Song, Xiaoni; Fang, Tong
A icle
Clima e change and he in luence o mone a y policy in
China
Jou nal o Applied Economics
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Clima e change and he influence o mone a y policy in
China
Xiaoni Song & Tong Fang
To ci e his a icle: Xiaoni Song & Tong Fang (2024) Clima e change and he influence
o mone a y policy in China, Jou nal o Applied Economics, 27:1, 2329840, DOI:
10.1080/15140326.2024.2329840
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© 2024 The Au ho (s). Published by In o ma
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RESEARCH ARTICLE
Clima e change and he in luence o mone a y policy in China
Xiaoni Song and Tong Fang
School o Economics, Shandong Uni e si y, Jinan, China
ABSTRACT
We in es iga e whe he clima e change a ec s he e iciency o
mone a y policy. We use empe a u e shocks, calcula ed as em-
pe a u e de ia ions om his o ical a e age empe a u es, o p oxy
clima e change, and u ilize a h eshold ec o au o eg ession
model (TVAR) o es ima e he impac o expansiona y and igh
mone a y shocks on economic ou pu unde high and low egimes
o empe a u e shocks. Ou esul s cha ac e ize a clima e change
egime-dependen mone a y policy. Expansiona y mone a y policy
is less e icien and he nega i e impac o igh mone a y policy is
enhanced, when clima e change is se e e. The esul s can be
explained by he clima e-induced c edi cons ain o comme cial
banks. Highe empe a u e shocks lead o inc eases in banks’ non-
pe o ming loan a ios, which esul s in la ge c edi cons ain s o
banks. Banks end o be mo e p uden in c edi expansion, and he
bank c edi channel o mone a y policy ansmissions is weakened.
ARTICLE HISTORY
Recei ed 14 Augus 2023
Accep ed 5 Ma ch 2024
KEYWORDS
Clima e change; mone a y
policy; egime dependency;
bank c edi channel
1. In oduc ion
Does clima e change a ec he e iciency o mone a y policy? This is an impo an
ques ion in clima e inance. Mone a y policy is ega ded as one o he mos impo an
ools o mi iga e economic ine iciency caused by clima e change (Ba anzini e al., 2017;
Maes e-And és e al., 2019). Answe ing his ques ion enhances he unde s anding o he
economic and inancial consequences o clima e change and p o ides use ul guidance o
clima e-based mone a y policy o mula ions (L. P. Hansen, 2022).
Theo e ically, expansiona y mone a y policy should be less e icien when clima e
change is se e e. Clima e change has been ecognized wo ldwide as a new and non-
negligible sou ce o economic and inancial ins abili y (Da e mos e al., 2018; Giglio e al.,
2021). Clima e change and i s induced na u al disas e s ha e nega i e shocks o eco-
nomic ac i i ies and lead o he de alua ion o colla e als (Bu ke e al., 2015; Dell e al.,
2009, 2012; Le a & Tol, 2019). Because clima e change a ec s economic ac i i ies and he
alua ion o colla e als, he deb paymen s o co po a ions and households will de e io-
a e (Da e mos e al., 2018; Hosono e al., 2016; Klomp, 2014). The a io o non-
pe o ming loans o comme cial banks will inc ease, exposing banks o g ea e c edi
cons ain s and making banks mo e p uden o expand c edi (Abbas e al., 2021; Abou-
CONTACT Tong Fang [email p o ec ed] School o Economics, Shandong Uni e si y, 27 Shanda Nanlu,
Jinan 250100, China
JOURNAL OF APPLIED ECONOMICS
2024, VOL. 27, NO. 1, 2329840
h ps://doi.o g/10.1080/15140326.2024.2329840
© 2024 The Au ho (s). Published by In o ma UK Limi ed, ading as Taylo & F ancis G oup.
This is an Open Access a icle dis ibu ed unde he e ms o he C ea i e Commons A ibu ion-NonComme cial License (h p://
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Manusc ip in a eposi o y by he au ho (s) o wi h hei consen .
El-Sood, 2016; Pool e al., 2015). The c edi channel o mone a y policy ansmissions is
weakened by clima e change, and hus, expansiona y mone a y policy is less in luen ial
when clima e change is se e e. Simila ly, he nega i e impac o igh mone a y policy on
economic ou pu should be exempli ied when clima e change is se e e. Cen al bank
should ca e o he clima e change p oac i ely by using he adi ional mone a y policy
(C. Chen e al., 2021). Al hough hese heo ies a e well unde s ood, he empi ical
e idence is s ill limi ed. He e a e he esea ch ques ions: Is he in luence o mone a y
policy a ec ed by clima e change? How o desc ibe he impac o clima e change on he
in luence o mone a y policy? How o explain he impac o clima e change on he
in luence o mone a y policy?
In his pape , we p o ide empi ical e idence on he impac o clima e change on
mone a y policy by examining he esponses o economic ou pu o mone a y shocks
unde clima e change egimes. We use empe a u e shocks, which a e calcula ed as
empe a u e de ia ions om his o ical a e age empe a u es, o p oxy clima e change
(Hong e al., 2019; Russell e al., 2014; Song & Fang, 2023). In he empi ical analysis, we
u ilize a TVAR amewo k o e eal he impac o mone a y policy on ou pu , and he
empe a u e shock is ega ded as he h eshold a iable. Mone a y policy (M2), ou pu
(GDP), and p ice le el (CPI) a e included as o he endogenous a iables. We pe o m
one-s anda d-de ia ion unan icipa ed posi i e and nega i e mone a y shocks sepa a ely
and es ima e he gene alized impulse esponses o ou pu o shocks unde high- and low-
empe a u e egimes. Mo eo e , we explain ou esul s h ough clima e-induced c edi
cons ain s in he bank c edi channel.
Ou esul s a e summa ized as ollows. Fi s , we ind ha he in luence o mone a y
policy is clima e change egime dependen . Tempe a u e shocks weaken he e iciency o
mone a y expansions and wo sen he e ec s o mone a y igh ening. Second, p e ious
li e a u e shows ha he e ec s o expansiona y and igh mone a y policies a e asym-
me ic, and we show ha asymme ies a e mo e appa en unde high- empe a u e
egimes. Thi d, we p o ide empi ical e idence o explain he pa h h ough which clima e
change a ec s he e ec i eness o mone a y policy by con i ming he c edi cons ain s o
comme cial banks. Tempe a u e shocks wo sen economic ac i i ies, which inc eases
co po a ions’ and households’ loan de aul a es and banks’ non-pe o ming loan a ios.
Consequen ly, banks ha e g ea e c edi cons ain s and a e p uden in expanding c edi
unde high- empe a u e egimes, sugges ing ha he c edi channel o mone a y policy is
weakened.
Ou s udy con ibu es o he li e a u e on he ole o clima e change in mone a y
policy implemen a ion. We p o ide empi ical e idence ha he e iciency o mone a y
policy is in luenced by clima e change, cha ac e ized as clima e change egime depen-
dency, which is meaning ul o mone a y policy in he con ex o clima e change (C. Chen
e al., 2021; Geo ge & Anas asios, 2018). Ou esul s con i m he heo e ical indings o
C. Chen e al. (2021) and sugges ha policymake s should conside clima e change in
mone a y policy o mula ions p oac i ely. Expansiona y mone a y policy could be mo e
agg essi e when empe a u es a e highe , and igh mone a y policy could be mo e
conse a i e when clima e change is se e e.
Ou pape is also ela ed o he ich li e a u e on he ansmission o mone a y policy
shocks. We ind ha clima e change dynamics a ec mone a y policy e iciency (Aas ei
e al., 2017; Boi in & Giannoni, 2006; Caggiano e al., 2014). Speci ically, we empi ically
2X. SONG AND T. FANG
con i m he clima e-induced c edi cons ain o comme cial banks, which has only been
in es iga ed in heo y (Be g & Sch ade , 2012; Hosono e al., 2016). Inc eases in em-
pe a u e lead o highe bank non-pe o ming loan a ios and g ea e c edi cons ain s,
which make banks mo e p uden in ex ending c edi . The c edi cons ain weakens he
bank c edi channel o mone a y policy and hus p o ides an explana ion o he impac
o clima e change on he in luences o mone a y policies om a mic o pe spec i e.
The emainde o ou pape is o ganized as ollows. Sec ion 2 p esen s a li e a u e
e iew. Sec ion 3 desc ibes he TVAR model and he da a. Sec ion 4 epo s ou main
esul s and obus ness checks. Sec ion 5 explains ou esul s, and Sec ion 6 concludes.
2. Li e a u e e iew and heo e ical analysis
2.1. Li e a u e e iew
Mone a y policy plays an impo an ole in adap ing o clima e- ela ed isks, and cen al
banks should inco po a e clima e isks in o hei policy amewo ks o main ain eco-
nomic and inancial s abili y (Dikau & Volz, 2021). Mos o he p e ious li e a u e
explo es how cen al banks use mone a y policy o suppo he ansi ion o a low-
ca bon economy.
On he one hand, mone a y policy ac s as a complemen a y ins umen o clima e
policy o cope wi h clima e isks. I is no su icien o ealize he desi ed objec i es by
only implemen ing clima e policies, and i is o g ea impo ance o conside addi ional
policy ins umen s (Ba anzini e al., 2017; Campiglio, 2016; Engle e al., 2018; Maes e-
And és e al., 2019; Rozenbe g e al., 2013). Mone a y policy is ega ded as an app op ia e
ool o add ess such p oblems (Benmi & Roman, 2020; Chan, 2020). Annicchia ico and
Dio (2017) u ilize an ex ended en i onmen al dynamic s ochas ic gene al equilib ium
model o e eal ha cen al banks should in eg a e clima e change in o mone a y policy.
Chan (2020) in es iga es he in e ac ions among clima e change, mone a y and iscal
policies using an en i onmen al DSGE model and demons a e ha ca bon axa ion
should complemen mone a y policy bu no espond o iscal policy, in which ca bon
emissions and household wel a e can be main ained as dynamically s able. Mo eo e ,
because o ma ke ailu es, Campiglio (2016) inds ha ca bon p icing i sel is no
e ec i e enough o enhance banking c edi o low-ca bon sec o s, whe eas mone a y
policy con ibu es o a enua ing he cons ain s o bank lending, especially in eme ging
economies, o which cen al banks ake powe ul con ols on c edi alloca ion and use
mo e mone a y policy ins umen s.
On he o he hand, some s udies p opose new clima e- ela ed mone a y policy
ins umen s ha in eg a e clima e objec i es in o mone a y policy and e alua e hei
e ec s. Böse and Senni (2020) p opose clima e-o ien ed mone a y policy ins umen s,
including g een quan i a i e easing, g een ese e equi emen s and a g een colla e al
amewo k. They ind ha hese ins umen s can make i ms adop cleane echnologies
ac oss he en i e economy, educe ca bon dioxide emissions and mi iga e clima e
damage. Bone a e al. (2022) inco po a e clima e change objec i es in o mone a y policy
and discuss a couple o ac ions cen al banks can ake o mi iga e clima e change.
McConnell e al. (2022) in es iga e se e al g een mone a y policy ins umen s and
JOURNAL OF APPLIED ECONOMICS 3
poin ou ha i is he mos p omising condui o inco po a e b own colla e al hai cu s
in o he colla e alized lending amewo k o cen al banks.
Howe e , s udies on he e ec s o clima e change on mone a y policy a e cu en ly
qui e limi ed. Geo ge and Anas asios (2018) use an indi ec me hod o assess he
impac s o clima e change on mone a y policy. They inco po a e o al ac o p oduc-
i i y (TFP) shocks de i ed om clima e change unce ain y in o he in eg a ed
assessmen model (IAM) and hen sugges ha clima e change unce ain y leads o
la ge and mo e pe sis en luc ua ions in economic ac i i y. By embodying mo e
no el en i onmen al ea u es, such as he concealed emissions and po en ial penal ies,
and clima e policy in he E-DSGE model, C. Chen e al. (2021) ind ha clima e
policy is a ac o a ec ing p ice le el and wel a e. McKibbin e al. (2021) poin ou
ha supply shocks om clima e change dis up cen al banks’ abili y o o ecas and
manage in la ion and highligh ha clima e isks in he mone a y amewo k will
make mone a y policy mo e e ec i e.
In summa y, one s eam o li e a u e mainly ocuses on mone a y policy as
a complemen a y ins umen o clima e policy o mi iga e clima e isks and ocuses
on inco po a ing clima e objec i es in o mone a y policy. Ano he s eam o li e a u e
highligh s he ole o mone a y policy in dealing wi h clima e isks and achie ing
sus ainable de elopmen . Howe e , he e a e se e al issues o be add essed. Fi s ,
a la ge numbe o p e ious s udies sugges ha clima e change can impai ag icul u al
yields, indus ial ou pu , and economic g ow h, which a e p ima y mone a y policy
conce ns. Howe e , ew s udies ha e simul aneously conside ed clima e change, mone-
a y policy, and ou pu in a amewo k o discuss he e ec i eness o mone a y policy in
he con ex o clima e change (Ca le on & Hsiang, 2016; Dell e al., 2012). Second, hough
many s udies p o ide heo ies ha ela e clima e change o mone a y policy, he numbe
o empi ical examina ions is qui e limi ed. E alua ing he eac ions o mone a y policy o
clima e shocks is bene icial o mo e explici ly ecognize clima e isks, which is he
o emos p e equisi e o cen al banks o c a op imal mone a y policy o a ain objec-
i es. These s udies also p o ide e idence o how cen al banks should adjus mone a y
policy condi ional on clima e change and new pe spec i es o adap ing o clima e isks
in mone a y policy ansmission. Thi d, p e ious s udies ha e no shown empi ical
e idence on he in luen ial mechanism ha explains how clima e change a ec s he
e iciency o mone a y policy. China has a bank-based inancial sys em, which implies
ha he bank c edi channel is impo an in mone a y policy ansmission. I clima e
change a ec s he bank c edi channel, hen i will de ini ely a ec he in luence o
mone a y policy. In he ollowing analyses, we aim o add ess hese issues using TVAR
models and bank-le el mic o da a.
2.2. Theo e ical analysis and hypo hesis de elopmen
The e ec i e implemen a ion o mone a y policy depends no only on he e o s o
he go e nmen , which o mula es easible mone a y policy, bu also, mo e impo -
an ly, on he ansmission o mone a y policy (Acha ya e al., 2020; K. Chen e al.,
2018). Fo example, when banks ace s ic egula o y and inancial cons ain s o a e
unde capi alized, he ansmission channel o mone a y policy may be hampe ed,
leading o less e ec i e expansi e mone a y policy (Acha ya e al., 2020). Meanwhile,
4X. SONG AND T. FANG
he ansmission channel mainly elies on inancial ins i u ions o he inancial
ma ke , and clima e change is ega ded as a new sou ce o inancial ins abili y
(Giglio e al., 2021; Mishkin, 1996, 2001; Walsh, 2017). The e o e, i is concei able
o us o explo e he pa h h ough which clima e change a ec s mone a y policy om
he iew o ansmission channels.
We ocus on e idence om China. Despi e being he la ges de eloping economy, i s
inancial ma ke is no well de eloped and i s inancial sys em p ima ily elies on he
banking sys em (Hou e al., 2018; Klingelhö e & Sun, 2019; H. Li e al., 2021). The e a e
se e al o he ansmission channels o mone a y policy, such as he in e es a e channel,
asse p ice channel, and exchange a e channel (Be nanke & Blinde , 1992; Kashyap &
S ein, 1995; Mishkin, 1996, 2001; Walsh, 2017). In China, he (bank) c edi channel
domina es all o he mone a y policy ansmission channels, whose con ibu ions a e
ela i ely limi ed. In his ega d, we analyze he ole o banks in explaining he impac o
clima e change on he ansmission o mone a y policy.
Acco ding o he li e a u e, empe a u e shocks ha e nega i e consequences o
economic ac i i ies and human beha io . High empe a u es lead o dec eases in
i m p o i abili y h ough labo p oduc i i y, o al ac o p oduc i i y (TFP), and
ope a ion cos s. Fi s , empe a u e is nega i ely associa ed wi h labo p oduc i i y.
High empe a u es end o ha m human physiological unc ions, cogni i e capaci-
ies, and psychological heal h, esul ing in losses in labo p oduc i i y (S. Chen
e al., 2018; Deschênes & Mo e i, 2009; Hsiang, 2010; Seppänen e al., 2006;
Somana han e al., 2021; Zheng e al., 2019; Zi in e al., 2015). Second, high
empe a u es educe TFP g ow h. Donadelli e al. (2017) indica e ha empe a u e
isk has a long-las ing nega i e e ec on TFP, which wo sens he wel a e cos .
Thi d, empe a u e shocks inc ease en e p ise ope a ion cos s. Pank a z e al. (2023)
documen ha adminis a i e, selling, and gene al expenses inc ease when i ms a e
exposed o high empe a u es o e p olonged pe iods o ime, leading o ising
ope a ion cos s.
Based on he ela ionship be ween empe a u e shocks and i m p o i abili y,
empe a u e shocks end o exace ba e he c edi channel. High empe a u es weaken
i m p o i abili y, which de e io a es he i m balance shee and i ms’ abili y o epay
loans om banks. The non-pe o ming loan a ios o banks co espondingly inc ease,
causing banks o su e capi al losses and o educe lending o main ain he egula o y
capi al a io (Abbas e al., 2021; Abou-El-Sood, 2016; Sand a e al., 2016). In addi ion,
inc eases in he non-pe o ming loan a ios could signal an economic down u n, and
comme cial banks could become mo e p uden and conse a i e in lending o i ms
(Kollmann e al., 2010; Pool e al., 2015). Weakened i m p o i abili y is also closely
ela ed o g ea e ad e se selec ion and mo al haza d, which hinde s banks om
lending o hese i ms.
O e all, high empe a u es weaken i m p o i abili y and inc ease he de aul p ob-
abili ies o bank loans, hus esul ing in highe bank non-pe o ming loan a ios. These
consequences make banks mo e isk a e se and p uden in expanding c edi and dampen
he e iciency o mone a y policies. This is he clima e-induced c edi cons ain , as
heo e ically s a ed by Be g and Sch ade (2012) and Hosono e al. (2016). The e o e,
we p opose he ollowing hypo heses:
JOURNAL OF APPLIED ECONOMICS 5
H1: When clima e change is se e e, he posi i e impac o expansiona y mone a y
policy is less e icien and he nega i e impac o igh mone a y policy is enhanced.
H2: Clima e change a ec s he e iciency o mone a y policy by weakening he bank
c edi channel o mone a y policy ansmission.
3. Econome ic model and da a desc ip ions
3.1. TVAR model speci ica ion
We employ a TVAR model o examine he egime-dependen nonlinea e ec s o
mone a y policy in he con ex o clima e change (Lo & Zi o , 2001; Tsay, 1998). The
TVAR model has he ollowing cha ac e is ics and ad an ages. Fi s , he TVAR model is
a nonlinea mul i a ia e sys em wi h egime swi ching. We can ans o m he model in o
se e al dis inc linea VARs. These VARs co espond o se e al egimes based on he
h eshold a iable (in his pape , he h eshold is he clima e change a iable), and he
coe icien s o VAR models a e unique o each egime. Second, he TVAR model allows
he h eshold a iable o be endogenous. This indica es ha he egime can swi ch a e
shocks occu (A onso e al., 2018; Balke, 2000; Fe a esi e al., 2015; Jö g, 2020)
To in es iga e whe he clima e change a ec s he in luence o mone a y policy, we use
empe a u e shocks as a p oxy o clima e change and as he h eshold a iable. The
model wi h wo egimes is speci ied as ollows:
Y ¼A1þϕ1Lð ÞY þA2þϕ2Lð ÞY
ð ÞIy�
d>γ
�þε ;(1)
whe e Y is he ec o o all endogenous a iables, including empe a u e shocks,
mone a y policy, p ice le el, and ou pu . In his pape , and he sample pe iod is om
2004Q2 o 2021Q4 (addi ional in o ma ion on hese a iables can be ound in
Sec ion 3.2).
1
We se he o de ing as empe a u e shocks, mone a y policy, p ice le el,
and ou pu in he baseline TVAR model. The o de ing in he ec o o endogenous
a iables e lec s he way in which a iables in e ac .
2
y�
d is he h eshold a iable o
empe a u e shocks a ime d, and d is he lag leng h. I is an indica o unc ion ha
equals 1 when y�
d is mo e han h eshold γ and 0 o he wise. This se ing means ha
egime swi ching occu s a ime i he h eshold a iable a ime d exceeds γ. A1 and
A2 a e he ec o s o he cons an e m. ϕ1Lð Þand ϕ2Lð Þa e lag polynomial ma ices. ε is
he ec o o s uc u al shocks.
To e eal he esponses o endogenous a iables o shocks, we employ he gene alized
impulse esponse unc ion (GIRF) o compu e impulse esponses (Koop e al., 1996).
Mo eo e , he GIRF allows us o in es iga e he e ec s o shocks o dis inc di ec ions
and sizes. The GIRF is desc ibed as ollows:
1
Since he main objec i es o People’s Bank o China implemen ing mone a y policy a e main aining p ice s abili y and
s imula ing economic g ow h, we selec h ee o he a iables, mone a y policy, p ice le el and ou pu , in baseline TVAR
model (H. Chen e al., 2017; K. Chen e al., 2018).
2
Fo example, empe a u e shocks a e o de ed i s , indica ing ha empe a u e shocks do no con empo aneously eac
o all o he a iables. Ou pu is o de ed las , indica ing ha ou pu con empo aneously eac s o all o he a iables. We
also use o he o de ings in obus ness checks.
6X. SONG AND T. FANG
GIRFyh;Ω 1;u
ð Þ ¼ E y þhjΩ 1;u
½ � E y þhjΩ 1
½ �;(2)
whe e y is he esponse a iable, h is he ho izon, Ω 1 deno es his o ical in o ma ion and
u is he shock. The esponse o a iable y a ho izon h is ha he expec a ion o a iable y
a pe iod þh imposing shock u condi ional on his o y Ω 1 deduc s he expec a ion o
a iable y a pe iod þh wi hou shock u condi ional on his o y Ω 1, which is
calcula ed unde he amewo k o linea VAR. In ega d o he TVAR model, i is
necessa y o calcula e impulse esponses o each egime.
3.2. Da a desc ip ions
3.2.1. Tempe a u e da a
Following p e ious s udies, empe a u e shocks a e calcula ed as empe a u e de ia ions
om his o ical a e age empe a u es, which indica e he end o global wa ming and
unan icipa ed empe a u e changes (Hong e al., 2019; Song & Fang, 2023). The em-
pe a u e da a o China a e ob ained om he Na ional Cen e s o En i onmen al
In o ma ion (NCEI) o he Na ional Oceanic and A mosphe ic Adminis a ion
(NOAA). The NCEI da abase o NOAA p o ides wea he da a moni o ed by e e y
me eo ological s a ion a ound he wo ld. To calcula e empe a u e shocks in China, we
ollow h ee s eps. Fi s , we selec s a ions loca ed in China. Because missing da a a each
s a ion a e a challenge o calcula ing a e age empe a u es, we emo e s a ions ha
eco ded da a o ewe han 350 days pe yea , esul ing in a emaining 260 s a ions.
Nex , we collec empe a u e da a om hese s a ions and ake he qua e ly a e age o
he daily empe a u es a each s a ion. Finally, we compu e he qua e ly a e age em-
pe a u es o hese s a ions (Tempe a u e ) and emo e he end by sub ac ing he
his o ical H-yea a e ages om qua e ly a e age empe a u es:
emp ¼Tempe a u e 1
HXH
j¼1Tempe a u e 4�j:(3)
The his o ical a e age empe a u es a e compu ed using H = 30/25/20-yea mo ing
a e ages (M. E. Kahn e al., 2021). Figu e 1 plo s he empe a u e shocks in China. We
ind ha mos empe a u e shocks a e g ea e han ze o, which e lec s a end o global
wa ming. Tempe a u e shocks ha e la ge luc ua ions in he i s and ou h qua e s,
which is consis en wi h he indings o Dell e al. (2012) and S. Kahn e al. (2019). The
sample pe iod spans om 2004Q2 o 2021Q4.
3.2.2. Mac oeconomic da a
The money s ock and in e bank-o e ed a e a e usually employed as p oxies o mone a y
policy. The in e bank-o e ed a e has he d awbacks o o wa d-looking expec a ions
and inconsis en mo emen wi h mone a y policy, while money s ocks p edominan ly
ac as he in e media e a ge o mone a y policy in China (K. Chen e al., 2018; He yan &
Tze emes, 2017; R. Li & Tian, 2018).
3
To p oxy mone a y policy, we use he mone a y
s ock g ow h a e, which is calcula ed as he yea -on-yea g ow h a e o money s ocks
3
The Cen al Economic Wo k Con e ence in China decides on he M2 g ow h a ge o he nex coming yea a he end o
his yea . In he nex yea , he go e nmen adjus s he ac ual M2 g ow h om qua e o qua e subjec o he annual
M2 g ow h a ge . M2 g ow h also se es mainly o he economic g ow h (K. Chen e al., 2018).
JOURNAL OF APPLIED ECONOMICS 7
he iscal policy a iable ( p) in he endogenous a iable ec o . Fiscal policy is p oxied by
he yea -on-yea g ow h a e o public expendi u e, which is collec ed om he Na ional
Bu eau o S a is ics o China. The endogenous a iable ec o
becomes Y¼ emp H;M2; p;CPI;GDP½ �.
Figu e 7 shows he impulse esponses o mone a y policy con olling he e ec s o
iscal policy. Unsu p isingly, he esponses o ou pu o mone a y shocks become weake .
Fo example, he ou pu inc eases by a mos app oxima ely 0.3% unde he low egime,
and he esponse is 0.2% unde he high egime o empe a u e shocks wi h 20-yea
his o ical a e ages a e a one-s anda d-de ia ion posi i e mone a y shock. The e i-
ciency o mone a y policy is s ill lowe when he wea he is ho e .
Figu e 5. Impulse esponses o he TVAR model wi h Y=[CPI, GDP, emp _H, M2]. This igu e shows
he impulse esponses o mone a y policy shocks o ou pu unde high- and low- empe a u e shocks.
Tempe a u e shocks a e emp _30, emp _25 and emp _20, which e lec qua e ly empe a u es
ha de ia e om his o ical 30/25/20-yea mo ing a e ages. The i s column shows impulse
esponses o ou pu o one-s anda d-de ia ion posi i e mone a y policy shocks, and he second
column shows impulse esponses o ou pu o one-s anda d-de ia ion nega i e mone a y policy
shocks. Solid lines deno e he impulse esponses unde low- empe a u e shocks, and dashed lines
a e unde high egimes o empe a u e shocks.
14 X. SONG AND T. FANG
4.3.3. Al e na i e a iables o ou pu
We use an al e na i e indica o o ou pu . We eplace he g ow h a e o GDP wi h he
g ow h a e o indus ial p oduc ion and e-es ima e he TVAR model. The esul s in
Figu e 8 show ha using an al e na i e p oxy o ou pu does no a ec ou empi ical
esul s. In addi ion, we use mon hly da a o ano he obus ness check o alle ia e he
bias om using a ela i ely small sample size o qua e ly da a. The impulse esponse
esul s a e shown in Figu e 9. Wi h a la ge sample size, he esul s a e consis en wi h he
baseline esul s.
Figu e 6. Impulse esponses o he TVAR model wi h Y=[CPI, GDP, M2, emp _H]. This igu e shows
he impulse esponses o mone a y policy shocks o ou pu unde high- and low- empe a u e shocks.
Tempe a u e shocks a e emp _30, emp _25 and emp _20, which e lec qua e ly empe a u es
ha de ia e om his o ical 30/25/20-yea mo ing a e ages. The i s column shows impulse
esponses o ou pu o one-s anda d-de ia ion posi i e mone a y policy shocks, and he second
column shows impulse esponses o ou pu o one-s anda d-de ia ion nega i e mone a y policy
shocks. Solid lines deno e he impulse esponses unde low- empe a u e shocks, and dashed lines
a e unde high egimes o empe a u e shocks.
JOURNAL OF APPLIED ECONOMICS 15
5. In luen ial mechanism: he clima e-induced c edi cons ain
Hypo hesis H2 indica es ha high empe a u es weaken i m p o i abili y, inc ease he
de aul p obabili ies o bank loans, and esul in highe bank non-pe o ming loan a ios.
Comme cial banks will be mo e isk a e se and p uden in expanding c edi , which
weakens he e iciency o mone a y policies. This clima e-induced c edi cons ain is
used o explain he impac o clima e change on mone a y policy e iciency (Be g &
Sch ade , 2012; Hosono e al., 2016). To empi ically examine he clima e-induced c edi
cons ain , we employ Equa ions (4)–(6).
Figu e 7. Impulse esponses o he TVAR model wi h Y = [ emp _H, M2, p, CPI, GDP]. This igu e
shows he impulse esponses o mone a y policy shocks o ou pu unde high- and low- empe a u e
shocks. Tempe a u e shocks a e emp _30, emp _25 and emp _20, which e lec qua e ly em-
pe a u es ha de ia e om his o ical 30/25/20-yea mo ing a e ages. The i s column shows impulse
esponses o ou pu o one-s anda d-de ia ion posi i e mone a y policy shocks, and he second
column shows impulse esponses o ou pu o one-s anda d-de ia ion nega i e mone a y policy
shocks. Solid lines deno e he impulse esponses unde low- empe a u e shocks, and dashed lines
a e unde high egimes o empe a u e shocks.
16 X. SONG AND T. FANG
loani; ¼α1þβ1 emp H þϕ1con olsi; þηiþεi; (4)
npli; ¼α2þβ2 emp H þϕ2con olsi; þηiþεi; (5)
loani; ¼α3þβ3 emp H þγ3npli; þϕ3con olsi; þηiþεi; (6)
whe e loani; deno es he g ow h a e o he bank loan scale calcula ed by he i s
di e ence o log o al loans, emp H deno es he empe a u e shocks as men ioned
abo e, npli; deno es he log non-pe o ming loan a io o banks, and con olsi; deno es
bank-le el a iables, which include bank size (lnsize), log o al asse s o banks, he capi al
a io (cap), he liquidi y a io (liq), he a io o liquid asse s o o al asse s, he a io o
Figu e 8. Impulse esponses o he TVAR model wi h Y=[ emp _H, M2, CPI, GDP_ind]. This igu e
shows he impulse esponses o mone a y policy shocks o ou pu unde high- and low- empe a u e
shocks. Tempe a u e shocks a e emp _30, emp _25 and emp _20, which e lec qua e ly em-
pe a u es ha de ia e om his o ical 30/25/20-yea mo ing a e ages. The i s column shows impulse
esponses o ou pu o one-s anda d-de ia ion posi i e mone a y policy shocks, and he second
column shows impulse esponses o ou pu o one-s anda d-de ia ion nega i e mone a y policy
shocks. Solid lines deno e he impulse esponses unde low- empe a u e shocks, and dashed lines
a e unde high egimes o empe a u e shocks.
JOURNAL OF APPLIED ECONOMICS 17
owne s’ equi y o o al asse s. We also include mac oeconomic a iables, such as shadow
banking (sb), he g ow h a e o he social en us ed loan scale, he money s ock (M2),
and economic g ow h (GDP). ηi deno es he bank ixed e ec . Conside ing he accessi-
bili y o bank da a, we selec 343 banks, con aining 6 s a e-owned banks, 12 join -s ock
comme cial banks, 111 ci y comme cial banks, and 214 u al comme cial banks, span-
ning om 2004Q2 o 2021Q4. We ob ain bank cha ac e is ics and economic da a om
he WIND da abase.
Table 3 displays he es ima ion esul s o Equa ions (3)–(5). The s anda d e o s a e
clus e ed a he bank le el. In Columns (1), (4), and (7), he coe icien es ima es o
empe a u e shocks in Equa ion (3) a e signi ican ly nega i e a he 10% o 5% le el,
indica ing ha inc eases in empe a u e shocks impede bank lending. Fo example, he
Figu e 9. Impulse esponses o he TVAR model wi h Y=[ emp _H, M2, CPI, IGDP]. This igu e shows
he impulse esponses o mone a y policy shocks o ou pu unde high- and low- empe a u e shocks.
We eplace qua e ly da a wi h mon hly da a in TVAR model. Tempe a u e shocks a e emp _30,
emp _25 and emp _20, which e lec mon hly empe a u es ha de ia e om his o ical 30/25/20-
yea mo ing a e ages. The i s column shows impulse esponses o ou pu o one-s anda d-de ia ion
posi i e mone a y policy shocks, and he second column shows impulse esponses o ou pu o one-
s anda d-de ia ion nega i e mone a y policy shocks. Solid lines deno e he impulse esponses unde
low- empe a u e shocks, and dashed lines a e unde high egimes o empe a u e shocks.
18 X. SONG AND T. FANG
es ima ed ^
β1 o emp 30 is −0.007 wi h a obus -s a is ic o −1.906, which is signi ican
a he 10% le el. In Columns (2), (5), and (8), he es ima es o empe a u e shocks in
Equa ion (4) a e signi ican ly posi i e a he 1% le el, sugges ing ha empe a u e shocks
inc ease he non-pe o ming loan a io o comme cial banks. In Columns (3), (6), and
(9), he es ima ed coe icien s o empe a u e shocks in Equa ion (5) a e all insigni ican ,
while he coe icien s ^
γ3 o he non-pe o ming loan a io a e signi ican ly nega i e a
he 1% le el. The signi ican ^
β1s and ^
γ3s and he insigni ican ^
β3s indica e ha he non-
pe o ming loan a io o banks has a comple e media ing e ec in explaining he
in luence o empe a u e shocks on mone a y policies. Hypo hesis H2 is con i med.
6. Concluding ema ks
In his pape , we in es iga e whe he clima e change a ec s he e iciency o mone a y
policy. We use empe a u e shocks, calcula ed as empe a u e de ia ions om his o ical
a e age empe a u es, o p oxy clima e change, and u ilize TVAR models and GIRFs o
e eal he e ec s o expansiona y and igh mone a y policies on economic ou pu unde
high- and low- empe a u e egimes.
Ou esul s sugges ha he e is a clima e change egime dependency in mone a y
policy. Inc eases in empe a u e weaken he e iciency o mone a y expansions and
wo sen he nega i e e ec s o mone a y igh ening. Consis en wi h p e ious indings,
Table 3. Mechanism es o c edi channel.
(1) (2) (3) (4) (5) (6) (7) (8) (9)
loan npl loan loan npl loan loan npl loan
emp _30 −0.007*
(−1.906)
0.104***
(7.168)
−0.001
(−0.298)
emp _25 −0.007**
(−1.978)
0.124***
(8.069)
−0.000
(−0.160)
emp _20 −0.008**
(−2.175)
0.109***
(7.325)
−0.002
(−0.705)
npl −0.040***
(−5.651)
−0.040***
(−5.635)
−0.039***
(−5.638)
liq 0.105***
(2.618)
−1.119***
(−3.596)
0.041
(1.009)
0.103**
(2.571)
−1.086***
(−3.539)
0.041
(1.014)
0.103**
(2.569)
−1.111***
(−3.580)
0.040
(0.978)
cap 0.153
(0.651)
−6.110***
(−3.860)
0.085
(0.368)
0.150
(0.638)
−6.040***
(−3.801)
0.085
(0.368)
0.153
(0.649)
−6.116***
(−3.862)
0.085
(0.367)
lnsize −0.033*
(−1.888)
−0.428***
(−3.644)
−0.031**
(−2.008)
−0.033*
(−1.902)
−0.418***
(−3.560)
−0.031**
(−2.006)
−0.033*
(−1.891)
−0.430***
(−3.664)
−0.031**
(−2.017)
sb −0.059***
(−2.831)
−0.626***
(−5.581)
−0.077***
(−3.516)
−0.058***
(−2.785)
−0.645***
(−5.753)
−0.078***
(−3.515)
−0.058***
(−2.808)
−0.623***
(−5.561)
−0.076***
(−3.480)
M2 0.645***
(5.629)
−1.628***
(−3.537)
0.670***
(6.258)
0.640***
(5.558)
−1.472***
(−3.197)
0.672***
(6.240)
0.638***
(5.591)
−1.603***
(−3.496)
0.664***
(6.227)
gdp −0.012
(−0.228)
0.268*
(1.677)
0.053
(1.533)
−0.013
(−0.239)
0.285*
(1.781)
0.053
(1.547)
−0.016
(−0.289)
0.299*
(1.852)
0.051
(1.478)
cons 0.171***
(2.836)
−2.028***
(−5.514)
0.010
(0.183)
0.173***
(2.857)
−2.084***
(−5.686)
0.010
(0.170)
0.172***
(2.857)
−2.016***
(−5.502)
0.012
(0.220)
obs 4759 5451 4500 4759 5451 4500 4759 5451 4500
R
2
0.106 0.124 0.151 0.106 0.128 0.151 0.106 0.124 0.151
This able epo s he es ima ion esul s o Equa ions (4)–(6).The key a iables a e he g ow h a e o loan size (loan), log
non-pe o ming loan a io (npl) and emp _H (H = 30/25/20), which e lec s qua e ly empe a u es ha de ia e om
he his o ical H-yea mo ing a e age. Con ol a iables include liquidi y a io (liq), capi al a io (cap), log o al asse s
(lnsize), shadow banking (sb), money s ock (M2) and he economic g ow h a e (GDP). Numbe s in pa en heses a e
-s a is ics based on obus s anda d e o s clus e ed on bank le els. ***, ** and * deno e 1%, 5% and 10% signi icance
le els, espec i ely.
JOURNAL OF APPLIED ECONOMICS 19
we also ind ha he e a e asymme ic e ec s o expansiona y and igh mone a y policy,
in which igh mone a y policy has s onge e ec s in bo h high- and low- empe a u e
egimes. The asymme ies a e mo e ob ious unde high- empe a u e egimes. We
explain ou esul s by empi ically con i ming he clima e-induced c edi cons ain o
comme cial banks a gued by p e ious heo e ical s udies. Tempe a u e shocks can
dec ease labo p oduc i i y and TFP g ow h and inc ease i m ope a ion cos s, which
impai s i m p o i abili y associa ed wi h loan epaymen , co esponding o an inc ease
in he non-pe o ming loan a io o banks. Inc eases in non-pe o ming loan a ios lead
o c edi cons ain s induced by clima e change and hus weaken he c edi channel o
mone a y policy ansmissions.
In he e a o global wa ming, ou s udy has meaning ul implica ions o clima e isk
managemen and mi iga ion o cen al banks and inancial ins i u ions. Fi s , cen al
banks should inco po a e clima e change in o hei mone a y policy amewo k o
o mula e op imal mone a y policies. Fo example, cen al banks should implemen
mo e agg essi e expansiona y mone a y policy and weake igh mone a y policy when
clima e change is se e e. Second, i is c i ical o banks o manage clima e isk. As clima e
condi ions de e io a e, comme cial banks should ake p ecau ions o inc ease hei isk-
aking abili y o a oid capi al losses and pe o m clima e s ess es s o equen ly e alua e
clima e change isk.
The e a e se e al limi a ions o ou wo k. Fi s , we highligh he channel h ough
which banks engage in isk- aking in mone a y policy ansmission because he
banking sec o domina es he Chinese inancial sys em. In de eloped economies,
he capi al ma ke is mo e impo an han he banking sec o , and new channels
h ough which clima e change a ec s he in luence o mone a y policy should also be
in es iga ed. Second, clima e change a iables o daily equency may be a ailable, bu
mac oeconomic a iables a e usually qua e ly. When including bo h daily and
qua e ly a iables in a TVAR model, we mus ans o m he equency o clima e
change a iables om daily o qua e ly. This esul s in a loss o in o ma ion o
clima e change luc ua ions. To add ess his issue, we may ely on a mixed- equency
da a sampling model ha accommoda es a iables o di e en equencies (Ghysels
e al., 2006, 2007). Thi d, o he p oxies o clima e change and na u al disas e s, such
as p ecipi a ion, d ough , and loods, may also a ec he ansmission o mone a y
policy. We may use he TVAR o o he models o p o ide addi ional e idence. We
lea e hem o u u e esea ch.
Disclosu e s a emen
No po en ial con lic o in e es was epo ed by he au ho (s).
Funding
This wo k was suppo ed by he Na ional Na u al Science Founda ion o China [72303134], he
Na u al Science Founda ion o Shandong P o ince [ZR2020QG034], he Social Science Planning
P ojec o Shandong P o ince [22DJJJ14], he Young Inno a i e Team P ojec o Uni e si ies and
Colleges in Shandong [2022RW002], and he Young Schola Fu u e P ojec o Shandong
Uni e si y [2020].
20 X. SONG AND T. FANG
ORCID
Tong Fang h p://o cid.o g/0000-0002-9507-9668
Da a a ailabili y s a emen
A ailable upon eques .
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