 2015). This e olu ion owa ds g ea e speci ici y in clima e
change egula ions oge he wi h he inconsis ency o he esul s ob ained in he
p e ious li e a u e ha e also p omp ed he w i ing o his hesis. In his sense, his
esea ch aims o analyse he p essu e exe ed by a coun y’s egula i e con ex
20
on companies’ ca bon epo ing s a egies, aking in o accoun whe he hey do
o do no disclose in o ma ion as well as he quali y o he in o ma ion disclosed.
Fu he mo e, he as majo i y o p e ious s udies ha e conside ed he egula o y
con ex o ins i u ions as a whole (as a single a iable), despi e he ac ha i is
possible o di e en ia e a ious componen s o he egula o y con ex ( ules and
laws; moni o ing mechanisms and penal ies; and ewa ds), making i possible o
del e in o he in luence o he egula o y con ex on h ough he analysis o he
in luence o i s componen s, which up o now has no been done.
1.2. Resea ch objec i es
The p ima y aim o his hesis is o imp o e ou unde s anding abou he
ela ionship be ween coun ies’ ins i u ional p o ile and olun a y ca bon
disclosu e (conside ing he h ee ins i u ional pilla s: egula i e, no ma i e and
cul u al-cogni i e (Sco , 2014)) and empi ically es ing a heo e ical model o ill
in he gap in knowledge. In addi ion, gi en i s impo ance, his esea ch aims o
analyse he in luence o he componen s o he egula i e pilla o ins i u ions on
olun a y ca bon disclosu es on he pa o companies.
In pa icula , d awing on he heo e ical amewo k o New Ins i u ional Sociology
(he eina e NIS) his esea ch es ablishes he ollowing objec i es:
1. To iden i y and empi ically analyse he in luence o coun ies’ clima e-
ela ed ins i u ional con ex ( egula i e, no ma i e and cul u al-cogni i e)
on companies’ decisions o olun a ily disclose ca bon in o ma ion, as well
as on he quali y o ca bon disclosu es.
2. To in es iga e whe he he di e en componen s o he clima e change-
ela ed egula i e pilla o coun ies in luence companies’ decisions o
olun a ily disclose ca bon in o ma ion, as well as he quali y o
disclosu es.
21
1.3. Theo e ical amewo k
Gi en ha his esea ch is ocused on coun ies’ ins i u ional p o ile, NIS heo y
has been used in o de o examine he p essu e o a coun y’s clima e- ela ed
ins i u ional pilla s on companies’ esponse o demands o ca bon disclosu e.
This heo y es ablishes ha he decision o disclose o no o disclose ca bon
in o ma ion, and how o disclose i , is no necessa ily he esul o a a ional
decision-making p ocess on he pa o o ganiza ions ha ac independen ly
(La inaga-González, 2007), bu a he i may be condi ioned by p essu es o he
ins i u ional con ex o he coun y common o hem (G auel & Go ha d , 2016).
Along hese lines, Sco (2014) poin ed ou ha o ganiza ions a e deeply
imme sed in ins i u ional con ex s, which a he same ime bo h acili a e and
es ic said o ganiza ions’ beha iou . Sco (2014) iden i ied h ee ins i u ional
pilla s ha in luence he beha iou o o ganiza ions. These a e egula i e,
no ma i e and cul u al-cogni i e. This hesis will measu e each o hese pilla s and
es whe he hey in luence companies’ decisions o olun a ily disclose ca bon
in o ma ion, as well as he quali y o he in o ma ion epo ed.
Al hough NIS heo y is he main heo e ical app oach o his esea ch, in Chap e
2 o he ele an heo ies ha ha e been used o explain olun a y ca bon
disclosu e a e e ised such as legi imacy heo y, s akeholde heo y, economic-
based heo ies, e c. (Hahn e al., 2015).
1.4. Me hodology
Bo h he incidence in he decision o disclose and he quali y o he in o ma ion
disclosed, a e analysed by using logi , Tobi and he Heckman wo-s age models.
These models a e buil on con ol a iables which ha e been widely used in
p e ious s udies based on di e en heo ies ha jus i y hei ela ionship wi h
dependen a iables (decision and quali y o disclosu e). The e o e, di e en
heo e ical models a e p oposed in o de o add ess he objec i es o his hesis.

22
1.5. Resea ch con ibu ions
This esea ch p o ides he i s comp ehensi e assessmen o he ela ionship
be ween coun ies’ clima e- ela ed ins i u ional pilla s and olun a y ca bon
disclosu e, aking in o accoun bo h he esponse decision as well as he quali y
o disclosu es. Hence, his hesis con ibu es o he exis ing li e a u e in se e al
ways.
Fi s , i links coun ies’ ins i u ional con ex s o he decision o i ms ha ope a e
in said coun ies o olun a ily disclose ca bon in o ma ion (G auel & Go ha d ,
2016). Second, i uses speci ic clima e- ela ed measu emen s o he egula i e,
no ma i e and cul u al-cogni i e dimensions o coun ies’ ins i u ional con ex
(Kos o a, 1997; Sco , 2014), mo e speci ically, i is he i s o conside he h ee
ins i u ional pilla s ela ed o clima e change in he s udy o olun a y ca bon
disclosu es. The e o e, he no el y o his s udy is ha i conside s speci ic
clima e- ela ed a iables o measu e he di e en ins i u ional pilla s. Thi d, i
conside s he h ee ins i u ional pilla s ela ed o clima e change in he same
eg ession model and p o ides empi ical e idence ha companies’ decisions o
olun a ily disclose ca bon in o ma ion and he quali y o he in o ma ion
disclosed should be examined sepa a ely because i is possible ha hey a e
in luenced by di e en ac o s. Fou , ega ding he egula i e pilla o ins i u ions,
unlike p e ious s udies which conside gene ic en i onmen al egula i e
componen s (Luo e al., 2012; Rankin e al., 2011; Reid & To el, 2009), his hesis
iden i ies and measu es he di e en componen s o coun ies’ clima e- ela ed
egula i e con ex s. Speci ically, i examines he p essu e exe ed by he di e en
egula i e componen s ( ules; moni o ing mechanisms and punishmen ; ewa ds)
on olun a y ca bon disclosu e on he pa o companies, again in con as o
p e ious s udies which ei he ocus on coun ies’ egula i e pilla s as a whole
(F eedman & Jaggi, 2005; Luo e al., 2012; Rankin e al., 2011; Reid & To el, 2009),
o cen e on one speci ic componen , e.g. clima e- ela ed ules (Ma eo-Má quez
23
e al., 2020). Fi e, i demons a es which coun ies p esen highe le els o
p essu e om said egula i e dimensions. Thus coun ies wi h highe le els o
p essu e may e lec a g ea e commi men o he igh agains clima e change.
Finally, his s udy ook in o conside a ion all he companies ha appea in he
2015 CDP epo s by coun y/ egion, hus a oiding he bias ound in many
p e ious s udies which only conside la ge -scale companies o hose lis ed in he
main indices o speci ic coun ies (e.g. F eedman and Jaggi, 2005; Luo e al., 2012;
P ado-Lo enzo e al., 2009; Tang and Luo, 2011).
1.6. S uc u e o he hesis
The emainde o his hesis is o ganised as ollows. Chap e 2 p esen s he
concep ual amewo k and he de elopmen o hypo heses. This chap e also
includes a comp ehensi e e iew o he di e en heo ies ha ha e been used in
he esea ch ield o olun a y ca bon disclosu es. Chap e 3 desc ibes he
esea ch design including sample selec ion, he heo e ical models and he
a iables in oduced in he s udy. Chap e 4 includes he desc ip i e analysis, he
co ela ion analysis, he empi ical esul s, and he obus ness analysis. This
chap e also p esen s a comp ehensi e analysis o he dis ibu ion o coun ies’
clima e- ela ed ins i u ional p o ile. Chap e 5 p esen s he main conclusions o
his s udy, as well as implica ions o u u e esea ch.
Chap e 2. Li e a u e e iew and de elopmen o hypo heses
In oduc ion, socio-poli ical heo ies o
disclosu e, economic-based heo ies o
disclosu e, ins i u ional heo y
32
olun a y ca bon epo ing ia he CDP is a ehicle o companies (bo h subjec
and no subjec o manda o y ca bon epo ing) o adap o he social
expec a ions o hei en i onmen . These expec a ions encompass wha socie y
expec s om companies. Thus, companies end o inco po a e hese expec a ions
in o hei ope a ions, and u he mo e, o e ime, expec a ions end o become
mo al obliga ions (Jä enpää, 2009). Companies’ adap a ion o social
expec a ions allows hem o ob ain legi imacy om bo h egula i e bodies
(legally au ho ised bodies ha ha e au ho i y o e o ganiza ions), and public
opinion (which has he ole o es ablishing he no ms o social accep abili y)
(Deephouse, 1996). Mo eo e , in he case o he CDP, his ini ia i e was launched
by ins i u ional in es o s, who hemsel es a e ac o s ha can p o ide inancial
esou ces, and abo e all, hey occupy a posi ion ha allows companies o con e
legi imacy (Deephouse, 1996). Wi hin he ield o olun a y ca bon disclosu es,
ins i u ional heo y has been widely used o explain he eason why companies
disclose ca bon and en i onmen al in o ma ion (Ji a & To el, 2013; Kolk e al.,
2008; Luo e al., 2012; Tang & Luo, 2016). The majo i y o s udies ega ding he
de e minan s o en i onmen al disclosu es conside ins i u ional p essu es a
coun y-le el (Ji a & To el, 2013; Luo, 2019; Tang & Luo, 2016).
Zucke (1987) iden i ied ins i u ions as he speci ic p ac ices, knowledge, ideas
and cogni i e amewo ks ha ha e been pe manen ly adop ed by an
o ganiza ion. I is possible o iden i y mul iple le els o ins i u ions, anging om
in e na ional con ex s (e.g. poli ical sys ems) o local sys ems (e.g. p o essional
associa ions) (Sco , 2014).
2.4.1. The h ee pilla s o ins i u ions
In addi ion o being economically e icien , o ganiza ions need social powe and
ins i u ional legi imacy in o de o su i e wi hin a ce ain con ex (DiMaggio &
Powell, 1983; Meye & Rowan, 1977). In his sense, DiMaggio and Powell (1983)
highligh ed he impo ance o he concep o ins i u ional isomo phism o

33
unde s anding he p ac ices ha pe ade ce ain con ex s. They s a ed ha
ins i u ional isomo phism occu s ia h ee mechanisms: coe ci e, no ma i e and
mime ic. Coe ci e isomo phism esul s om poli ical in luence and he p oblem
o legi imacy (DiMaggio & Powell, 1983). In ce ain cases, o ganiza ional change
is d i en by a go e nmen manda e, o example, in he ield o ca bon
disclosu es, companies wi hin ce ain speci ic sec o s a e equi ed o disclose hei
ca bon emissions as consequence o he implemen a ion o an ETS. The e o e,
he exis ence o a legal amewo k does in luence an o ganiza ion's beha iou .
These au ho s linked no ma i e p essu es o p o essionalisa ion and educa ion.
In his sense, p o essionalisa ion e e s o he con inuous s uggle by he pa ne s
o an associa ion o de ine how hey should ca y ou hei wo k. Con e sely, he
s anda d o mal educa ion p o ided by educa ional ins i u ions in luences he
decision-making p ocesses o manage s in he majo i y o companies in he
indus ialised wo ld (Ma en & Moon, 2008). Mime ic p ocesses o igina e om
unce ain y, since unce ain y is a o ce ha p omo es imi a ion among
o ganiza ions. The e o e, in he ace o unce ain y, o ganiza ions end o imi a e
he s a egies o success ul o ganiza ions in hei ins i u ional con ex s, hus
becoming isomo phic in hei managemen s uc u es and p ac ices (DiMaggio &
Powell, 1983).
Isomo phic p essu es as iden i ied by DiMaggio and Powell (1983) a e ela ed o
he ins i u ional pilla s subsequen ly de ined by Sco (2014). Sco designed an
analy ic amewo k o NIS heo y and ound ha o ganiza ional beha iou is
in luenced by h ee ins i u ional pilla s: egula i e, no ma i e and cul u al-
cogni i e. These ins i u ions p o ide a amewo k wi hin which o ganiza ions
mus ope a e since hey a e unde p essu e om ules, no ms and cul u al belie s
ha ha e been accep ed and adop ed in a speci ic en i onmen .
The egula i e pilla is ela ed o coe ci e p essu es. This ins i u ional pilla
encompasses ules and laws as along wi h en o cemen mechanisms sanc ioned
34
by egula i e bodies, and which a e used by o ganiza ions in selec ing and
in e p e ing in o ma ion (DiMaggio & Powell, 1983). The e o e, go e nmen s play
a undamen al ole gi en hei capaci y o sanc ion ules. Qian and Bu i (2008)
no e ha he egula i e dimension o ins i u ions c ea es he s onges incen i e
o companies o de elop en i onmen al ac ions, as well as imposing p essu es
upon hem o do so.
I is possible o iden i y coun ies’ egula i e con ex as he laws and no ms ha
hey ha e es ablished in ela ion o clima e change. Thus, he ole o go e nmen s
is undamen al gi en hei capaci y o es ablish laws and egula ions, he eby
incen i izing companies o educe hei GHG (S odda e al., 2012). Townshend
e al. (2013) poin ed ou ha na ional clima e change- ela ed egula ion is o i al
impo ance o implemen ing in e na ional ag eemen s, as well as o inc easing
con idence o u u e in e na ional commi men s gi en ha expe ience a he
na ional le el may inc ease he likelihood o a aining in e na ional pledges.
Many go e nmen s use a ca bon p icing ins umen o in e nalize he ex e nal
cos s o ca bon emissions, as well as o educe GHG emissions o he a mosphe e
(Me cal & Weisbach, 2009). Two main mechanisms can be used o se a p ice on
ca bon emissions: ca bon ax and he GHG emissions ading scheme (he eina e
ETS). Wi h ega d o ca bon ax ins umen s, go e nmen s place an explici p ice
on ca bon emissions by es ablishing a ax a e, i.e. a p ice pe onne o CO2
emi ed, as well as by speci ying hose companies o indus ies subjec o said ax
(Hai es, 2018). In his sense, a ge ed subjec s can choose be ween educing hei
emissions o paying o hem. The e o e, he GHG emission educ ion depends
on he decision aken by he a ge ed subjec s. An ETS ins umen se s a limi on
ca bon emissions by selec ed subjec s, and issues allowances in quan i ies
app oxima ely equal o he limi . Emission igh s a e adable, and hei p ice is
de e mined by supply and demand (Che allie , 2013). ETS di e s om ca bon ax
35
in ha he ca bon p ice o emissions is no p ede ined whe eas he GHG emission
educ ion ou come is (Wo ld Bank, 2018).
Apa om implemen ing ca bon p icing ins umen s, go e nmen s can equi e
manda o y epo ing o companies’ GHG emissions. Fo ins ance, he Aus alian
go e nmen p omulga ed he Na ional G eenhouse and Ene gy Repo ing
(he eina e NGER) Ac in 2008, which equi es he epo ing o GHG emissions
on he pa o speci ic o ganiza ions. This was a challenge o many companies,
since hey had o be manda o ily accoun able o hei GHG emissions. Thus, he
p omulga ion o he NGER Ac led o he eme gence o many accoun ing
implica ions o bo h o ganiza ions and he go e nmen . In his sense, he NGER
Ac acili a es ca bon epo ing and GHG emissions assu ance; i p o ides
engagemen wi h s akeholde s; i o e s a ool wi h which o manage isks a ising
om clima e change; i acili a es he implemen a ion o ca bon managemen
accoun ing; i makes a ailable da a abou GHG emissions; and is ex emely use ul
o de eloping a ca bon p icing mechanism (Lodhia, 2011). Fou yea s la e ,
Aus alia implemen ed a sys em which pu a p ice on ca bon emissions, bu i was
abolished in 2015 (Jo zo & Mazouz, 2015). In his s udy’s sample, all he coun ies
possess a clima e change egula i e amewo k. Howe e , i is di icul o measu e
he le el o se iousness o a gi en coun y based solely on he numbe o laws
ela ed o clima e change disclosu es, since while some ules a e b oad and
in eg a i e, o he s a e e y na ow in scope (Townshend e al., 2013).
Companies olun a ily disclose ca bon in o ma ion in o de o be be e
posi ioned o u u e changes in egula ion (Luo e al., 2012; Solomon & Lewis,
2002). Se e al p e ious s udies use he signing o he Kyo o P o ocol as a p oxy
o egula i e p essu es. Al hough some au ho s epo ed no signi ican
ela ionship be ween ca bon disclosu es and companies headqua e ed in a
signa o y coun y o he Kyo o P o ocol (Luo e al., 2012; Tang & Luo, 2016), he
majo i y o hese s udies did ind a posi i e and signi ican associa ion be ween
36
hese a iables (F eedman & Jaggi, 2005; P ado-Lo enzo e al., 2009). Simila ly, in
hei s udy o olun a y en i onmen al disclosu es and he supply chain, Ji a and
To el (2013) ound a posi i e and signi ican ela ionship be ween companies’
disclosu es and hei belonging o Kyo o P o ocol coun ies.
Mo eo e , some s udies wen u he s ill and conside ed o he GHG- ela ed
egula ion such as egula ions o speci ic pollu ing sec o s o hose ela ed o
ETS (Kim & Lyon, 2011; Luo e al., 2013; Rankin e al., 2011; Reid & To el, 2009;
Schol ens & Kleinsmann, 2011). Luo e al. (2013) analysed olun a y ca bon
disclosu es in bo h de eloping and de eloped coun ies. They demons a ed ha
hese disclosu es a e posi i ely associa ed wi h companies’ belonging o a
coun y ha has an es ablished ETS. This esul is simila o ha epo ed by Kim
and Lyon (2011) and Reid and To el (2009), who ound ha egula i e h ea s did
ha e a posi i e in luence on companies’ ac ions wi h ega d o olun a y
disclosu e o ca bon emissions. Con e sely, Rankin e al. (2011) did no ind
e idence ha companies lis ed in he Eu opean Union ETS a e mo e likely o
pa icipa e in olun a y GHG disclosu e p ac ices. Schol ens and Kleinsmann
(2011) ound mixed e idence ega ding egula i e de e minan s based on GHG-
speci ic egula ion. Al hough he indings o p e ious li e a u e a e ambiguous, i
is possible o iden i y he p edominance o a posi i e ela ionship be ween he
egula i e con ex and olun a y ca bon disclosu es.
Al hough esponse o he CDP ques ionnai e is on a wholly olun a y basis, i may
be expec ed ha companies which belong o coun ies wi h es ablished speci ic
clima e change egula ions will adap and make in es men s o con ol and
educe hei GHG emissions, wi h he aim o a oiding possible sanc ions o loss
o legi imacy (Cho & Pa en, 2007). In addi ion, clima e change- ela ed egula ion,
apa om imposing manda o y ules on a ge companies, also con ibu es o
he e being g ea e isibili y o he clima e change challenge in socie y. This leads
o he gene a ion o social expec a ions ha may in luence he beha iou o bo h
37
a ge and non- a ge companies. Regula ion may also es ablish a se o
equi emen s ha he in o ma ion disclosed mus comply wi h, which in u n
se es as a gua an ee o he quali y o said in o ma ion. This would hen sugges
ha companies headqua e ed in coun ies wi h mo e s ingen le els o speci ic
clima e egula ion will be mo e likely o olun a ily disclose ca bon in o ma ion
compa ed o companies based in coun ies wi h lowe le els o clima e change
egula ion. Apa om in luencing company pa icipa ion, coun ies wi h high
le els o clima e change egula ion may also exe p essu e on he quali y o he
in o ma ion epo ed. The e o e, he i s hypo heses in his hesis may be
es ablished as ollows:
H1a: Coun ies’ clima e- ela ed egula i e con ex s posi i ely in luence
companies’ decisions o olun a ily disclose ca bon in o ma ion.
H1b: Coun ies’ clima e- ela ed egula i e con ex s posi i ely in luence he
quali y o olun a y ca bon disclosu es.
The no ma i e pilla o ins i u ions e e s o he social amewo k based on alues
–de ined as concep ions o he p e e ed o he desi able – along wi h no ms ha
speci y he way in which ac ions should be unde aken in o de o achie e
o ganiza ional objec i es (Sco , 2014). In his sense, i may be iden i ied wi h “ he
mo ally co ec hing o do” (Jones, 1999, p. 165). Thus, while he egula i e pilla ’s
basis o legi imacy is “legally sanc ioned” and i s basis o compliance is he
“expediency” o a oiding sanc ions, in he case o he no ma i e pilla , he basis
o legi imacy is “mo ally go e ned” while he basis o compliance is “social
obliga ion”. Fu he mo e, Sco (2014) conside s ha he egula i e pilla exhibi s
high alues as ega ds he dimensions o obliga ion, p ecision and delega ion,
while alues o hese same dimensions in he case o he no ma i e pilla a e
lowe .

38
No ma i e p essu es a e ela ed o he no ma i e isomo phism iden i ied by
DiMaggio and Powell (1983). The e o e, companies may unde s and ha he
mo ally co ec hing o do is o disclose in o ma ion abou he impac o hei
ac i i ies on clima e change, wi h he CDP being he ehicle selec ed o his
pu pose. In his ega d, he CDP se es as a sel - egula i e amewo k o
companies’ no ma i e beha iou since he e is no s anda dized global ca bon
epo . In his way he CDP has c ea ed a common amewo k o ules ha
companies mus adhe e o i hey wish o pa icipa e in he CDP ques ionnai e
(Baldwin e al., 2012). Mo e speci ically, he no ma i e expec a ions p esen ed by
he CDP es ablish how o ganiza ions a e supposed o beha e ega ding clima e
change, and how hey should epo i . These expec a ions a e also held by ce ain
o he no able ac o s, such as in es o s who suppo he CDP, and he e o e a e
expe ienced by o ganiza ions as an ex e nal p essu e.
In he ield o olun a y ca bon disclosu e, esea ch o da e has no ye
de e mined he e ec s o clima e- ela ed no ma i e p essu es on companies’
olun a y ca bon disclosu es. Pe aul -C aw o d and Cla k-Williams (2010)
conduc ed a desc ip i e analysis which conside ed no ma i e p essu es measu ed
as he pa icipa ion o coun ies’ o ganiza ions in he CDP along wi h co po a e
social epo ing ac i i ies. Howe e , hey did no p esen an econome ic
associa ion be ween hese a iables. S anny (2013) examined olun a y ca bon
disclosu es o he Uni ed S a es S&P 500 companies in he CDP ques ionnai e,
and concluded ha olun a y disclosu es o he CDP ha e become ` ou ine´ o
hese i ms, and a e ca ied ou on an annual basis. In addi ion, S anny (2013)
ound ha he mos ele an ac o in luencing companies' u u e disclosu es is
hei p e ious disclosu es. Thus i would seem ha pa icipa ing in he CDP
ques ionnai e has become ` he no m´ o la ge lis ed companies. Mo eo e ,
companies’ engagemen in olun a y ca bon disclosu e seems o ollow a a he
consis en pa e n e e y yea . I is necessa y o highligh he ac ha he
39
in o ma ion disclosed by companies o he CDP may a y be ween coun y-
speci ic con ex s (Pe aul -C aw o d & Cla k-Williams, 2010). In his sense, he
coun y-speci ic no ma i e con ex may a ec companies’ decisions o olun a ily
disclose ca bon da a, as well as gene a ing highe -quali y epo ing.
Consequen ly, his discussion leads o he ollowing hypo heses:
H2a: Coun ies’ clima e- ela ed no ma i e con ex s posi i ely in luence
companies’ decisions o olun a ily disclose ca bon in o ma ion.
H2b: Coun ies’ clima e- ela ed no ma i e con ex s posi i ely in luence he
quali y o olun a y ca bon disclosu es.
The cul u al-cogni i e dimension o ins i u ions is he main dis inguishing ea u e
o he NIS pe spec i e (Ho man, 1999; Phillips & Malho a, 2008; Sco , 2014).
This pilla e e s o he socially sha ed concep ions and he common belie s ha
c ea e amewo ks h ough which o ganiza ions in e p e hei en i onmen and
ake ac ion. Acco ding o Sco (2014), he basis o compliance o his pilla is he
sha ed unde s anding ha is aken o g an ed in a gi en con ex , while i s basis
o legi imacy is “cul u ally suppo ed”.
The cul u al-cogni i e pilla s esses ha he in e nal in e p e i e p ocesses upon
which indi iduals and o ganiza ions ely o hei decision-making – such as
whe he o no o disclose ca bon in o ma ion, o example – a e con igu ed and
in luenced by ex e nal cul u al amewo ks. Thus in his way, he belie sys ems
and cul u al amewo ks which exis in coun ies pu p essu e on indi idual ac o s
and o ganiza ions.
Ho man (1999) highligh ed ha he cogni i e aspec s o ins i u ions a e he mos
en enched because hey o m aken- o -g an ed belie s and a e esis an o
change. Acco ding o Ho man (1999, p. 364), “un o una ely, he p esence o
cogni i e ins i u ions is ex emely di icul o measu e”, as has also been
highligh ed by o he au ho s (e.g. La inaga-González, 2007). Howe e , in his
40
s udy ega ding he e olu ion o en i onmen alism in he U.S. chemical indus y,
Ho man (1999, p. 364) iden i ied he cul u al-cogni i e pilla o ins i u ions wi h
“a new mindse ” in which he chemical indus y was conside ed o be pa o he
solu ion o en i onmen al p oblems, and no as a p oblem o he en i onmen ,
as was he case in p e ious pe iods. In his sense, i can be a gued ha a new
mindse is eme ging in ela ion o clima e change. While clima e change was
ha dly conside ed o be a p oblem by socie y du ing he 1980s, social conce n
and awa eness has been on he inc ease since he 2000s, hus ecognizing he
p oblem and i s an h opogenic na u e, along wi h i s se ious epe cussions o
u u e gene a ions and he need o o ganiza ions o ake mi iga ing ac ion, e c.
Social awa eness has ad anced om igno ance o he sha ed belie ha clima e
change is a p oblem ha equi es he in e en ion o o ganiza ions a he global
le el. This in u n is pu ing p essu e on o ganiza ions, who ha e hus s a ed o
p o ide ca bon epo ing as a means o esponding o said p essu e. The e o e,
jus as i is aken o g an ed ha o ganiza ions mus epo on hei ac i i ies and
hei economic and inancial si ua ion h ough hei annual accoun s, wi h no
ques ions aised as o hei necessi y, he same may occu wi h he p o ision o
in o ma ion ela ed o he impac o o ganiza ions’ clima e change ac i i ies, and
i may also become a aken- o -g an ed p ac ice in he u u e.
Wi h ega d o he ela ionship be ween coun ies’ clima e- ela ed cul u al
p essu es and olun a y ca bon disclosu es, li le esea ch has been ca ied ou .
In ac , ce ain au ho s ha e ocused a he mo e on desc ip i e analyses and ha e
no es ablished a signi ican ela ionship wi h hese a iables (Pe aul -C aw o d
& Cla k-Williams, 2010). Con e sely, o he au ho s ha e examined olun a y
ca bon disclosu es and na ional cul u al alues using an econome ic analysis
(Luo & Tang, 2016). Howe e , hey app oxima ed na ional cul u al alues by
cul u e indices ha a e a he gene alis in na u e (e.g. he Ho s ede measu e
(Ho s ede e al., 2010) o he Global Leade ship O ganiza ional Beha iou
41
E ec i eness (GLOBE) measu e (House e al., 2004)), and he e o e did no ake
in o conside a ion speci ic na ional cul u al alues ega ding clima e change. In
his sense, p e ious s udies ha e no examined he in luence o speci ic coun ies’
clima e change awa eness on olun a y ca bon disclosu es. To ill his gap in
esea ch, his s udy conside s coun ies’ cul u al p essu es ela ed o clima e
change in he s udy o olun a y ca bon disclosu es, by conside ing coun ies’
mindse s conce ning clima e change, which is hen e lec ed in clima e change-
ela ed social awa eness and conce ns in each o he di e en coun ies.
I would appea ha companies headqua e ed in coun ies wi h high le els o
clima e change awa eness will be mo e likely o disclose ca bon- ela ed
in o ma ion, gi en ha such a e he pa e ns ollowed in hese coun ies.
The e o e, he clima e- ela ed cul u al con ex o coun ies may in luence he
decisions o companies in said coun ies o olun a ily disclose ca bon
in o ma ion, as well as he quali y o he in o ma ion epo ed. Consequen ly, he
ollowing hypo heses may be es ablished:
H3a: Coun ies’ clima e- ela ed cul u al con ex s posi i ely in luence
companies’ decisions o olun a ily disclose ca bon in o ma ion.
H3b: Coun ies’ clima e- ela ed cul u al con ex s posi i ely in luence he
quali y o olun a y ca bon disclosu es.
In his s udy, coun ies’ ins i u ional p o iles will be used o de ine clima e- ela ed
p essu es om he na ional con ex s. This idea is consis en wi h he social
embedded pe spec i e which explains how indi iduals and o ganiza ions a e
a ec ed by he social en i onmen in which hey ope a e (Kos o a, 1997). Thus
his s udy measu es coun ies’ ins i u ional p o iles ocusing on he speci ic heme
o clima e change issues and conside ing he h ee ins i u ional pilla s: egula i e,
no ma i e and cul u al-cogni i e (Sco , 2014). These ins i u ional ac o s will be
in oduced in he same eg ession se ing o examine hei in luence on

Chap e 3. Me hodology
Sample, sou ces, empi ical
models, and a iables
51
3.1. Sample
The sample was ini ially composed o 3,106 i ms lis ed in he 2015 CDP clima e
epo s om hose coun ies wi h da a a ailable ega ding hei clima e- ela ed
ins i u ional con ex . The coun ies conside ed a e Aus alia, Canada, F ance,
Ge many, India, Indonesia, I aly, Japan, Sou h A ica, Sou h Ko ea, Tu key, he
Uni ed Kingdom, and he Uni ed S a es o Ame ica. This s udy conside s a single
yea o da a (2015) due o he a ailabili y o da a ega ding coun ies’ ins i u ional
p o ile ela ed o clima e change. In line wi h Luo e al. (2012), companies in he
inancial sec o (608) we e subsequen ly iden i ied and elimina ed. Companies
which we e duplica ed in he CDP clima e epo s (8), SA companies (due o hei
being a subsidia y o ha ing unde gone a me ge du ing he 2015 CDP
ques ionnai e submission p ocess (30) and companies wi h missing inancial da a
(133) we e also elimina ed om he sample (Luo e al., 2012). The inal sample is
hus composed o 2,327 companies om 13 coun ies, ope a ing in he ollowing
sec o s, acco ding o he Global Indus y Classi ica ion S anda d (GICS): Consume
Disc e iona y; Consume S aples; Ene gy; Heal h Ca e; Indus ials; In o ma ion
Technology; Ma e ials; Telecommunica ion Se ices; U ili ies.
3.2. Sou ces
Companies’ esponse s a us and he CDP disclosu e sco e we e collec ed by hand
om he 2015 CDP clima e epo o each sample coun y, which may be ound
on he CDP websi e. Gi en ha he 2015 CDP clima e epo Hong Kong and
Sou h Eas Asia edi ion only con ained i ms ha esponded and published hei
esponse, da a o Indonesian companies ha ei he declined o espond o did
no espond, as well as hose ha did no publish hei esponse, was ga he ed
om he CDP web da abase. CDP da a has been used in se e al p e ious s udies
conce ning olun a y ca bon disclosu es (e.g. Ben-Ama and McIlkenny, 2014;
Kolk e al., 2008; Lemma e al., 2019; Luo, 2019).
52
Da a ega ding coun ies’ ins i u ional con ex was ob ained om di e en
sou ces. Fi s ly, egula i e p essu es ela ed o clima e change we e measu ed
using he En i onmen al Policy S ingency Index (he eina e EPSI) p o ided by
he OECD (O ganisa ion o Economic Co-ope a ion and De elopmen ). This
index measu es he egula i e s ingency o e e y coun y’s en i onmen al-
ela ed policies (OECD, 2019). I is a ailable on he OECD’s websi e. Secondly,
no ma i e p essu es we e es ima ed by using he in o ma ion included in he
2014 CDP clima e epo s o each sample coun y. Finally, da a conce ning
coun ies’ cul u al con ex was ob ained by hand om he Pew Resea ch Cen e 's
2015 Global A i udes Su ey. In pa icula , his s udy akes in o accoun a speci ic
clima e change conce n index ha was published in his epo , based on a global
su ey ega ding le els o public conce n abou clima e change ca ied ou in
each coun y (S okes e al., 2015).
Rega ding he componen s o coun ies’ egula i e pilla , da a on clima e- ela ed
ules was ob ained om he s udy by Nachmany e al., (2015) which p o ides a
e iew o clima e change legisla ion a ound he wo ld. Da a ega ding clima e-
ela ed en o cemen mechanisms and punishmen s was collec ed om he OECD
da abase. Mo e speci ically, his s udy used he a o emen ioned EPSI index o
measu e he s ingency o each coun y’s en i onmen al- ela ed policies because
i conside s bo h clima e- ela ed moni o ing sys ems and mechanisms ha place
a p ice on con amina ion. In o de o measu e he ewa ds componen , his
in es iga ion used coun ies’ clean ene gy in es men s which we e ob ained om
he Bloombe g New Ene gy Finance (BNEF) da abase.
Financial da a equi ed o calcula e he con ol a iables was collec ed om
Da as eam da abase. Since he CDP eques s companies o p o ide emissions
and accoun ing da a o he p eceding yea (Luo e al., 2012; S anny, 2013),
inancial da a was e ie ed o he p e ious iscal yea .
53
3.3. Empi ical models
The heo e ical amewo k p esen ed in Chap e 2 shows ha companies’
decisions o olun a ily disclose ca bon in o ma ion, as well as he quali y o
disclosu es, is a unc ion o a se o p essu es (social, inancial ma ke and
ins i u ional). Figu e 1 below p esen s he ela ionship be ween he h ee
ins i u ional pilla s and olun a y ca bon disclosu es. Acco ding o NIS heo y,
companies’ decisions o disclose ca bon in o ma ion, as well as he quali y o
disclosu es may be a ec ed by di e en ins i u ional p essu es. Sco (2014)
b eakdowns hese p essu es on egula i e, no ma i e and cul u al-cogni i e.
Figu e 1. Rela ionship be ween he h ee ins i u ional pilla s and olun a y ca bon
disclosu es
Based on hese p essu es, a wo-s ep esea ch app oach is pe o med in o de o
examine he in luence o coun ies’ clima e- ela ed ins i u ional con ex
( egula i e, no ma i e and cul u al-cogni i e) on companies’ decisions o
olun a ily disclose ca bon in o ma ion, as well as on he quali y o ca bon
Con ol a iables:
•Size
•Risk
•TobinQ
•ROA
•Le
•DCDP -1
•
Sec o s
Regula i e
No ma i e
Cul u al-cogni i e
Volun a y ca bon
disclosu es
H1a H1b
H2a H2b
H3a H3b
Ins i u ional p essu es

54
disclosu es ( he i s objec i e o his hesis) (Bou en e al., 2012; Rankin e al.,
2011). The ini ial econome ic model conside s he decision o companies o
olun a ily disclose ca bon in o ma ion h ough he CDP clima e su ey, hence a
bina y-choice P obi model is used (1). This model is es ed o he whole sample
o 2,327 companies. In Model 1, he dependen a iable (DCDP) is a dicho omous
a iable o CDP pa icipa ion which is equal o 1 i he company olun a ily
esponded o he 2015 CDP ques ionnai e and made he esponse public, and 0
o he wise. Bo h esponding and publica ion decisions a e conside ed in he same
model since he majo i y o sample i ms ha esponded o he 2015 CDP clima e
su ey made hei esponse public. Model 1, which comp ises a bina y measu e
o he p obabili y o pa icipa ion, is as ollows:
𝐷𝐶𝐷𝑃 = 𝛽0+ 𝛽1𝐸𝑃𝑆𝐼 + 𝛽2𝑁𝑜𝑟𝑚 + 𝛽3𝐶𝑢𝑙𝑡𝑢𝑟𝑎𝑙 + 𝛽4𝑆𝑖𝑧𝑒 +
𝛽5𝑅𝑖𝑠𝑘 + 𝛽6𝑇𝑜𝑏𝑖𝑛𝑄 + 𝛽7𝑅𝑂𝐴 + 𝛽8𝐿𝑒𝑣 + 𝛽9𝐷𝐶𝐷𝑃𝑡−1 + 𝛽10−17𝑆𝑒𝑐𝑡𝑜𝑟 + 𝜀 (1)
The second s age explo es he ela ionship be ween clima e- ela ed ins i u ional
pilla s and he quali y o olun a y ca bon disclosu es (as measu ed by he 2015
CDP disclosu e sco e). Hence, his model comp ises a mo e complex measu e o
he dependen a iable ha cap u es he quali y o he in o ma ion epo ed o
he CDP clima e su ey by hose companies which olun a ily disclose hei
ca bon da a. The CDP disclosu e sco e has been used in se e al p e ious s udies
o measu e he quali y o ca bon in o ma ion (Ben-Ama & McIlkenny, 2014;
Lemma e al., 2019; Luo, 2019; Ma eo-Má quez e al., 2020). I e lec s he quali y
and comp ehensi eness o ca bon in o ma ion epo ed by companies h ough
he CDP clima e su ey (Ben-Ama & McIlkenny, 2014; Lemma e al., 2019).
The majo i y o companies in he sample ha eplied o he 2015 CDP clima e
ques ionnai e ecei ed a high CDP disclosu e sco e. As shown in Table 1, mo e
han 72 pe cen o he esponding i ms (852 ou o 1,170 i ms) ob ained a CDP
sco e equal o g ea e han 85 poin s in he 2015 CDP clima e p og am. The e o e,
55
i would appea ha hose companies ha decided o espond o he CDP su ey
also decided o disclose high-quali y ca bon in o ma ion. In his case, he 2015
CDP disclosu e sco e is skewed o he igh and does no illus a e a posi i e esul
o a no mal dis ibu ion. Thus, ins ead o using an O dina y Leas Squa es (OLS)
eg ession, a P obi model is pe o med whe e he dependen a iable is equal o
1 i he company ob ained a CDP disclosu e sco e g ea e han 93.5 ( he median
sco e o esponding i ms), and 0 o he wise (Tang & Luo, 2011).
Table 1. Dis ibu ion o he 2015 CDP sco e o companies in he sample
Range
N
Pe cen (%)
Mean
Min.
Median
Max.
0 < = 2015 CDP sco e < 30
31
2.65
16.48
2.00
11.00
28.00
30 < = 2015 CDP sco e < 50
50
4.27
39.26
30.00
38.00
49.00
50 < = 2015 CDP sco e < 70
87
7.44
61.06
50.00
61.00
69.00
70 < = 2015 CDP sco e < 85
150
12.82
77.66
70.00
78.00
84.00
2015 CDP sco e > = 85
852
72.82
95.26
85.00
96.00
100.00
Sample To al
1,170
100.00
85.98
2.00
93.50
100.00
The second model is based on a subsample o a o al o 1,170 i ms om ac oss
he sample coun ies ha esponded o and published he 2015 CDP clima e
epo . I only i ms which decided o pa icipa e in he 2015 CDP su ey a e
conside ed, hen sample selec ion bias may be in oduced in o he p oposed
model as a esul o sel -selec ion bias (B een, 1996). In line wi h Heckman (1979),
in o de o co ec o sample selec ion bias, his s udy calcula es and includes he
Heckman co ec ion ac o (Lambda) in Model 2. The e o e, Model 2 is as ollows:
𝐶𝐷𝑃𝑠𝑐𝑟 = 𝛽0+ 𝛽1𝐸𝑃𝑆𝐼 + 𝛽2𝑁𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒 + 𝛽3𝐶𝑢𝑙𝑡𝑢𝑟𝑎𝑙 + 𝛽4𝑆𝑖𝑧𝑒 +
𝛽5𝑅𝑖𝑠𝑘 + 𝛽6𝑇𝑜𝑏𝑖𝑛𝑄 + 𝛽7𝑅𝑂𝐴 + 𝛽8𝐿𝑒𝑣 + 𝛽9𝐷𝐶𝐷𝑃𝑡−1 + 𝛽10𝐿𝑎𝑚𝑏𝑑𝑎 +
𝛽11−18𝑆𝑒𝑐𝑡𝑜𝑟 + 𝜀 (2)
Th ee coun y-le el independen a iables we e included in Models 1 and 2,
ep esen ing he di e en dimensions o coun ies’ ins i u ional con ex ela ed
o clima e change. Speci ically, EPSI, No ma i e and Cul u al a iables a e
56
included as illus a i e o he in luence o coun ies’ ins i u ional p essu es ela ed
o clima e change. In addi ion, six i m-le el con ol a iables we e also included
in bo h models: Size, Risk, TobinQ, ROA, Le and DCDP -1. These ac o s we e
in oduced in o he models since hey ha e been ound o be associa ed wi h
olun a y ca bon disclosu e on he pa o companies (Hahn e al., 2015; S anny,
2013; Wegene e al., 2013). Fu he mo e, dummy a iables o each sec o GICS
we e in oduced in o de o con ol he ixed e ec s o each.
As men ioned in Chap e 2, olun a y ca bon disclosu es on he pa o companies
may be a ec ed by he componen s o he egula i e pilla o ins i u ions. Figu e
2 shows he heo e ical ounda ions o Models 3 and 4, which es he in luence
o he componen s o he egula i e pilla on he decisions o companies o
olun a ily disclose ca bon in o ma ion (Model 3), as well as he quali y o he
in o ma ion epo ed (Model 4). As p e ious models, hese models include
con ol a iables o social p essu es, inancial/ma ke p essu es. In o de o es
whe he he componen s o he clima e- ela ed egula i e pilla in luence
companies’ decisions o olun a ily disclose ca bon in o ma ion, as well as on he
quali y o disclosu es ( he second objec i e o he hesis), an addi ional wo-s ep
model is pe o med.
The i s s age is based on a p obi model whe e he dependen a iable is
DCDP15, namely a dicho omous a iable o olun a y ca bon disclosu e (Model
3). This a iable equals 1 i he company answe ed he 2015 CDP clima e
ques ionnai e and made he esponse public, and ze o o he wise. As in Model 1,
companies’ decisions bo h o espond and o publish a e conside ed in he same
eg ession because he majo i y o companies in he sample ha did eply o he
CDP su ey also made hei esponse public. The second model conside s he
quali y o ca bon disclosu es, which is measu ed using he CDP disclosu e sco e
(Ben-Ama & McIlkenny, 2014). I encapsula es he quali y and
57
comp ehensi eness o he ca bon in o ma ion p o ided h ough he CDP clima e
su ey (Lemma e al., 2019).
Figu e 2. In luence o he componen s o he egula i e dimension o ins i u ions
on olun a y ca bon disclosu es
Model 3, which es he in luence o he componen s o he egula i e dimension
o in ui ions on companies’ decisions o olun a ily disclose ca bon in o ma ion,
is as ollows:
𝐷𝐶𝐷𝑃 = 𝛽0+ 𝛽1𝐿𝑎𝑤𝑠 + 𝛽2𝐸𝑃𝑆𝐼 + 𝛽3𝑅𝑒𝑤𝑎𝑟𝑑𝑠 + 𝛽4𝑆𝑖𝑧𝑒 + 𝛽5𝑅𝑖𝑠𝑘 +
𝛽6𝑇𝑜𝑏𝑖𝑛𝑄 + 𝛽7𝑅𝑂𝐴 + 𝛽8𝐿𝑒𝑣 + 𝛽9𝐷𝐶𝐷𝑃𝑡−1 + 𝛽10−17𝑆𝑒𝑐𝑡𝑜𝑟𝑠 + 𝜀 (3)
Due o he a ailabili y o da a ega ding he componen s o he egula i e pilla ,
Model 3 was es ed o a subsample o 2,176 i ms (Tu kish and Sou h A ican
companies we e excluded). As men ioned be o e, he second s ep conside s a
sub-sample o i ms ha esponded o he 2015 CDP clima e su ey and made
hei esponse public. Focusing solely on esponding i ms may in oduce sel -
Con ol a iables:
•Size
•Risk
•TobinQ
•ROA
•Le
•DCDP -1
•Sec o s
Volun a y ca bon
disclosu es
Rules and laws (H4a; H4b)
Moni o ing mechanisms and
penal ies (H5a; H5b)
Rewa ds (H6a; H6b)
Componen s o he egula i e
dimension o ins i u ions
64
he egula i e pilla since, acco ding o NIS heo y, companies headqua e ed in
coun ies wi h a ewa ds sys em in place o beha iou in line wi h clima e- ela ed
egula ion will be mo e likely o pa icipa e in he CDP ques ionnai e and o
ecei e a highe CDP sco e. The g ea e he in es men s made by coun ies in
clean ene gy, he g ea e he ewa ds ha companies will ecei e. The e o e,
ewa ds encou age companies o make new in es men s as well as o adop
measu es o mi iga e hei ca bon emissions, which may posi i ely con ibu e o
olun a y ca bon disclosu e and o he quali y o he in o ma ion disclosed.
3.4.3. Con ol a iables
Size. Legi imacy heo y a gues ha la ge i ms a e subjec o g ea e social
p essu e. Thus said i ms will be willing o olun a ily disclose ca bon in o ma ion,
as well as o p o ide high quali y o ca bon da a in o de o demons a e hei
compliance wi h social expec a ions and o p e en hei legi imacy om being
h ea ened (Cho & Pa en, 2007; Solomon & Lewis, 2002). Size has been used as
a con ol a iable in se e al p e ious s udies ela ed o en i onmen al disclosu e
(Co mie e al., 2005; Liu and Anbumozhi, 2009; Ma ínez e al., 2015; Ma iso
2013), and hey all ag ee ha he e is a posi i e and signi ican ela ionship
be ween size and ca bon epo ing. I is he e o e expec ed ha company size will
ha e a posi i e e ec , bo h on he pa icipa ion o companies in he CDP as well
as on he sco e hey ob ain. Size is measu ed by he na u al loga i hm o o al
e enues (Co e & Najah, 2012; Ma iso , 2013).
Risk. P e ious s udies con i m ha he e is a posi i e and signi ican ela ionship
be ween a company’s ola ili y o isk and en i onmen al in o ma ion disclosu e
(Co mie e al., 2005). Acco ding o s akeholde heo y, i ms wi h a highe le el
o business isk a e mo e likely o pa icipa e in ca bon epo ing in o de o allow
in es o s and c edi o s o e alua e his in o ma ion mo e accu a ely (Tang & Luo,
2011). This a iable has been included in o de o measu e company isk, which
is expec ed o be posi i ely associa ed wi h olun a y ca bon disclosu e (Tang &

65
Luo, 2011). The Risk a iable e e s o a company’s be a, which is based on 23 o
35 consecu i e end-o -mon h p ice pe cen age changes and hei ela i i y o he
local ma ke index.
TobinQ. Acco ding o Luo e al. (2012), his a iable is in oduced as an
app oxima ion o companies' u u e g ow h expec a ions. Fi ms wi h a highe
TobinQ will be mo e likely o disclose mo e in o ma ion in o de o educe
in o ma ion asymme ies. Thus, in es o s will be e able o calcula e he ma ke
alue o hese i ms and hei in angible asse s (S anny & Ely, 2008). The p e ious
li e a u e does no es ablish a conclusi e ela ionship be ween en i onmen al
disclosu e and TobinQ. Many s udies do no ind a signi ican ela ionship
be ween bo h a iables (González-González & Zamo a-Ramí ez, 2016b; Luo e al.,
2012; Tang & Luo, 2011; Wegene e al., 2013). TobinQ is calcula ed as he sum
o he company's ma ke alue plus p e e ed sha es plus he book alue o long-
e m deb and cu en liabili ies, di ided by he book alue o he o al asse s (P.
M. Cla kson e al., 2008). TobinQ is expec ed o ha e a posi i e and signi ican
impac on companies’ p opensi y o disclose olun a y ca bon in o ma ion
(González-González & Zamo a-Ramí ez, 2016b), as well as on he quali y o
disclosu es (Tang & Luo, 2011).
ROA. The p e ious li e a u e on olun a y disclosu e a gues ha he inancial
pe o mance o companies may in luence en i onmen al disclosu e. In his way,
p o i able companies may be be e posi ioned o add ess he cos s associa ed
wi h educing ca bon emissions (Bewley & Li, 2000). Howe e , o he mos pa ,
empi ical s udies do no demons a e a conclusi e ela ionship be ween company
p o i abili y and ca bon epo ing (Chu e al. , 2012; Luo e al., 2013; Rankin e al.,
2011). In his hesis, i is assumed ha company p o i abili y will posi i ely and
signi ican ly in luence bo h companies’ pa icipa ion in olun a y ca bon
epo ing (Luo e al., 2013) and he quali y o he in o ma ion epo ed (Tang &
Luo, 2011). ROA (Re u n on Asse s), as measu ed as ea nings be o e in e es and
66
axes di ided by o al asse s (Penman, 2007; Sub amanyam & Wild, 2009), is used
as an app oxima ion o company p o i abili y.
Le . Fi ms wi h highe le els o le e age will be subjec o g ea e p essu e om
hei s akeholde s. Hence hese i ms will be willing o pa icipa e in ca bon
epo ing in o de o espond o he demands o he a o emen ioned
s akeholde s and o imp o e hei inancial lexibili y (S anny & Ely, 2008). Wi h
ega d o he in luence o le e age on en i onmen al disclosu e, empi ical s udies
ha e no achie ed consis en esul s. Some au ho s ha e no ound a signi ican
ela ionship be ween companies’ le e age and hei le el o en i onmen al
disclosu e (e.g. F eedman and Jaggi, 2005; P ado-Lo enzo e al., 2009; S anny and
Ely, 2008). And ikopoulos and K iklani (2013) analysed he en i onmen al
disclosu es o companies lis ed on he Copenhagen S ock Exchange and ound a
nega i e ela ionship be ween he le el o le e age o hese companies and hei
disclosu e. On he con a y, Cla kson e al. (2008) obse ed a posi i e and
signi ican ela ionship be ween le e age and en i onmen al disclosu e.
Following F eedman and Jaggi (2005), i is assumed ha le e age will posi i ely
and signi ican ly in luence esponse o he CDP ques ionnai e and he sco e
ob ained. To al deb o o al asse s a io is used o measu e he companies’
le e age (Bo ghei and Leung, 2013).
DCDP -1. This indica o e lec s i ms’ p io disclosu e beha iou wi h espec o
CDP pa icipa ion. I has been included in he s udy because i ms’ p io CDP
disclosu e is he mos signi ican ac o in luencing i s u u e olun a y ca bon
disclosu e beha iou (S anny, 2013). DCDP -1 is a dummy a iable which is equal
o 1 i i m disclosed he p e ious CDP, and 0 o he wise.
Chap e 4. Resul s
B eakdown o clima e- ela ed
ins i u ional p o ile by coun y,
desc ip i e s a is ics, co ela ion
analysis, eg ession esul s, and
obus ness checks
69
This chap e p esen s a b eakdown o he ins i u ional p o ile ela ed o clima e
change and companies by coun y, as well as a summa y o i ms by sec o . I also
p o ides an o e iew o he componen s o coun ies’ clima e- ela ed egula i e
con ex , along wi h s a is ics co esponding o i ms’ pa icipa ion in he CDP
su ey and he a e age CDP sco e by coun y. Finally, his chap e de ails he
desc ip i e analyses, he empi ical esul s, and he obus ness checks.
4.1. B eakdown o clima e- ela ed ins i u ional p o ile by coun y
Table 3 p esen s he dis ibu ion o coun ies’ ins i u ional p o ile ela ed o
clima e change and companies by selec ed coun ies. This able also shows
s a is ics co esponding o i ms’ esponses o he CDP ques ionnai e as well as
he p opo ion o he CDP disclosu e sco e by coun y.
As shown in Table 3, Japanese i ms cons i u e he la ges g oup o he sample
(397 ou o 2,327, o 17.06%). The second la ges g oup con ains companies om
he Uni ed S a es o Ame ica, ollowed by i ms headqua e ed in he Uni ed
Kingdom and F ance. Toge he hey accoun o mo e han 50 pe cen o he
sample. Coun ies wi h a highe esponse a e o he 2015 CDP ques ionnai e a e
Sou h A ica, he Uni ed Kingdom and he Uni ed S a es, wi h esponse a es o
83.87, 78.54 and 68.7 pe cen espec i ely. In a e age e ms, he 2015 CDP
disclosu e sco e is highe in Sou h Ko ea, Sou h A ica and India, all ecei ing a
CDP disclosu e sco e g ea e han 90 poin s. Al hough hese coun ies do no
ha e high le els o egula i e p essu es (as shown by he EPSI a iable), hey do
p esen a signi ican deg ee o conce n ega ding clima e change, as shown by
he clima e change index in he ou h column o Table 3.
In e ms o clima e- ela ed egula i e p essu es, coun ies wi h highe le els o
clima e- ela ed egula i e s ingency a e he Uni ed Kingdom, F ance, Canada
and I aly. These coun ies ha e an EPSI index g ea e han 3.25 poin s. I is o no e

70
ha hey all ha e a ca bon p icing ins umen in place a he na ional o sub-
na ional le el (Kossoy e al., 2015).
Table 3. Dis ibu ion o clima e- ela ed ins i u ional p o ile and i ms by coun y
Coun y
EPSI
No m
Cul u al
N
%
R
%
CDP
Sco e
Aus alia
3.17
39.00
8.75
179
7.69
63
35.20
81.48
Canada
3.28
59.50
9.45
134
5.76
79
58.96
84.56
F ance
3.58
39.20
9.94
210
9.02
77
36.67
86.73
Ge many
3.06
44.08
9.49
144
6.19
76
52.78
74.83
India
1.82
29.50
10.77
142
6.10
30
21.13
93.07
Indonesia
1.08
20.00
9.21
40
1.72
4
10.00
53.00
I aly
3.28
53.00
10.12
69
2.97
36
52.17
86.00
Japan
3.17
46.60
10.11
397
17.06
206
51.89
89.23
Sou h A ica
0.71
80.00
9.44
62
2.66
52
83.87
94.60
Sou h Ko ea
3.07
34.80
10.03
207
8.90
45
21.74
94.62
Tu key
1.92
41.00
9.28
89
3.82
27
30.34
77.89
UK
3.83
70.86
8.78
261
11.22
205
78.54
84.49
USA
2.69
69.00
8.78
393
16.89
270
68.70
86.42
To al
2,327
100.00
1,170
50.28
85.98
No es: N = o al sample i ms. R = numbe o i ms ha answe ed and made public he
2015 CDP ques ionnai e. CDP sco e is he a e age 2015 CDP disclosu e sco e by coun y,
which is calcula ed using he o al CDP disclosu e sco e o esponding i ms di ided by
o al numbe o esponding i ms in he coun y. EPSI a iable ep esen s he s ingency
o each coun y’s speci ic en i onmen al policy. No m e lec s he pe cen age o
companies ha answe ed he CDP ques ionnai e in he p e ious yea in a gi en coun y.
Cul u al is an index ha e lec s coun ies’ clima e change conce n. UK = Uni ed
Kingdom. US = he Uni ed S a es o Ame ica.
As shown in he hi d column o Table 3, mo e han 60 pe cen o companies
om Sou h A ica, he Uni ed Kingdom and he Uni ed S a es o Ame ica
pa icipa ed in he CDP su ey he p e ious yea . This e lec s he highe le el o
no ma i e p essu es wi h espec o clima e change in hese coun ies, especially
in Sou h A ica (80 pe cen ). In ela ion o he cul u al-cogni i e pilla o
ins i u ions, Sou h Ko ea, Japan, I aly and India ha e a sco e g ea e han 10
poin s in he clima e change conce n su ey, which would sugges ha socie y in
hese coun ies belie es global clima e change o be a se ious p oblem. I can be
seen om he da a in Table 3 ha companies headqua e ed in coun ies wi h
71
high cul u al-cogni i e p essu es a e mo e likely o disclose high-quali y ca bon
in o ma ion, as e idenced by he high mean o hei CDP disclosu e sco es.
Table 4 below p esen s an o e iew o he componen s o coun ies’ clima e-
ela ed egula i e con ex , as well as he pa icipa ion o companies in he 2015
CDP clima e epo by coun y, showing hei espec i e CDP disclosu e sco e.
Table 4. B eakdown o he componen s o he egula i e pilla and i ms by coun y
Coun ies
Laws
EPSI
Rewa ds
N
%
R
%
CDP sco e
Aus alia
9
3.17
0.18
179
8.23
63
35.20
81.48
Canada
3
3.28
0.25
134
6.16
79
58.96
84.56
F ance
9
3.58
0.15
210
9.65
77
36.67
86.73
Ge many
15
3.13
0.50
144
6.62
76
52.78
74.83
India
11
1.82
0.39
142
6.53
30
21.13
93.07
Indonesia
19
1.08
0.03
40
1.84
4
10.00
53.00
I aly
22
3.28
0.12
69
3.17
36
52.17
86.00
Japan
9
3.17
0.96
397
18.24
206
51.89
89.23
Sou h Ko ea
12
3.07
0.17
207
9.51
45
21.74
94.62
Uni ed Kingdom
23
3.83
0.83
261
11.99
205
78.54
84.49
Uni ed S a es
9
2.69
0.34
393
18.06
270
68.70
86.42
To al
2,176
100.00
1,091
50.14
85.77
No es: N = o al sample i ms. CDP sco e is he a e age 2015 CDP disclosu e sco e o
esponding i ms, which is calcula ed as he o al CDP sco e o esponding i ms di ided
by he o al numbe o esponding companies. R = numbe o i ms ha answe ed and
made public he 2015 CDP ques ionnai e. Laws a iable ep esen s he numbe o
clima e- ela ed laws o a coun y. EPSI is an index ha measu es he s ingency o each
coun y’s speci ic en i onmen al policy. Rewa ds a iable e lec s he coun y’s
in es men s in clean ene gy, which is ep esen ed as a pe cen age o he coun y’s GDP.
As men ioned in Chap e 3, in he s udy o he componen s o coun ies’ clima e-
ela ed egula i e pilla , companies om wo coun ies (Sou h A ica and Tu key)
we e emo ed om he sample due o he a ailabili y o da a ega ding he
ewa ds componen o he egula i e con ex . The e o e, he coun ies unde
conside a ion a e Aus alia, Canada, F ance, Ge many, India, Indonesia, I aly,
Japan, Sou h Ko ea, he Uni ed Kingdom and he Uni ed S a es o Ame ica. Thus,
he sample con ains 11 coun ies, wi h Japan p opo ionally he la ges (18.24 pe
cen o he o al sample). The Uni ed S a es and he Uni ed Kingdom make up he
72
second and hi d la ges g oups in e ms o he numbe o companies su eyed
by he CDP, accoun ing o 18.06 and 11.99 pe cen o he s udy sample
espec i ely. As shown in Table 4, he coun ies wi h highes esponse a e o he
CDP ques ionnai e a e he Uni ed Kingdom, he Uni ed S a es and Canada, wi h
alues o 78.54, 68.70 and 58.96 pe cen espec i ely. In e ms o he quali y o
ca bon disclosu es, companies om India, Japan and Sou h Ko ea display he
highes le els o quali y, as shown by hei high a e age CDP disclosu e sco e.
Wi h ega d o he ules componen o he egula i e pilla , i can be seen om
he da a in Table 4 ha he Uni ed Kingdom, I aly and Indonesia a e he sample
coun ies wi h he highes numbe o clima e change- ela ed laws. Fo hei pa ,
he coun ies wi h he lowes numbe o clima e change- ela ed laws a e Canada,
Aus alia, F ance, Japan and he Uni ed S a es, all o which ha e less han en
pieces o egula ion ela ed o clima e change.
The coun ies wi h he highes egula i e s ingency a e he Uni ed Kingdom,
F ance, Canada and I aly. They all ha e an EPSI index g ea e han 3.2 poin s.
These coun ies ha e a ca bon p icing ins umen in place a he na ional o
subna ional le el (Kossoy e al., 2015). In a e age e ms, coun ies wi h s ingen
clima e change- ela ed egula ion ha e a g ea e numbe o companies
disclosing ca bon in o ma ion o he CDP. In addi ion, companies headqua e ed
in hese coun ies ha e, on a e age, a be e CDP sco e, which means ha hey
a e disclosing high-quali y in o ma ion ega ding hei ca bon emissions. I is o
no e ha ce ain coun ies such as India, Indonesia, Sou h A ica and Tu key,
whe e he e is no ca bon p icing implemen ed, ha e less han 2 poin s in he EPSI
index. The coun ies wi h he g ea es le el o clima e- ela ed ewa ds a e Japan,
he Uni ed Kingdom and Ge many, which p esen alues o 0.96, 0.83 and 0.50
espec i ely. Con e sely, he coun ies wi h he lowes le el o in es men s in
clean ene gy a e Indonesia, I aly and F ance.
73
Table 5 shows he b eakdown o i ms by sec o . As shown in Table 5, Consume
disc e iona y, Indus ials and Ma e ials a e he la ges g oup in he 2015 CDP
clima e epo . U ili ies companies ha e he highes a e age CDP sco e (mo e
han 93 poin s, on a e age). Such companies, o example, elec ic u ili ies a e
unde highe egula i e p essu es ha o ce hem o con ol and epo hei
ca bon emissions (Kolk e al., 2008). Thus, hese companies ake ad an age o he
syne gy o pa icipa e in olun a y ca bon epo ing, such as he CDP.
Telecommunica ion companies ha e he highes esponse a e, ollowed by
In o ma ion Technology and Ma e ial companies.
Table 5. Dis ibu ion o i ms by sec o
DCDP = 0
DCDP = 1
To al
A e age
CDP
sco e
Sec o
Numbe
o i ms
Pe cen age
(%)
Numbe
o i ms
Pe cen age
(%)
Consume Disc e iona y
286
56.86
217
43.14
503
83.56
Consume S aples
98
47.34
109
52.66
207
92.06
Ene gy
105
57.69
77
42.31
182
86.13
Heal h Ca e
124
62.31
75
37.69
199
81.43
Indus ials
225
45.55
269
54.45
494
84.4
In o ma ion Technology
104
41.43
147
58.57
251
84.64
Ma e ials
136
42.63
183
57.37
319
88.1
Telecommunica ion
17
32.69
35
67.31
52
85.89
U ili ies
62
51.67
58
48.33
120
93.36
To al
1,157
49.72
1,170
50.28
2,327
85.98
No es: The a e age CDP sco e is he sum o he o al CDP disclosu e sco e o sample
companies ha eplied he CDP su ey di ided by o al numbe o esponding i ms in
he sec o .
4.2. Desc ip i e s a is ics
Table 6 epo s he desc ip i e s a is ics o bo h dependen and independen
a iables. Mo e speci ically, i de ails he mean, s anda d de ia ion, minimum,
pe cen iles (25, 50 and 75) and maximum o each o he a iables in oduced in
he s udy. In o de o educe he impac o ex eme alues on he esul s, all
con inuous independen a iables we e winso ised a 1 pe cen in he uppe and
lowe ails o he dis ibu ion.
80
ep esen s he in e se Mill’s a io, is in oduced as an addi ional independen
a iable in Model 2 o accoun o selec i i y bias in he sample. As shown in
Model 2 o Table 8, he es ima ed coe icien o Lambda a iable is no signi ican ,
sugges ing ha he e is no no ewo hy sample selec ion p oblem. Dummy
a iables o con ol o sec o - ixed e ec s we e included in bo h models.
The EPSI a iable, which ep esen s coun ies’ egula i e con ex ela ed o
clima e change, shows a posi i e and signi ican ela ionship wi h companies’
decisions o olun a ily disclose ca bon in o ma ion (0.15, p < .05; Model 1). This
esul suppo s Hypo hesis H1a, hus indica ing ha coun ies’ clima e change-
ela ed egula i e con ex posi i ely in luences companies’ decisions o
olun a ily pa icipa e in he CDP ques ionnai e in said coun ies. Thus, i ms
headqua e ed in coun ies cha ac e ized by ha ing s ic clima e- ela ed
egula ions a e mo e likely o olun a ily disclose ca bon in o ma ion. On a e age,
he EPSI a iable has an impac consis ing o a 6 pe cen inc ease in he
p obabili y o esponding o each uni inc ease in he alue o his a iable. The
esul s o he EPSI a iable a e consis en wi h p e ious s udies (F eedman &
Jaggi, 2005; Ji a & To el, 2013; Luo e al., 2012), despi e being ocused on gene ic
en i onmen al egula ions.
Con a y o ini ial expec a ions (as ega ds H1b), I ind ha coun ies’ egula i e
p essu es a e no signi ican ly associa ed wi h he quali y o disclosu es (see
Model 2 o Table 8). Al hough clima e- ela ed egula ions a e es ablished in mos
o he sample coun ies (Kossoy e al., 2015; Nachmany e al., 2015), hey a e no
p o ing e ec i e enough o mo i a e companies o olun a ily disclose high-
quali y ca bon in o ma ion. The e o e, he egula i e pilla o ins i u ions is no
mo i a ing o ganiza ions o make an “ex a e o ” (González-González &
Zamo a-Ramí ez, 2016a; Hess & Wa en, 2008) in o de o disclose high-quali y
and comp ehensi e ca bon in o ma ion h ough a olun a y mechanism. This
could be because his pilla is based on coe ci e mechanisms which ein o ce

81
egula i e ules, which in u n ha e a lowe impac as ega ds mo i a ing
companies o disclose high-quali y ca bon in o ma ion h ough he CDP su ey.
The es ima ed coe icien o he No m a iable is signi ican ly posi i e a he
maximum le el o he esponse decision (2.18, p < .01; Model 1), as well as o
he disclosu e quali y (1.84, p < .01; Model 2). This inding suppo s bo h
hypo heses H2a and H2b, indica ing ha i ms’ p opensi y o disclose and he
quali y o he in o ma ion epo ed bo h inc ease in line wi h coun ies’ clima e-
ela ed no ma i e p essu es. The e o e, he g ea e he dissemina ion o he CDP
ques ionnai e in a gi en coun y, as e idenced by he numbe o esponding
companies, he g ea e he no ma i e p essu e on companies o bo h pa icipa e
in he CDP and epo high-quali y ca bon in o ma ion. Consis en wi h NIS
heo y, hese companies will disclose ca bon in o ma ion o he CDP because hey
belie e ha i is he mo ally igh hing o do in his con ex , which in u n helps
hem o p o ec hei legi imacy (Sco , 2014).
The Cul u al a iable is no signi ican ly associa ed wi h i ms’ decisions o
pa icipa e in he CDP su ey (see Model 1 o Table 8). This esul does no
suppo he hypo hesis ha coun ies’ clima e- ela ed cul u al con ex s posi i ely
in luence companies’ decisions o olun a ily disclose ca bon da a (H3a). This
could be a ec ed by he ac ha he cul u al-cogni i e pilla is based on hose
mo e sub le aspec s o social eali y (Sco , 2014). People and o ganiza ions ake
longe o in e nalize and inco po a e hem in o hei beha iou and, he e o e, o
be able o pu p essu e on he beha iou o companies ega ding esponding he
CDP ques ionnai e.
In he disclosu e quali y model (Model 2), he coe icien o he Cul u al a iable
is posi i e and signi ican a he maximum le el (0.57, p < .01). This esul p o ides
suppo o hypo hesis H3b, which s a es ha coun ies’ cul u al-cogni i e
p essu es posi i e and signi ican ly in luence he quali y o he in o ma ion
82
disclosed. The e o e, i ms headqua e ed in coun ies wi h high le els o clima e
change awa eness will be mo e likely o olun a ily disclose high-quali y
in o ma ion, gi en ha such a e he pa e ns ollowed in hese coun ies (Sco ,
2014). Thus, he g ea e he conce n ega ding clima e change in a gi en coun y,
he mo e in e nalized he p oblem o clima e change in said coun y’s socie y will
be. The e o e, cogni i e schemes ela ed o clima e change in said coun y’s
socie y will be mo e widesp ead and sha ed o a g ea e deg ee in o de o ob ain
imp o ed conside a ion and in e p e a ion as ega ds he p oblem o clima e
change. This in u n leads o g ea e cul u al-cogni i e p essu es on companies in
said coun y o adop measu es o add ess clima e change which, in his case,
implies g ea e p essu e o hem o p o ide high-quali y ca bon in o ma ion o
he CDP.
Taken oge he , he esul s sugges ha he signi ican ac o s ela ed o he
esponse decision di e om he signi ican ac o s ela ed o disclosu e quali y.
The e o e, his s udy p o ides e idence agains analysing companies’ decisions
o olun a ily disclose ca bon in o ma ion and he quali y o hei disclosu es
oge he . This is in con as o he p io li e a u e on olun a y ca bon disclosu e
which uses a Tobi model in o de o explain bo h aspec s (e.g. González-González
and Zamo a-Ramí ez, 2016b; Guen he , Guen he , Schiemann, and Webe , 2016;
Ma eo-Má quez e al., 2020).
Rega ding he con ol a iables, consis en wi h p io s udies (e.g. Luo, 2019;
Rankin e al., 2011; S anny, 2013), he coe icien o Size a iable is posi i e and
signi ican bo h in Models 1 and 2, sugges ing ha la ge i ms end o espond
o he CDP ques ionnai e, as well as o p o ide high-quali y ca bon da a.
Acco ding o legi imacy and s akeholde heo ies, hese i ms a e mo e likely o
olun a ily disclose ca bon in o ma ion owing o demands om hei
s akeholde s, as well as om he gene al public (Luo e al., 2012; Pa en, 2002). In
83
addi ion, hese companies ha e mo e esou ces o disclose high-quali y ca bon
in o ma ion (Ben-Ama & McIlkenny, 2014).
TobinQ also p esen s a posi i e and signi ican coe icien in Model 2, which
indica es ha companies wi h high u u e g ow h expec a ions end o disclose
high-quali y ca bon in o ma ion o allow in es o s and c edi o s o be e
de e mine hei alue. Howe e , TobinQ is no associa ed wi h i ms’ decisions o
olun a ily disclose ca bon da a. P e ious s udies on i ms’ esponse o he CDP
su ey ha e also epo ed no signi ican coe icien s o his a iable (Luo e al.,
2012; S anny & Ely, 2008). In addi ion, esponding o he CDP ques ionnai e in
yea -1 (measu ed by he a iable DCDP -1) posi i ely and signi ican ly in luences
bo h i ms’ decisions o espond o he CDP in yea and he quali y o he
in o ma ion epo ed in ha yea . This is consis en wi h he indings o S anny
(2013), who sugges s ha i ms’ p io CDP disclosu e is he mos signi ican ac o
in luencing i s u u e olun a y ca bon disclosu e beha iou . The coe icien s o
Risk, ROA and Le a e no signi ican o ei he Model 1 o Model 2.
Table 9 p esen s he p obi eg essions o Models 3 and 4. These models examine
he in luence o he componen s o he egula i e dimension o ins i u ions on
he decisions o i ms o answe he CDP ques ionnai e (Model 3), as well as on
he quali y o hei disclosu es (Model 4).
As shown in Model 3 o Table 9, he chi-squa e alue o 1,665.21 is signi ican a
he maximum le el, indica ing ha he model was able o dis inguish hose
sample companies ha olun a ily disclosed ca bon in o ma ion ia he CDP om
hose ha did no .
Table 9 also shows ha Model 3 co ec ly p edic ed he ou come o he disclosu e
decision o mo e han 80 pe cen o he companies in he sample. The pseudo-
R2 o he Model 3 is 0.552, which is compa able o p e ious olun a y disclosu e
li e a u e (Rankin e al., 2011; S anny & Ely, 2008).
84
Table 9. P obi eg essions (Models 3 and 4)
Model 3 - Response decision
Model 4 - Disclosu e quali y
Va iables
P edic ed
sign
Coe .
z-s a
ME
P edic ed
sign
Coe .
z-s a
ME
Laws
+
0.01*
1.69
0.01*
+
0.01*
1.92
0.01*
EPSI
+
0.47***
5.29
0.18***
+
0.03
0.28
0.01
Rewa ds
+
0.41***
3.19
0.16***
+
0.39**
2.44
0.15**
Size
0.17***
6.41
0.07***
0.43***
9.77
0.17***
Risk
0.03
0.31
0.01
-0.15
-1.32
-0.05
TobinQ
-0.01
-0.45
-0.01
0.03
0.75
0.01
ROA
1.40**
2.06
0.55**
-0.62
-0.68
-0.25
Le
0.13
0.55
0.05
0.60**
2.01
0.23**
DCDP -1
2.37***
28.06
0.75***
1.01*
1.68
0.35*
Lambda
-
-
-
0.49
1.03
0.19
Cons an
-5.67***
-10.32
-
-8.44***
-5.46
-
Chi-squa e
1,665.21***
282.82***
Log likelihood
-675.67
-631.74
Pseudo R2
0.552
0.164
% Co ec ly p edic ed
88.79%
69.20%
Numbe o obse a ions
2,176
1,091
Con ol o sec o e ec s
yes
yes
No es: *, **, *** ep esen coe icien s signi ican a he 0.1, 0.05 and 0.01 le els
espec i ely ( wo- ailed). ME = Ma ginal e ec s. All a iables a e desc ibed in Table 2.
Model 4 is based on a sub-sample o 1,091 companies which bo h esponded o
he 2015 CDP clima e su ey and made hei esponse public. The dependen
a iable is an indica o a iable equal o 1 i he company ob ained a CDP
disclosu e sco e g ea e han 93 ( he median CDP sco e o esponding
companies), and ze o o he wise. The likelihood- a io chi-squa e alue o 282.82
is signi ican a p < .01, which indica es ha ou model as a whole i s signi ican ly.
Table 9 also shows ha he Model 4 co ec ly p edic ed he ou come o disclosu e
quali y o 69 pe cen o he companies in he sample. As in Model 2, he in e se
Mills a io was included a his s age as an addi ional independen a iable
(Lambda), so as o accoun o sample selec ion bias. As can be seen, he Lambda
coe icien is no signi ican , indica ing ha he e is no selec i i y bias o any no e
in he sample. As men ioned in Chap e 3, Models 3 and 4 do no conside Sou h
85
A ican and Tu kish companies due o he a ailabili y o da a ega ding he ewa d
componen o egula i e pilla o hese coun ies.
As shown in Model 3 o Table 9, he ules componen o he egula i e pilla
ela ed o clima e change (as ep esen ed by he Laws a iable) shows a posi i e
and signi ican ela ionship wi h companies’ decisions o olun a ily disclose
ca bon in o ma ion (0.01, p- alue < .10). On a e age, howe e , i s impac on he
p obabili y o esponding o he CDP ques ionnai e is qui e limi ed since his
a iable has an impac o 0.5 pe cen inc ease in he p obabili y o esponding
o each uni inc ease in he alue o his ac o . This esul is consis en wi h he
hypo hesis H4a, which sugges s ha he numbe o clima e change- ela ed laws
o coun ies does in luence he le el o pa icipa ion o i ms in said coun ies in
he CDP su ey. This suppo s he NIS idea ha clima e- ela ed laws, apa om
placing p essu e on a ge companies, con ibu e o he gene a ion o social
expec a ions conce ning companies’ en i onmen al beha iou which may a ec
he beha iou o companies bo h subjec and no o said laws. Thus, companies
will olun a ily disclose ca bon- ela ed in o ma ion in o de o adap hemsel es
o he social expec a ions p e alen in hei ins i u ional con ex (Sco , 2014). The
esul o his egula i e componen is consis en wi h p io s udies (F eedman &
Jaggi, 2005; Luo e al., 2012), despi e hei being based on gene ic en i onmen al
egula ions.
Rega ding he quali y o disclosu es (see Model 4 o Table 9), he es ima ed
coe icien o Laws is signi ican ly posi i e (0.01, p < .10), indica ing ha he
numbe o clima e- ela ed laws enac ed by a coun y posi i ely in luences he
quali y o i ms’ ca bon disclosu e. This p o ides suppo o hypo hesis H4b
which sugges s ha he quali y o ca bon in o ma ion disclosed by companies
inc eases wi h he numbe o clima e- ela ed laws o he coun y in which hey
ope a e.

86
P essu es o igina ing om moni o ing mechanisms and punishmen s a e
measu ed by he EPSI a iable, which e lec s coun ies’ clima e change- ela ed
egula i e s ingency. This a iable is posi i ely and signi ican ly associa ed wi h
i ms’ pa icipa ion in he CDP ques ionnai e. Mo e speci ically, he es ima ed
coe icien o EPSI is 0.47 which is signi ican a he maximum le el (see Model 3
o Table 9), indica ing ha moni o ing mechanisms and punishmen s ela ed o
clima e change posi i ely and signi ican ly a ec companies’ olun a y ca bon
disclosu e beha iou . This suppo s hypo hesis H5a, namely ha companies in
coun ies wi h s ingen moni o ing mechanisms and punishmen s ela ed o i s
clima e laws a e mo e likely o olun a ily pa icipa e in he CDP su ey.
Con a y o ini ial p edic ions, moni o ing mechanisms and punishmen s (as
measu ed by he EPSI a iable) a e no signi ican ly associa ed wi h he quali y o
ca bon disclosu es. Thus he empi ical e idence is no ully consis en wi h my
p edic ions as made in H5b. This esul could occu because in spi e o he ac
ha moni o ing mechanisms and punishmen s ha e been implemen ed in he
majo i y o he sample coun ies, hey a e no e ec i e enough o encou age
i ms o olun a ily disclose high-quali y ca bon in o ma ion. O ganiza ions gain
legi imacy by pa icipa ing in he CDP. Highe le els o quali y in ca bon
in o ma ion equi es an “ex a e o ” on he pa o o ganiza ions (González-
González & Zamo a-Ramí ez, 2016a; Hess & Wa en, 2008), an e o which o i s
pa is no in luenced by moni o ing mechanisms and penal ies, since hese se e
a he o ein o ce he coe ci e cha ac e o egula ion. This in u n exe s less
p essu e on hose o ganiza ions pa icipa ing in he CDP, gi en ha he CDP is in
ac a ehicle o he olun a y disclosu e o in o ma ion.
The ewa ds componen o egula i e pilla is measu ed by he Rewa ds a iable,
which is based on an index ep esen ing coun ies’ in es men s in clean ene gy.
The es ima ed coe icien o he Rewa ds a iable is also ound o be posi i e and
signi ican a he maximum le el (0.41, p- alue < .01), which p o ides suppo o
87
hypo hesis H6a and sugges s ha companies’ p opensi y o olun a ily epo
ca bon da a inc eases in line wi h coun ies’ ewa d mechanisms ela ed o
clima e change (see Model 3 o Table 9). This inding suppo s he a gumen o
NIS heo y ha companies headqua e ed in coun ies which ha e implemen ed
a ewa ds sys em o beha iou in line wi h es ablished clima e change egula ion
will be mo e likely o disclose ca bon in o ma ion. Consis en wi h hypo hesis
H6b, he Rewa ds coe icien (0.39, p < .05) is signi ican wi h a p edic ed posi i e
sign showing ha clima e- ela ed ewa d mechanisms encou age i ms o
disclose high quali y ca bon in o ma ion (see Model 4 o Table 9). The e o e,
companies in coun ies wi h a ewa ds sys em o beha iou in line wi h
es ablished clima e change egula ion a e mo e likely o p o ide high quali y
ca bon in o ma ion.
Wi h espec o he con ol a iables included in Models 3 and 4, Size is posi i ely
ela ed o i ms’ p opensi y o disclose ca bon in o ma ion, wi h a signi ican
coe icien a he 1 pe cen le el. This inding is consis en wi h p e ious s udies
(Luo e al., 2012; Pa en, 2002), and sugges s ha la ge i ms a e mo e likely o
olun a ily pa icipa e in he CDP su ey. In Model 4 o Table 9, he es ima ed
coe icien o Size is also signi ican ly posi i e (0.43, p < .01), which indica es ha
la ge companies a e mo e likely o disclose high-quali y ca bon in o ma ion in
o de o legi imize hei ope a ions in esponse o hei social exposu e (Chu e
al., 2012; Liu & Anbumozhi, 2009; Luo e al., 2012). This inding is simila o ha
ound in Models 1 and 2 o his a iable.
Simila ly, he ROA a iable posi i ely and signi ican ly impac s on companies’
pa icipa ion in he CDP su ey (1.40, p < .05; Model 3). This esul is also
consis en wi h he p e ious li e a u e which indica es ha highly p o i able i ms
a e mo e likely o olun a ily disclose ca bon- ela ed in o ma ion since hey may
ha e mo e esou ces wi h which o a o d he cos s ela ed o olun a y ca bon
88
disclosu es (Bewley & Li, 2000). Howe e , his a iable does no seem o be
signi ican ly ela ed o he quali y o ca bon disclosu es (see Model 4 o Table 9).
In addi ion, esponding o he CDP ques ionnai e in yea -1 posi i ely and
signi ican ly in luences bo h i ms’ decisions o espond o he CDP in yea and
he quali y o he in o ma ion epo ed in ha yea . This is consis en wi h he
indings o S anny (2013), who sugges s ha i ms’ p io CDP disclosu e is he
mos signi ican ac o in luencing i s u u e olun a y ca bon disclosu e
beha iou . As in Models 1 and 2, disclosing ca bon in o ma ion o he CDP in yea
-1 (measu ed by DCDP -1) posi i ely and signi ican ly in luences bo h i ms’
decisions o espond o he CDP in yea and he quali y o he in o ma ion
epo ed in ha yea . Mo e p ecisely, he p edic ed p obabili y o esponding o
he CDP su ey in yea is 0.75 g ea e o hose i ms ha disclosed ca bon
in o ma ion o he CDP in yea -1 (see Model 3 o Table 9).
Fi m le e age is also posi i e and signi ican ly ela ed o he quali y o ca bon
disclosu e (0.60, p < .05; Model 4). This shows ha highly le e aged i ms a e
mo e likely o epo high-quali y ca bon in o ma ion so as o allow hei in es o s
and c edi o s o e alua e hei en i onmen al beha iou (Lemma e al., 2019; Luo,
2019). Howe e , i m le e age is no signi ican ly ela ed o companies’ decisions
o pa icipa e in he CDP ques ionnai e. On he o he hand, he coe icien s o
Risk and TobinQ a e no signi ican ly associa ed wi h ei he i ms’ p opensi y o
olun a ily disclose ca bon da a o he quali y o disclosu es.
4.5. Robus ness checks
Wi h ega d o he s udy o he in luence o h ee ins i u ional pilla s on olun a y
ca bon disclosu es, i e addi ional sensi i i y checks we e ca ied ou in o de o
asce ain whe he he esul s o his hesis a e alid. Fi s ly, in Models 1 and 2, he
a iable ha measu es coun ies’ egula i e p essu es (EPSI a iable) was eplaced
by a a iable aken om he s udy ca ied ou by Nachmany e al. (2015), which
89
conside s he numbe o clima e- ela ed laws ha a coun y has enac ed (Laws
a iable). The esul s a e p esen ed in Table 10.
Table 10. Robus es s conside ing he numbe o laws
Model (1) - Response decision
Model (2) - Disclosu e quali y
Va iables
P edic ed
sign
Coe .
z-s a
ME
P edic ed
sign
Coe .
z-s a
ME
Laws
+
0.06**
2.23
0.02**
+
0.08***
2.67
0.03***
No m
+
2.17***
6.39
0.86***
+
2.05***
4.03
0.81***
Cul u al
+
-0.11
-1.46
-0.04
+
0.66***
6.57
0.26***
Size
0.14***
5.59
0.05***
0.39***
10.75
0.15***
Risk
0.01
0.19
0.01
-0.10
-0.94
-0.04
TobinQ
-0.03
-1.21
-0.01
0.083*
1.9
0.03*
ROA
0.01
0.03
0.01
-0.89
-1.34
-0.35
Le
-0.20
-0.87
-0.08
0.39
1.4
0.156
DCDP -1
2.26***
28.43
0.73***
1.36***
2.62
0.45***
Lambda
-
-
-
0.74*
1.73
0.29*
Cons an
-3.33***
-3.82
-
-15.36***
-9.28
-
Chi-squa e
1,756.82***
286.20***
Log likelihood
-734.50
-667.882
Pseudo R2
0.544
0.176
% Co ec ly p edic ed
88.10%
69.23%
Numbe o obse a ions
2,327
1,170
Con ol o sec o e ec s
yes
yes
No es: *, **, *** ep esen coe icien s signi ican a he 0.1, 0.05 and 0.01 le els
espec i ely ( wo- ailed). ME = Ma ginal e ec s. All a iables a e desc ibed in Table 2.
The esul s o Model 1 in Table 10 sugges ha he in e ences a e quan i a i ely
unchanged and compa able o he da a epo ed in Model 1 o Table 8. As shown
in Table 10, he a iables included in he model p esen ed simila signs and
signi icance as hose epo ed in Model 1 o Table 8. Wi h ega d o he disclosu e
quali y (Model 2), he esul s a e also e y simila o hose p esen ed in Model 2
o Table 8, excep o Laws a iable, which shows a coe icien o a di e en sign
and signi icance le el. This could be because Laws a iable only cap u es he
numbe o clima e change- ela ed laws o a coun y and he e o e does no
conside o he clima e- ela ed mechanisms, as he EPSI a iable does. Howe e ,
he esul s o Laws a iable a e simila o Model 4 o Table 9, which included his
a iable.
96
Table 14. Tobi eg ession esul s
P edic ed
sign
Tobi eg ession
coe icien s
Ma ginal
e ec s on
obse able
a iable, gi en
uncenso ed
Ma ginal e ec s
on p obabili y o
being
uncenso ed
EPSI
+
5.13***(2.69)
2.27***(2.69)
0.03***(2.69)
No m
+
83.37***(7.03)
36.95***(7.09)
0.63***(7)
Cul u al
+
3.51(1.27)
1.55(1.27)
0.02(1.27)
Size
7.92***(9.28)
3.51***(9.35)
0.06***(9.23)
Risk
1.13(0.37)
0.5(0.37)
0(0.37)
TobinQ
-1.28(-1.14)
-0.57(-1.14)
0(-1.14)
ROA
5.38(0.29)
2.38(0.29)
0.04(0.29)
Le
-2.98(-0.38)
-1.32(-0.38)
-0.02(-0.38)
DCDP -1
101.61***(34.47)
49.15***(34.06)
0.64***(45.26)
Ca bon-in
2.66(0.91)
1.18(0.9)
0.02(0.91)
Cons an
-235.65***(-7.02)
-
-
To al obs
2,327
2,327
2,327
Le -censo ed
obs a cdp==0
1,157
1,157
1,157
Uncenso ed
1,050
1,050
1,050
Righ -censo ed
obs a
cdp>=100
120
120
120
Log likelihood
-6,209.51
LR Chi2
1,985.14***
Pseudo R2
0.139
No es: *** = signi ican p < 0.01, ** = signi ican p < 0.05, * = signi ican p < 0.10.
Coe icien s o he Tobi eg ession a e es ima ed by maximum likelihood me hod. T-
alues (Tobi eg ession coe icien s) and z-s a is ics (ma ginal e ec s) a e epo ed in
pa en heses. Obs = obse a ions. All a iables a e desc ibed in Table 2.
The Tobi eg ession coe icien s in pa icula should no be in e p e ed as i hey
we e linea eg ession es ima es. Hence hey mus be b oken down in o de o
assess he magni ude o he eg esso in each o he wo e ec s: on he one hand,
he e ec on he sco e ob ained by he companies ha did espond o and
publish he CDP ques ionnai e; on he o he , he e ec on he p obabili y o
pa icipa ion in he ques ionnai e on hose companies ha did no espond,
declined o pa icipa e, o did no publish he ques ionnai e. The ma ginal e ec s

97
o each o he independen a iables a e p esen ed in he las wo columns o
Table 14.
As shown in Table 14, he EPSI a iable, which ep esen s coun ies’ clima e-
ela ed egula i e p essu es, shows a posi i e and signi ican ela ionship a he
maximum le el wi h bo h companies’ p opensi y o espond o he CDP
ques ionnai e and he quali y o disclosu es (5.13, p < 0.1). This esul is simila o
ha epo ed in he esponse decision model epo ed in Table 8. Howe e , i
di e s om he disclosu e quali y model shown in Table 8. This could be because
he Tobi model does no assume he disclosu e quali y model condi ional on
posi i e pa icipa ion in he CDP ques ionnai e.
The es ima ed coe icien o No m a iable is signi ican ly posi i e (83.37, p < .01),
which indica es ha i ms in coun ies wi h high le els o no ma i e p essu es a e
mo e likely o olun a ily espond he CDP ques ionnai e, as well as o disclose
high-quali y ca bon in o ma ion. This esul is la gely consis en wi h hose
epo ed in Models 1 and 2 o Table 8. The es ima ed coe icien o cul u al-
cogni i e p essu es is no signi ican ly ela ed o olun a y ca bon disclosu es.
This is consis en wi h Model 2 o Table 8 bu no wi h Model 1. This could be
because o he ac ha Tobi es ima ion assumes ha a single mechanism
de e mines bo h he decision o answe he CDP ques ionnai e and he le el o
quali y o he in o ma ion epo ed (Bou en e al., 2012). Thus, i may be no
app op ia e o use he Tobi model in his case since i con la es wo decisions in
a single measu e. Tha is, he company i s decides whe he o answe he CDP
clima e ques ionnai e.
Second, i he company decides o espond, i mus decide how much in o ma ion
o disclose. Thus, a conce n is ha , in he i s s ep, companies ha choose o
espond o he CDP ques ionnai e a e undamen ally di e en om he i ms ha
do no espond. Thus, in Tobi model, he e may be sel -selec ion bias. This is why
98
a Heckman wo-s age app oach (used in Models 1 and 2) would be a be e
app oach since i allow he ini ial esponse decision o be sepa a e om he
disclosu e quali y decision (Woold idge, 2016). In he Heckman wo-s age model,
sel -selec ion bias is con olled by Lambda a iable ( he in e se Mills a io). Thus,
he inclusion o Lambda a iable allows o make he disclosu e quali y model
condi ional on posi i e pa icipa ion in he CDP ques ionnai e (Rankin e al.,
2011).
Consis en wi h Bou en e al. (2012), his s udy p o ides e idence agains
analysing companies’ decisions o olun a ily disclose ca bon in o ma ion and he
quali y o hei disclosu es oge he . This is in con as o he p io li e a u e on
olun a y ca bon disclosu e which uses a Tobi model in o de o explain bo h
aspec s (e.g. González-González & Zamo a-Ramí ez, 2016b; Guen he , Guen he ,
Schiemann, & Webe , 2016; Ma eo-Má quez e al., 2020).
As his s udy demons a es, di e en de e minan s may be associa ed wi h he
decision o answe he CDP ques ionnai e and he disclosu e le el. Thus, a
Heckman wo-s age model may be a be e app oach since i assumes ha he
decision o disclose and he le el o quali y o he in o ma ion disclosed a e
independen om each o he (Bou en e al., 2012).
Rega ding he componen s o he egula i e pilla o ins i u ions, ou addi ional
analyses we e ca ied ou in o de o examine whe he he esul s o his s udy
a e alid. Fi s , addi ional p obi analyses we e pe o med o examine whe he he
esul s we e sensi i e o he winso isa ion ope a ion. Thus, Models 3 and 4 we e
es ima ed unsing unwinso ised da a. The esul s a e epo ed in Table 15.
In gene al, s a is ic esul s o bo h he esponse decision model (Model 3) and
disclosu e quali y model (Model 4) we e la gely simila o hose p esen ed in
Table 9. The signs and signi icance o bo h independen and con ol a iables
p esen ed in Table 15 a e b oadly simila o hose epo ed in Table 9.
99
Table 15. Robus eg essions using unwinso ised da a (Models 3 and 4)
Model 3 - Response decision
Model 4 - Disclosu e quali y
Va iables
P edic ed
sign
Coe .
z-s a
ME
P edic ed
sign
Coe .
z-
s a
ME
Laws
+
0.01*
1.70
0.01*
+
0.01*
1.92
0.01*
EPSI
+
0.46***
5.20
0.18***
+
0.01
0.13
0.00
Rewa ds
+
0.43***
3.37
0.17***
+
0.37**
2.3
0.14**
Size
0.15***
6.28
0.06***
0.40***
9.75
0.16***
Risk
0.03
0.34
0.01
-0.09
-0.98
-0.03
TobinQ
-0.01
-0.09
0.00
0.00
0.17
0.00
ROA
0.92*
1.74
0.36*
-0.2
-0.3
-0.08
Le
0.10
0.44
0.04
0.58**
2.05
0.23**
DCDP -1
2.39***
28.43
0.75***
0.89
1.43
0.32
Lambda
-
-
-
0.39
0.79
0.15
Cons an
-5.33***
-10.32
-
-7.91***
-5.17
-
Chi-squa e
1,659.44***
245.15***
Log likelihood
-678.56
-633.57
Pseudo R2
0.55
0.162
% Co ec ly p edic ed
88.60%
69.75%
Numbe o obse a ions
2,176
1,091
Con ol o sec o e ec s
yes
yes
No es: *, **, *** ep esen coe icien s signi ican a he 0.1, 0.05 and 0.01 le els
espec i ely ( wo- ailed). ME = Ma ginal e ec s. All a iables a e desc ibed in Table 2.
Second, as in Models 1 and 2, Models 3 and 4 we e es ed using a single dummy
a iable, Ca bonin (ins ead o eigh sec o dummy a iables o con ol o he
sys ema ic indus y di e ences). As men ioned ea lie , Ca bonin a iable equals
one i a company ope a es in Ene gy, Ma e ials o U ili y sec o s and ze o
o he wise (Luo e al., 2012; Tang & Luo, 2011). Thus, a Heckman- wo s age model
was pe o med which included Ca bonin a iable. The esul s o hese models
a e p esen ed in Table 16. The indings a e la gely consis en wi h hose epo ed
in Table 9. The coe icien s o he independen a iables p esen ed simila signs
and signi icance, excep o EPSI a iable which showed a nega i e coe icien .
Howe e , he coe icien o his a iable is no signi ican . Also, he coe icien s o
hese a iables we e consis en wi h hose shown in Table 9. In addi ion, he
100
coe icien s o con ol a iables p esen ed simila signs and signi icance as hose
epo ed in p e ious Table 9, excep o Le , Risk and DCDP -1.
Table 16. Robus eg essions using a single sec o dummy a iable (Models 3 and
4).
Model (3) - Response decision
Model (4) - Disclosu e quali y
Va iables
Coe .
z-s a
ME
Coe icien s
z-s a
ME
Laws
0.01*
1.74
0.01*
0.01*
1.93
0.01*
EPSI
0.47***
5.33
0.18***
-0.04
-0.30
-0.01
Rewa ds
0.39***
3.13
0.15***
0.36**
2.27
0.14**
Size
0.15***
5.93
0.06***
0.38***
8.88
0.15***
Risk
0.13
1.38
0.05
-0.21**
-2.01
-0.08**
TobinQ
-0.01
-0.48
0
0.01
0.28
0
ROA
1.46**
2.19
0.58**
-0.39
-0.44
-0.15
Le
-0.02
-0.10
0
0.65**
2.32
0.26**
DisCDP -1
2.36***
28.60
0.75***
0.56
0.81
0.21
Ca bon-in
0.15
1.63
0.05
0.17*
1.77
0.07*
Lambda
-
-
-
0.11
0.20
0.04
Cons an
-5.32***
-9.99
-
-6.81***
-4.05
-
Chi-squa e
1,633.63***
224.18***
Log likelihood
-691.46
-644.05
Pseudo R2
0.541
0.148
% Co ec ly p edic ed
88.79%
69.11%
Numbe o obse a ions
2,176
1,091
No es: *, **, *** ep esen coe icien s signi ican a he 0.1, 0.05 and 0.01 le els
espec i ely ( wo- ailed). ME = Ma ginal e ec s. All a iables a e desc ibed in Table 2.
Thi d, Model 3 and 4 we e also es ima ed o subsamples o i ms ope a ing in
ca bon-in ensi e and non-ca bon-in ensi e indus ies. Table 17 p esen s he
esul s o eg essions o i ms in ca bon-in ensi e sec o s (Panel A) as well as o
companies ope a ing in non-ca bon-in ensi e indus ies (Panel B). I can be seen
om he da a in Table 17 ha di e en de e minan s in luence he decision o
answe he CDP ques ionnai e and he disclosu e quali y, in bo h i ms in ca bon-
in ensi e and in non-ca bon-in ensi e sec o s. Mo e speci ically, he componen s
o he egula i e pilla a ec olun a y ca bon epo ing o ca bon-in ensi e i ms
and non-ca bon-in ensi e i ms di e en ly.
101
Table 17. Robus eg essions o subsamples o ca bon-in ensi e and non-ca bon-
in ensi e sec o s
Panel A: Subsample o ca bon-in ensi e indus ies
Model (3) - Response decision
Model (4) - Disclosu e quali y
Va iables
Coe .
z-s a
ME
Coe .
z-s a
ME
Laws
-0.01
-0.29
0.01
0.02*
1.89
0.01*
EPSI
0.45***
3.05
0.17***
-0.22
-0.89
-0.09
Rewa ds
0.50*
1.74
0.20*
-0.35
-1.06
-0.14
Size
0.08*
1.95
0.03*
0.4***
5.35
0.16***
Risk
-0.19
-1.18
-0.07
0.09
0.50
0.03
TobinQ
-0.26**
-2.10
-0.10**
0.07
0.46
0.03
ROA
2.01
1.60
0.79
0.4
0.23
0.16
Le
-0.41
-0.79
-0.16
1.71**
2.45
0.68**
DisCDP -1
2.63***
14.79
0.8***
-1.78
-1.24
-0.54
Lambda
-
-
-
-1.82
-1.60
-0.72
Cons an
-3.56***
-3.96
-
-4.85*
-1.78
-
Chi-squa e
462.77***
103.97***
Log likelihood
-163.53
-150.24
Pseudo R2
0.586
0.2571
% Co ec ly p edic ed
90.00%
77.40%
Numbe o obse a ions
570
292
Panel B: Subsample o non-ca bon-in ensi e indus ies
Laws
0.02**
2.13
0**
0.01
1.55
0.01
EPSI
0.48***
4.01
0.19***
0.05
0.32
0.02
Rewa ds
0.40***
2.77
0.16***
0.60***
3.27
0.24***
Size
0.23***
6.53
0.09***
0.47***
8.31
0.18***
Risk
0.10
0.72
0.04
-0.26*
-1.70
-0.10*
TobinQ
0.01
0.24
0.01
0.06
1.16
0.02
ROA
1.42*
1.74
0.56*
-1.11
-0.99
-0.44
Le
0.32
1.12
0.13
0.53
1.56
0.21
DisCDP -1
2.28***
23.13
0.73***
1.6***
2.66
0.5***
Lambda
-
-
-
1.04**
2.08
0.41**
Cons an
-6.79***
-9.53
-
-9.8***
-5.41
-
Chi-squa e
1,220.60***
159.30***
Log likelihood
-502.87
-473.84
Pseudo R2
0.548
0.143
% Co ec ly p edic ed
88.42%
67.83%
Numbe o obse a ions
1,606
799
No es: *, **, *** ep esen coe icien s signi ican a he 0.1, 0.05 and 0.01 le els
espec i ely ( wo- ailed). ME = Ma ginal e ec s. All a iables a e desc ibed in Table 2.
Wi h ega d o companies ope a ing in ca bon-in ensi e sec o s, he EPSI and
Rewa ds a iables posi i ely and signi ican ly a ec he pa icipa ion o

102
companies in he CDP ques ionnai e (see Panel A o Table 17). This indica es ha
bo h ewa ds and moni o ing mechanisms (and sanc ions) posi i ely and
signi ican ly in luence he decision o companies o espond he CDP
ques ionnai e. This esul is consis en wi h Model 3 o Table 9. Fu he mo e, he
sign and coe icien o he EPSI a iable is e y simila o ha p esen ed in Table
13. Howe e , he Laws a iable, which measu es he numbe o clima e change-
ela ed laws, does no signi ican ly in luence he p opensi y o i ms ope a ing in
ca bon-in ensi e sec o s o disclose in o ma ion h ough he CDP ques ionnai e.
Rega ding he quali y o disclosu es, he a iable Laws p esen s a posi i e and
signi ican coe icien (0.02; p < 0.1), which indica es ha laws ela ed o clima e
change posi i ely and signi ican ly a ec he quali y o disclosu es by companies
in ca bon-in ensi e sec o s. Howe e , i s in luence is minimal on he le el o
quali y o he in o ma ion epo ed. EPSI and Rewa ds a iables do no
signi ican ly impac on he disclosu e quali y o i ms in ca bon-in ensi e
indus ies. In sum, he componen s o he egula i e pilla do no appea o exe
a high in luence on he quali y o disclosu es o i ms ope a ing in ca bon-
in ensi e sec o s. The e is a posi i e and signi ican ela ionship be ween all he
componen s o he egula i e pilla and he p opensi y o i ms ope a ing in non-
ca bon-in ensi e indus ies o espond o he CDP ques ionnai e. This esul is
consis en wi h he indings o Model 3 p esen ed in Table 9. Rega ding he
quali y o disclosu es, i is ound ha he ewa d componen o he egula i e
pilla posi i ely and signi ican ly in luences he quali y o disclosu es o i ms in
non-ca bon-in ensi e sec o s. Howe e , nei he Laws no EPSI a iable a ec he
quali y o disclosu es o hese companies. This esul is simila o Model 4
epo ed in Table 9, al hough in his model he Laws a iable is posi i e and
signi ican , howe e i s in luence is minimal on he quali y o he disclosu e.
Fou , an addi ional Tobi model was pe o med in o de o analyse he in luence
o he componen s o egula i e pilla on co po a e olun a y ca bon disclosu e.
103
As men ioned be o e, Tobi model has been widely used in ca bon accoun ing
li e a u e. Al hough i has some d awbacks (men ioned ea lie ), i may be used as
a obus ness o he esul s o his s udy. Table 18 p esen s Tobi eg ession esul s.
Table 18. Tobi eg ession esul s
Va iables
P edic ed
sign
Tobi eg ession
coe icien s
Ma ginal
e ec s on
obse able
a iable, gi en
uncenso ed
Ma ginal
e ec s on
p obabili y o
being
uncenso ed
Laws
+
0.19(0.76)
0.08(2.69)
0.01(2.69)
EPSI
+
16.37***(5.37)
7.26***(7.09)
0.12***(7)
Rewa ds
+
14.78***(3.41)
6.56***(1.27)
0.11***(1.27)
Size
9.48***(10.53)
4.2***(9.35)
0.07***(9.23)
Risk
1.76(0.56)
0.78(0.37)
0.01(0.37)
TobinQ
-0.79(-0.66)
-0.35(-1.14)
-0.01(-1.14)
ROA
13.41**(1.13)
5.94**(0.29)
0.1**(0.29)
Le
3.19(0.39)
1.41(-0.38)
0.02(-0.38)
DCDP -1
104.05***(34.21)
50.68***(34.06)
0.66***(45.26)
Ca bon-in
5.18*(1.71)
2.33*(0.9)
0.03*(0.91)
Cons an
-233.01***(-12.79)
-
-
To al obse a ions
2,176
2,176
2,176
Le -censo ed obs a
cdp=0
1,085
1,085
1,085
Uncenso ed
975
975
975
Righ -censo ed obs a
cdp>=100
116
116
116
Log likelihood
-5,745.92
LR Chi2
1,912.47***
Pseudo R2
0.1427
No es: *** = signi ican p < 0.01, ** = signi ican p < 0.05, * = signi ican p < 0.10.
Coe icien s o he Tobi eg ession a e es ima ed by maximum likelihood me hod. T-
alues (Tobi eg ession coe icien s) and z-s a is ics (ma ginal e ec s) a e epo ed in
pa en heses. Obs = obse a ions. All a iables a e desc ibed in Table 2.
The dependen a iable in his Tobi model equals o he sco e ob ained in he
CDP ques ionnai e i he company in ques ion answe ed he ques ionnai e and
made public he sco e and ze o o he wise i.e. i a company ha did no espond
o he ques ionnai e, declined o pa icipa e, o did no publish he ques ionnai e.
As shown in Table 18, he esul s ob ained we e simila o hose p esen ed in
104
Table 9. The signi icance and he signs o he a iables a e simila o hose shown
in Model 3 o Table 9. Simila ly, he coe icien s do no p esen signi ican alue
a ia ions. These esul s se e o ein o ce he indings o his s udy, and o
con i m he ela ionship be ween he componen s o coun ies’ egula i e con ex
and olun a y ca bon epo ing on he pa o companies headqua e ed in hose
coun ies. In addi ion, he da a was analysed using o dina y leas squa es
eg ession. The esul s (no abula ed) a e quali a i ely simila and do no change
he in e ences o he s udy.
Chap e 5. Conclusions
Key indings and main con ibu ions o
he hesis, implica ions o p ac ice,
limi a ions, and u u e esea ch
di ec ions
112
policymake s should ca y ou mo e ac ions o encou age i ms o educe hei
ca bon oo p in as well as o make ca bon disclosu e as a s a egic p io i y. These
ac ions could include he implemen a ion o s ic e egula ion along wi h a mo e
e ec i e moni o ing mechanism o achie e he goal (Luo e al., 2012).
Thi dly, in es o s, sha eholde s and o he s akeholde s can bene i om his
esea ch as i demons a es which clima e change- ela ed ins i u ional con ex
exe s mo e p essu e on companies o olun a ily disclose ca bon in o ma ion, as
i ms in hose coun ies will be mo e likely o pa icipa e in he CDP clima e
p og am, as well as o disclose high-quali y ca bon in o ma ion. This will help
hem o de elop coun y-speci ic disclosu e s a egies and in es men plans. The
esul s o his hesis a e o use o in es o s and o he s akeholde s so as o ind
ou whe he a company is managing he isks o clima e change well, gi en he
cha ac e is ics o he con ex in which i ope a es, and hus be e assess whe he
i may cons i u e a good in es men oppo uni y. In addi ion, he indings o his
hesis a e o use o non-go e nmen al o ganisa ions and o he ac i is s so as o
analyse o wha ex en he company is con ibu ing o he igh agains clima e
change and o be able o sc u inise hei clima e ac ions, conside ing he
ins i u ional cha ac e is ics o he coun y in which he company ope a es.
Finally, his hesis p o ides schola s and p ac i ione s speci ic clima e- ela ed
measu es o he h ee dimensions o ins i u ions as well as o he componen s
o he egula i e pilla , and helps hem o accumula e and apply knowledge
ega ding he de elopmen o he NIS pe spec i e in he s udy o olun a y
co po a e ca bon disclosu e.
5.3. Limi a ions and u u e esea ch di ec ions
This esea ch is subjec o ce ain limi a ions. Fi s o all, i only conside ed
coun ies’ ins i u ional p o iles ela ed o clima e change, hus cau ion should be
exe cised when gene alizing he indings o o he ins i u ional p o iles ela ed o

113
o he en i onmen al issues (Kos o a, 1997). Second, he s udy pe iod was
ela i ely sho compa ed wi h p e ious s udies on olun a y ca bon disclosu es
(Lemma e al., 2019; Liesen e al., 2015; Luo, 2019; S anny, 2013); howe e , he
mul ina ional design, wi h 13 coun ies including 2,327 companies ope a ing in
di e en sec o s, helped compensa e o his limi a ion. In his ega d, a u he
s udy could assess bo h he e ec s o coun ies’ ins i u ional con ex and he
componen s o he egula i e dimension o ins i u ions using mo e yea s o da a
in he analysis. Thi d, i examined coun y-le el ins i u ional ac o s ela ed o
clima e change sepa a ely, hus i would be in e es ing o in es iga e he way in
which he in e ac ion be ween o mal and in o mal ins i u ions a ec s co po a e
ca bon epo ing. In his sense, i would be also in e es ing analysing he in e play
be ween each ins i u ional pilla and/o he componen s o he egula i e
dimension o ins i u ions. Fu he mo e, in ela ion o no ma i e p essu es,
companies may adop olun a y ini ia i es such as he TCFD (Task Fo ce on
Clima e-Rela ed Financial Disclosu es) guidelines (TCFD, 2019) no because hey
a e imposed by egula ions, bu a he because hey belie e i is mo ally he igh
hing o do (Sco , 2014). Hence u he esea ch could explo e how no ma i e
p essu es may a ec olun a y co po a e ca bon disclosu e.
In ecen yea s, many companies a e engaging in g eenwashing, misleading hei
s akeholde s ega ding hei ca bon pe o mance and/o he en i onmen al
ad an ages o hei p oduc s o se ices (Delmas & Bu bano, 2011). The
Volkswagen emissions scandal in Sep embe 2015 is a clea example o co po a e
g eenwashing beha iou (Siano e al., 2017; Yang e al., 2018). Be o e he scandal,
he i m claimed ha i s ehicles had an injec ion sys em ha educes emissions.
These low-emissions ca s allowed he i m o ob ain some ewa ds, such as g een
ca subsidies and ax exemp ions in he USA (Yang e al., 2018). Howe e , he
En i onmen al P o ec ion Agency (EPA) ound ha many Volkswagen ca s being
sold in he USA we e chea ing emission es s by using a “de ea de ice” (Ho en,
114
2015). Acco ding o Delmas and Bu bano (2011), co po a e g eenwashing
beha iou may be in luenced by he ins i u ional con ex in which he i m
ope a es. In his line, u he esea ch may explo e he ole o ins i u ional pilla s
on co po a e g eenwashing beha iou .
Clima e change is seen by many s akeholde s as a signi ican isk o companies,
pa icula ly in indus ies such as oil and gas. Howe e , he lack o disclosu e and
he inabili y o de e mine company clima e isk con inues o be a conce n o such
g oups as BlackRock and The Vangua d G oup (Chasan & Massa, 2019; Mooney,
2020). Howe e , con adic ions a e also appa en since hese big in es o s g oups
such as BlackRock and The Vangua d G oup showed hin suppo o clima e-
ela ed sha eholde p oposals (Chasan & Massa, 2019). Acco ding o a su ey
ca ied ou by E ns and Young, clima e change is among he mos common
opics eques ed by s ockholde s (E ns and Young, 2014). In es o s inc easingly
use sha eholde esolu ions as a mean o elici g ea e clima e change- ela ed
disclosu es and o induce companies o manage be e he challenges and
oppo uni ies ha a ise om clima e change. Based on ins i u ional heo y, i
would be in e es ing how ins i u ional ac o s a ec clima e- ela ed sha eholde
esolu ions. This leads o he ollowing ques ions: Do ins i u ional ac o s
in luence i ms o be a ge ed by clima e- ela ed sha eholde esolu ions? Do
ins i u ional ac o s help de e mine whe he clima e- ela ed esolu ions will be
p oposed, o ed on and adop ed? As clima e- ela ed sha eholde esolu ions a e
inc easing o e ime i would be in e es ing o analyse whe he he ins i u ional
p o ile o coun ies a ec he ou come hese ypes o sha eholde esolu ions.
Re e ences
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Appendix A – Example o o e all disclose s in I aly
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Lis o non- esponding companies in I aly. Sou ce: CDP Clima e Change Repo
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Appendix B – Lis o publica ions
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Q1 (68/355). SJR index: 0.672.
Ma eo-Má quez, A.J., González-González, J.M. & Zamo a-Ramí ez, C. (2020).
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