Technological Forecasting & Social Change 191 (2023) 122480

Contents lists available at ScienceDirect

Technological Forecasting & Social Change

journal homepage: www.elsevier.com/locate/techfore

The interaction of climate risk and bank liquidity: An emerging market

perspective for transitions to low carbon energy
Qiaoqi Lang a, Feng Ma b, *, Nawazish Mirza c, Muhammad Umar d, e
a
School of Management, Wuhan Textile University, Wuhan, China
b
School of Economics and Management, Southwest Jiaotong University, Chengdu, China
c
Excelia Business School, La Rochelle, France
d
School of Economics, Qingdao University, Qingdao, Shandong, China
e
Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon

A R T I C L E I N F O A B S T R A C T

JEL classification: Climate change leads to many financial risks, including exerting pressure on banking liquidity. The issue is
Q54 plausibly more severe for emerging markets that suffer from financial frictions. Therefore, an assessment of the
Q56 liquidity profile of banks in emerging markets is necessary to understand the devastating impact of climate risk to
G18
devise optimal financial strategies to support low-carbon transitions. Using a comprehensive sample of banks
G21
from twenty-three emerging markets and assessing data for over a decade, we report that climate risk is nega­
Keywords:
tively associated with liquidity. This means that banks that are domiciled in countries with a higher level of
Climate change
Emerging markets
climate risk are likely to experience more liquidity pressures. The finding remained robust for various definitions
Liquidity risk of liquidity and after controlling for a series of exogenous variables. We argue that such pressures emanate from
Banking both physical and transitional risks and therefore it is in the interest of financial intermediaries to devise sus­
tainable financial strategies to support sustainable development goals and limit ecological degradation.

1. Introduction financial intermediaries (Gozgor, 2018; Lu et al., 2020; Shang et al.,

2022). While a plethora of studies evaluates the performance benefits for
Climate change and resulting ecological degradation are the pressing financial institutions, very few have assessed if climate degradation has
issues of recent times that warrant immediate action. The Paris agree­ any impact on the risk and more specifically the liquidity profile.
ment of 2015 and COP26 are notable global initiatives aimed at There are many reasons why climate risk may impair the liquidity of
reducing greenhouse emissions, financing low-carbon transitions, and the financial sector. If the physical risk is triggered (for example wildfire,
regulating carbon markets. Consequently, the focus was to facilitate floods, etc) it may spark the withdrawal of deposits resulting in more
circular economies and sustainable business models through conducive than anticipated outflows. Such risks may also crystalize the off-balance
financing and investments (Guo et al., 2022; Liang et al., 2022; Shan sheet commitments requiring immediate financing. In case of transition
et al., 2022). A key consideration of COP26 is to support emerging risks, the prudential regulations may get stringent limiting the available
markets and help them in the proactive management of carbon emis­ market-based funding or external recourse (for example if credit expo­
sions (Su et al., 2022b). sure is skewed towards high-emission firms). As climate change is non-
In emerging markets, financial intermediaries notably the banking linear, a sudden degradation may exert more regulatory pressures
sector play a critical role in channeling the funds between deficit and resulting in a hard landing for the banking sector.
surplus units (Ji et al., 2021b; Umar et al., 2021c). Multiple studies like Climate risk can also impact bank liquidity through the direct effects
(Umar et al., 2021a), (Rizvi et al., 2021), and (Ji et al., 2021a) have on their loan portfolios. Banks that have invested in sectors that are
documented that there are inherent benefits for stakeholders engaging vulnerable to the impacts of climate change, such as agriculture,
in green financial practices. These pieces of evidence suggest that cur­ tourism, and energy, can face increased defaults and losses due to
tailing climate risk through sustainable investment styles incentivizes climate-related events like droughts, hurricanes, and sea level rise. This

* Corresponding author.
E-mail addresses: qiaoqilang@wtu.edu.cn (Q. Lang), mafeng2016@swjtu.edu.cn (F. Ma), elahimn@excelia-group.com (N. Mirza), umar140287@hotmail.com
(M. Umar).

https://doi.org/10.1016/j.techfore.2023.122480
Received 30 September 2022; Received in revised form 27 February 2023; Accepted 1 March 2023
Available online 9 March 2023
0040-1625/© 2023 Elsevier Inc. All rights reserved.
Q. Lang et al. Technological Forecasting & Social Change 191 (2023) 122480

reduction in assets can result in a decrease in the bank’s liquidity, periods of turbulence and geopolitical crisis (Yarovaya and Mirza,
making it more difficult for the bank to meet its short-term obligations 2022). Therefore, it is critical to evaluate how bank liquidity evolves in
(Su et al., 2022a). Similarly, there can be a broader economic impact. the context of climate risk. To assess this, we adopt the following
Climate-related events can result in supply chain disruptions, increased empirical strategy.
costs for businesses, and decreased consumer spending, all of which can We constitute our sample from emerging markets using the classifi­
lead to a decrease in economic activity and lower profits for businesses cation of MSCI.1 The classification includes twenty-three countries from
(Yu et al., 2022). As a result, the bank’s loan portfolio may become less the Americas, Europe, the Middle East, Africa, and Asia. Further, we
valuable, leading to a decrease in its liquidity (Mirza et al., 2023). consider all locally incorporated banks in these locations that have
As mentioned before, the increasing regulatory focus on climate risk is disseminated liquidity and other fundamental data from January 2011
also having an impact on bank liquidity. Regulators are becoming more to June 2022. The choice of sample period is motivated by the argu­
stringent in their requirements for banks to assess and disclose their ments of (Alam et al., 2021), (Naqvi et al., 2021), and (Chen et al., 2022)
exposure to climate risk, and to implement strategies to manage this risk to ensure that there are no spillovers from the global financial crisis.
(Umar et al., 2022). This increased regulatory scrutiny can lead to Table 1 presents our sample distribution across countries.
increased costs for banks, including the cost of obtaining necessary in­ There are three constructs for liquidity that are employed in this
formation and implementing risk management strategies, which can paper. These include Loan to Deposits (Afzal and Firdousi, 2022; Fal­
further impact their liquidity (Li et al., 2020). Therefore, it is clear that lanca et al., 2020; Mili et al., 2019), liquidity coverage ratio (Gómez-
climate risk is having a significant impact on bank liquidity, and that Ortega et al., 2022; Nguyen and Nguyen, 2022), and market funds to
banks must take this issue seriously. This means taking steps to under­ tangible banking assets (Abdelsalam et al., 2022; Chipalkatti et al.,
stand and manage their exposure to climate risk, and to implement 2020; Khemakhem and Boujelbene, 2018). These variables of liquidity
strategies that will help to mitigate the impacts of this risk on their help us extend the study of (Lee et al., 2022) to present a more holistic
liquidity. By taking these steps, banks can ensure that they remain resil­ view of bank liquidity. The loan to deposits (LTD) compares the funding
ient in the face of the increasing challenges posed by climate change. pledged in long-term avenues. Given that the loan commitments are
The role of banking in addressing climate risk is crucial in emerging long-term while the average maturity of deposits is shorter, the ratio is
markets. These markets are often more susceptible to the impacts of meant to capture the liquidity (or illiquidity) available.
climate change due to factors such as poverty, limited infrastructure, The liquidity coverage ratio (LCR) is a supervisory instrument
and weak governance. The effects of a major climate-related crisis on introduced by Basel III, and it reflects the availability of highly liquid
financial stability in these regions could result in reduced access to credit assets with a bank. It is considered superior to other ratios because the
and increased loan defaults, leading to a liquidity crisis for banks estimation is forward-looking and sometimes considered a generic stress
operating there. This could have serious consequences for the broader test. The LCR is estimated as follows
economy. Additionally, emerging markets have a critical role to play in
LAit
global efforts to address climate change as many of these countries are LCRit = , (1)
NCOF it
significant emitters of greenhouse gases and also face significant climate
impacts. Banks in these markets can finance the transition to low-carbon where LA is high-quality liquid assets that can be easily converted into
and climate-resilient development, reducing the vulnerability of these
countries to the impacts of climate change. By taking a proactive
approach to managing climate risk, banks in emerging markets can help Table 1
Sample description.
ensure the long-term financial stability and sustainability of these re­
gions and support global efforts to mitigate and adapt to the effects of Countries No of Banks CRI Score
climate change. 2021

Based on this discussion, a valid research gap is to evaluate the Brazil 12 33.67
impact of climate change on the liquidity profile of the banking sector in Chile 9 33
Americas Colombia 8 36.33
emerging markets. This paper assesses this impact on liquidity creation
Mexico 9 59.5
by employing various definitions of liquidity. Overall, our results show Peru 5 56.33
that climate risk is negatively associated with the liquidity profile and Czech
13 92.83
this key finding remained robust even after sorting the sample based on Republic
bank size or geographic location. The channel for this relationship is Egypt 7 102
Greece 14 45
plausible. For example, if a borrower is heavily invested in industries Hungary 9 85.83
that are vulnerable to the effects of climate change, such as fossil fuels or Europe, Middle East, and Kuwait 6 118
real estate in flood-prone areas, then a shift in regulations or consumer Africa Poland 11 80
sentiment away from these industries. This could lead to a decrease in Qatar 9 118
Saudi Arabia 12 73
their value, potentially impacting the bank’s liquidity. Additionally, the
South Africa 15 32.5
cost of addressing climate change, such as building sea walls or tran­ Turkey 8 66
sitioning to renewable energy, could increase the expenses for bor­ UAE 9 118
rowers, leading to a higher likelihood of default and decreased liquidity China 35 42.83
for the bank. The findings have important implications for the role of India 20 16.67
Indonesia 12 24.83
financial intermediaries to support structural changes and transitions to Asia Korea 10 64
low-carbon energy consumption. Malaysia 9 87.33
The rest of the paper is organized as follows. Section 2 outlines our Philippines 6 26.67
empirical strategy and data. Section 3 presents the findings of this Thailand 5 43.17
Total 253
research and Section 4 concludes.

2. Empirical strategy and data

As mentioned earlier, liquidity plays a critical role in the financial

resilience of financial institutions, notably banks (Hasnaoui and Has­ 1
The classification is available at https://www.msci.com/our-solutions/
naoui, 2022). The importance of liquidity increases manifold during indexes/market-classification

2
Q. Lang et al. Technological Forecasting & Social Change 191 (2023) 122480

cash via market sales or using collateralization (for example through These results have important implications for the banking sector.
repos) without incurring a significant loss of value. The NCO refers to the Earlier evidence by (Chen et al., 2022; Umar et al., 2021b) documented
anticipated net cash outflow over a thirty-day horizon. It includes both that banking profitability benefits from green credit strategies. Our
on and off-balance sheet commitments. Finally, the ratio of market funds findings complement this notion by identifying that climate degradation
to tangible banking assets (MFTA) helps in measuring the liability side exerts liquidity pressures and therefore banks must proactively finance
volatility and associated liquidity risk. The estimate has predictive sustainable initiatives to limit climate change. For control variables, we
precision during periods of credit bouts, especially for institutions with could not deduce any significance for the Islamic banking dummy and
reliance on market funding to support liquidity needs. therefore can conclude that faith-based banking does not influence
To capture the relationship between climate change and bank liquidity creation. This is similar to the findings of (Mirza et al., 2015)
liquidity, our independent variable is the climate risk index (CRI) of who argued that there are no fundamental differences in the business
ResourceWatch. The index is aimed at identifying the adverse impact model of conventional and Islamic banking.
climate change had on different countries. These include meteorological We found banking spread to be positively associated with liquidity
events like storms, hydrological outbursts like floods, or climatological and this was consistent for the three constructs. This is plausible because
happenings like wildfires. A lower score on the index is an indication of the spread represents core intermediation earning and a higher spread
high climate risk and vice versa.2 We hypothesize that an increase in will support liquidity via persistent cash flows and deposit volume. The
climate risk will relapse the liquidity creation of the banking sector and market concentration is also significant and contributes to the liquidity
the banks in countries with higher climate risk will have lower liquidity profile. It is also understandable because, in emerging markets where
(Hughes, 2022). few banks tend to dominate the market share, liquidity is also concen­
There are exogenous factors that may impact the liquidity of the trated. Finally, the GDP growth is also significant suggesting that busi­
banks and therefore we control for them in our panel setting. Some of the ness cycles shape banking liquidity.
countries in our sample have a strong Islamic banking industry. (Reddy The results for the size-sorted sample are presented in Table 5. The
et al., 2017), (Mirza et al., 2022), (Louhichi et al., 2019), (Alizadeh findings for climate risk are similar to those of the complete sample as
et al., 2021), and (Tusiime and Wang, 2020) noted that faith-based the coefficient of CRI is significant and positively associated with all
financing exhibits unique characteristics that are different from their three variables of liquidity. Hence, we can argue that climate risk hin­
conventional counterparts. Hence, we control for this by introducing a ders bank liquidity regardless of the scale. Again, this is not surprising
dummy (IBD) that takes a value of 0 for Islamic banks and 1 otherwise. because climate risk is a macroeconomic sensitivity and therefore it has
Along similar lines, we control for banking spread, the market value of a similar impact on funding, financing, and market-based support. Since
equity, volatility of banking assets, credit quality, cost-to-income ratio, the consequences of climate risk for liquidity are scale neutral, both
market concentration, growth in GDP, and money supply. For the three large and small banks should expedite their efforts to support sustain­
constructs of liquidity, we employ the following panel regressions with ability goals.
country and year-fixed effects. The differences between big and small banks when it comes to
combating climate change include resources, risk tolerance, market in­
LTDit =αt + β1t CRI it + β2t IBDit + β3t πit + β4t MVEit + β5t VAit + β6t CtI it
(2) fluence, regulation, and customer demand. Big banks typically have
+ β7t HHI it + β8t gGDPit + β9t MSit + εt more resources, including financial, technological, and human re­
sources, which enables them to develop and implement comprehensive
LCRit =αt + β1t CRI it + β2t IBDit + β3t πit + β4t MVEit + β5t VAit + β6t CtI it
(3) and sophisticated strategies to manage climate risk. However, smaller
+ β7t HHI it + β8t gGDPit + β9t MSit + εt banks may have a lower risk tolerance and limited resources to manage
the risks posed by climate change, limiting their ability to invest in low-
MFTAit =αt + β1t CRI it + β2t IBDit + β3t π it + β4t MVEit + β5t VAit + β6t CtI it carbon and climate-resilient sectors and making it more challenging for
+ β7t HHI it + β8t gGDPit + β9t MSit + εt them to adapt to the impacts of climate change. About market influence,
(4) big banks have a greater market influence and can play a key role in
driving the transition to a low-carbon economy, while smaller banks
3. Results and discussion may have less influence but still play an important role in financing local
and regional initiatives to address climate change.
The descriptive statistics of selected variables are presented in In terms of regulation, big banks are often subject to more stringent
Table 2, while the correlation matrix of variables is shown in Table 3. regulatory requirements and are more likely to have the resources to
The pairwise correlation across all variables is low suggesting the meet these requirements. On the other hand, smaller banks may face
absence of multicollinearity. fewer regulatory requirements but may also have limited resources to
The results of fixed effect panel regression of the complete sample for comply with these regulations. In terms of customer demand, big banks
Eqs. (2) to (4) are presented in Table 4. Our results show that liquidity is may have a broader customer base, including institutional investors and
negatively associated with climate change. This is true for the three large corporations, which may have greater demands for climate-
definitions of liquidity with loans to deposits significant at 5 %, while LCR friendly financial products and services. However, smaller banks may
and market funds to tangible assets are significant at 1 %. This is plausible have fewer resources to meet these demands but may be better posi­
as climate change can limit the ability of financial institutions to raise tioned to serve local communities and support local initiatives to address
additional funds or liquidate assets to support liquidity. Alternatively, the climate change.
climate risk (for example natural disasters) can trigger deposit outflow Finally, Both big and small banks have a critical role to play in
due to unprecedented withdrawals. Similarly, due to physical risks, some addressing climate change. By working together, they can leverage their
unfunded exposure (for example guarantees) may become due and exert strengths and address their weaknesses to support the transition to a
pressure on the banking liquidity. This is in line with the earlier findings low-carbon and climate-resilient economy.
of (Brei et al., 2019) which suggested that physical risks tend to impair There are some interesting observations concerning the control
banking fundamentals including liquidity risk. variables for the size-sorted sample. The dummy for Islamic banking
remains insignificant regardless of the size. For the bigger banks, the
variables related to size are significant. This includes the market value of
equity and the HHI measure of concentration. The spread was not sig­
2
The detailed methodology of Climate Risk Index is available at https:// nificant for the larger banks while it was for small banks. This reveals
resourcewatch.org/ that larger banks in emerging markets largely benefit from the scale

3
Q. Lang et al. Technological Forecasting & Social Change 191 (2023) 122480

Table 2
Descriptive Statistics of Selected Variables (Weighted Average for the Sample Period).
Loans to Deposits LCR Market Funds to Tangible Banking Assets Spread Volatility of Assets Asset Quality Cost to Income

Brazil 0.8339 0.1606 0.3293 0.0970 0.0399 0.1972 0.4887

Chile 0.8551 0.1568 0.2905 0.0984 0.0378 0.1866 0.4622
Colombia 0.7806 0.1786 0.3501 0.0402 0.0453 0.1002 0.4233
Mexico 0.8195 0.2267 0.3280 0.0167 0.0405 0.0745 0.5062
Peru 0.9475 0.1390 0.4283 0.0613 0.0435 0.0931 0.4676
Czech Republic 0.8106 0.1702 0.3210 0.0373 0.0446 0.1150 0.4109
Egypt 0.9119 0.1670 0.3700 0.0926 0.0370 0.1330 0.5210
Greece 0.8805 0.1485 0.2798 0.0868 0.0365 0.0141 0.4841
Hungary 0.8737 0.1400 0.3777 0.0929 0.0360 0.1981 0.4959
Kuwait 0.9137 0.1567 0.3711 0.0879 0.0425 0.0981 0.5550
Poland 0.7860 0.1845 0.2791 0.0316 0.0411 0.0690 0.5412
Qatar 0.8807 0.2389 0.3356 0.0556 0.0376 0.1229 0.4804
Saudi Arabia 0.8669 0.1501 0.4500 0.0612 0.0428 0.1845 0.4596
South Africa 0.8406 0.1608 0.4259 0.0327 0.0393 0.0875 0.4469
Turkey 0.8526 0.1235 0.3937 0.0501 0.0442 0.0982 0.4407
UAE 0.8845 0.1641 0.3976 0.0386 0.0396 0.1064 0.4525
China 0.9261 0.1733 0.3556 0.0288 0.0426 0.1443 0.5330
India 0.8911 0.1639 0.2971 0.0153 0.0336 0.1002 0.5240
Indonesia 0.9075 0.1931 0.3851 0.0554 0.0382 0.0802 0.5033
Korea 0.8475 0.1812 0.4238 0.0892 0.0414 0.1547 0.5207
Malaysia 0.9463 0.1579 0.3610 0.0849 0.0346 0.1148 0.5453
Philippines 0.8891 0.1887 0.3885 0.0959 0.0487 0.0786 0.6253
Thailand 0.8206 0.1667 0.2921 0.0217 0.0385 0.1583 0.6945

while the smaller banks have to rely on the core earnings for supporting variables can help the stakeholder reconcile the factors that influence
their liquidity. For smaller banks both the volatility in assets and assets bank liquidity in addition to the climate risk.
quality are relevant revealing that asset variation and quality are Financial institutions can take several measures to manage the
important determinants of liquidity. Finally, the business cycles signif­ liquidity problems posed by climate change. They can conduct a thor­
icantly influence smaller banks with GDP growth associated positively ough assessment of the potential impacts of climate change on their
with liquidity. However, for bigger banks, there is no impact on GDP operations and investments, which helps to identify the areas of greatest
growth. risk and prioritize efforts to manage those risks. Portfolio management is
Given that our sample stems from very diversified geographic re­ another key strategy, where financial institutions can consider reducing
gions, we also present the results for Eqs. (2) to (4) after sorting the exposure to high-risk industries, such as fossil fuels, and increasing in­
sample banks into three regions. These include the Americas, Europe vestments in low-carbon and climate-resilient sectors, such as renewable
Middle East, Africa, and Asia. It is important to consider climate risk energy and sustainable agriculture. Stress testing is a useful tool, as it
across various locations because the impacts of climate change can vary allows financial institutions to evaluate the resilience of their balance
widely depending on geography. Different regions may experience sheets and identify areas where additional capital may be needed to
different levels of vulnerability to extreme weather events, sea level rise, withstand potential losses. Financial institutions can also develop and
and other effects of a changing climate. For example, coastal areas may implement risk management strategies, such as insurance, to protect
be more susceptible to flooding and storm surges, while regions with against the impacts of climate change and offer financial products that
water scarcity may face increased competition for resources. In addition, help clients adapt to and mitigate the effects of climate change.
the local infrastructure, economies, and social systems can also play a Improving transparency and disclosure of their exposure to climate risk
role in shaping a region’s vulnerability to climate change. For example, and efforts to manage those risks can also build trust with stakeholders
an area with a strong tourism industry may be more economically and increase the ability to attract investment. By taking these steps,
vulnerable to the effects of rising temperatures or changes in weather financial institutions can better understand and manage the risks posed
patterns that affect the tourism season. by climate change, reducing the potential for liquidity problems in the
By considering climate risk across various locations, organizations, future.
including banks, can better understand the potential impacts on their
operations and investments and take steps to manage those risks. This 4. Conclusion and policy implications
can help to ensure the long-term financial stability and sustainability of
the organization, as well as support efforts to mitigate and adapt to the 4.1. Conclusion
effects of climate change. The results are presented in Table 6. Similar to
our earlier findings, climate risk proves to be an important determinant The financial risks emanating from climate change pose real chal­
of liquidity across all regions and there is a negative relation between lenges for the financial system. These risks come from both physical and
the two. This is plausible because climate degradation is a global phe­ transitional aspects of climate change. Among other fundamentals, the
nomenon and our results show that across all regions the banking liquidity profile of financial institutions is also sensitive to ecological
liquidity is sensitive to the CRI. Hence, while many financial policies are degradation. Liquidity is critical for banks because it is necessary to limit
domestic, combatting climate change is something that requires a global extreme events like bank runs and satiate the outflow demands of un­
pursuit. secured creditors. In this paper, we evaluate if climate risk affects the
In the case of control variables, there are some regional discrep­ liquidity profile of banking firms in emerging markets.
ancies. For banks located in the Americas, the volatility of assets and Our results show that liquidity is sensitive to climate risk and banks
business cycles is equally important. The banks in Europe, the Middle in countries with greater climate risk experience more pressure on their
East, and Africa, size, asset quality, and market concentration also play a liquidity compared to the banks that are domiciled in countries with
significant role in determining liquidity. Finally, in Asia, banking relatively lower climate risk. The findings remained robust for various
liquidity benefits from the intermediation spread, asset quality, market definitions of liquidity. We repeated our assessment after sorting the
concentration as well as business cycles. The findings on these control sample based on the firm size and climate risk continued to remain

4
Q. Lang et al. Technological Forecasting & Social Change 191 (2023) 122480

Table 4

0.184062
Panel regression results for complete sample.

gGDP
Loan to Deposits LCR Market Funds to Tangible
Banking Assets

Constant 0.0180 0.5376 0.5436

0.284748
0.070807
t stats 0.4198 0.9914 0.6121
CRI 0.0538 ** − 0.0991 *** − 0.0408 ***
HHI

t stats 1.9926 − 3.0150 − 3.1706

IBD 0.9497 0.7942 0.2113
t stats 1.3879 0.1724 0.7131
Spread − 0.0375 ** 0.0418 ** 0.0573 **

0.293059
0.108425
0.20475
t stats − 2.0244 1.9914 2.1891
MVE 0.1556 0.2245 0.1802
CtI

t stats 0.4184 1.7653 0.8618

V(A) 0.0488 0.0127 0.0794
0.032565 t stats 0.9258 1.4535 0.6306

0.136406
0.136569
− 0.16901 AQ 0.0713 0.0430 0.0453
t stats 0.8435 0.9437 1.0560
CtI 0.0819 0.0927 0.0360
AQ

t stats 0.4299 0.9567 0.3492

HHI − 0.0819 *** 0.0687 *** 0.0806 ***
t stats − 2.5459 3.5919 2.8606
gGDP 0.0211 ** 0.0618 ** 0.0180 **
0.186885

0.078087
0.259523
0.009991
− 0.15343

t stats 2.0390 2.1618 2.0775

MS 0.8803 0.2210 0.5336
V(A)

t stats 0.6875 0.4081 0.8795

Country FE YES
Year FE YES
Adj R2 0.711389 0.61198 0.678269
0.028887
0.095384

0.239237
− 0.13009

0.21649
− 0.01769

***represent significance at 1 %, ** at 5 % and * at 10 %.

MVE

significant. Finally, creating a subsample based on geographical loca­

tions yielded the same result. This, while confirming, a negative rela­
0.018456

0.183239
0.122641
0.063102
0.006145

tionship between climate risk and liquidity, also indicates that liquidity-
0.25245
− 0.0328

related concerns will be there for all banks regardless of the size or their
Spread

location. Since there is no escape for any firm, we conclude that a

comprehensive and globally coordinated financial strategy is warranted
to mitigate the climate risk.
0.009279

0.119738

0.068286
0.136058
0.237254
− 0.11242

− 0.04556

0.36293

4.2. Policy implications

IBD

Banks can play a crucial role in achieving the targets of COP26 by

supporting and financing low-carbon and sustainable projects and ini­
0.271617

0.163911

0.145637
0.310375

0.300613

tiatives. This can include financing renewable energy projects, sup­

− 0.19602
− 0.07203

− 0.02239

0.10512

porting energy-efficient buildings, and investing in sustainable

CRI

agriculture and transportation. Banks can also encourage their cus­

tomers to adopt sustainable practices by offering green financial prod­
ucts and promoting environmentally-friendly behavior. Additionally,
0.05725
0.16579
0.22236
0.03339
0.14764
0.15893
0.04958

0.04284

banks can work towards reducing their carbon footprint and becoming
− 0.1305

− 0.094
MFTBA

more environmentally conscious in their internal operations.

Furthermore, by implementing guidelines and regulations, author­
ities may push banks to manage the risks associated with climate change
in their operations and urge banks to assess the threats posed by climate
0.0547
0.1793

0.2936
0.1478
0.2883
0.0686
0.1739

0.0269
0.0161
− 0.149

0.007

MFTBA = Market Funds to Tangible Banking Assets.

change. Policymakers may support low-carbon financing by incentiv­

LCR

izing banks to finance projects by introducing tax incentives or estab­

lishing financing streams dedicated to low-carbon activity. Improving
data collection and analysis is possible so policymakers understand the
Correlation matrix of selected variables.
Loan to Desposits

risks associated with climate change. This information may formulate

− 0.026463395
− 0.071879612
0.273921536
0.104484401
0.041268258
0.233485859
0.039766042
− 0.124014924
0.121854174
0.093938073
− 0.073655413
0.19700907

effective policies and laws promoting the transition to a low-carbon

economy. In order to facilitate the transition to a low-carbon econ­
omy, policymakers can promote collaboration across several stake­
holders. This might entail collaborating with banks, governments, and
other stakeholders to develop solutions for mitigating climate change
risks and supporting low-carbon activities. Access to capital is some­
Loan to Desposits

thing that policymakers may facilitate for small and medium-sized en­
terprises (SMEs) that are transitioning to a low-carbon economy.
Policymakers can enhance regulatory frameworks to support the tran­
MFTBA

Spread
Table 3

gGDP

sition to a low-carbon economy. This may entail establishing criteria for

MVE
V(A)

HHI
LCR

IBD
CRI

AQ

MS
CtI

measuring carbon emissions, establishing goals for reducing emissions,

5
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Table 5 Table 6
Panel regression results for size sorted sample. Panel regression results for geographic sample.
Loan to Deposits LCR Market Funds to Tangible Loan to Desposits LCR Market Funds to Tangible
Banking Assets Banking Assets

Big Banks Americas

Constant 0.0118 0.3529 0.3569 Constant 0.014205 0.424736 0.429503
t stats 0.2756 0.6509 0.4018 t stats 0.331643 0.783292 0.483616
CRI 0.0353 ** − 0.0651 *** − 0.0268 *** CRI 0.042474 ** − 0.07833 *** − 0.03224 ***
t stats 1.9631 − 2.9793 − 4.0815 t stats 2.036253 − 3.58556 − 4.01202
IBD 0.6235 0.5214 0.1387 IBD 0.750341 0.627469 0.166912
t stats 0.9111 0.1132 0.4682 t stats 1.096536 0.136205 0.563424
Spread − 0.0246 0.0275 0.0376 Spread 0.029595 0.033061 0.04529
t stats − 1.3290 1.3074 1.4371 t stats 1.599472 1.573387 1.072955
MVE 0.1022 ** 0.1474 *** 0.1183 *** MVE 0.122968 0.177373 0.142346
t stats 2.1275 3.1589 2.5657 t stats 1.560369 3.801718 1.087827
V(A) 0.0320 0.0084 0.0521 V(A) 0.038559 ** 0.010059 ** 0.062747 ***
t stats 0.6078 0.9542 0.4140 t stats 1.973145 1.148348 3.498247
AQ 0.0468 0.0282 0.0298 AQ 0.056342 0.033965 0.035827
t stats 0.5538 0.6195 0.6933 t stats 0.666462 0.745579 0.834346
CtI 0.0537 0.0609 0.0236 CtI 0.064674 0.073266 0.028438
t stats 0.2822 0.6281 0.2293 t stats 0.33965 0.755862 0.275902
HHI − 0.0537 ** 0.0451 ** 0.0529 ** HHI 0.064674 0.054315 0.063669
t stats − 1.9671 2.1358 1.9878 t stats 0.36741 2.570403 0.392274
gGDP 0.0139 0.0406 0.0118 gGDP 0.016691 ** 0.048815 ** 0.014235 **
t stats 1.3386 1.4192 1.3639 t stats 1.961096 1.707988 1.996414
MS 0.5779 0.1451 0.3503 MS 0.695546 0.174617 0.421617
t stats 0.4513 0.2679 0.5774 t stats 0.543147 0.322399 0.69484
Country FE YES Country FE YES
Year FE YES Year FE YES
Adj R2 0.68175 0.7408 0.71795 Adj R2 0.71091 0.6251 0.70139

Small Banks Europe Middle East and Africa

Constant 0.0160 0.4774 0.4828 Constant 0.024329 0.727464 0.735628
t stats 0.3728 0.8805 0.5436 t stats 0.56802 1.341578 0.82831
CRI − 0.0477 ** − 0.0880 *** − 0.0362 *** CRI − 0.07275 *** − 0.13416 *** − 0.05522 ***
t stats − 1.7697 − 2.6776 − 3.8159 t stats − 2.69642 − 3.07989 − 5.81422
IBD 0.8434 0.7053 0.1876 IBD 1.285141 0.746933 0.285878
t stats 1.2326 0.1531 0.6333 t stats 1.878085 0.233283 0.965
Spread − 0.0333 *** 0.0372 *** 0.0509 *** Spread 0.050689 0.056625 0.07757
t stats 2.7979 3.7686 3.9441 t stats 1.263183 1.742204 0.966668
MVE 0.1382 0.1994 0.1600 MVE 0.210612 ** 0.303794 *** 0.243802 ***
t stats 0.3716 1.5678 0.7653 t stats 2.056625 2.388861 3.16615
V(A) 0.0433 ** 0.0113 ** 0.0705 ** V(A) 0.066041 0.017229 0.107469
t stats 1.9822 2.0291 2.1560 t stats 0.203062 0.917096 0.285104
AQ 0.0633 ** 0.0382 *** 0.0403 ** AQ 0.0965 *** 0.058173 *** 0.061362 ***
t stats 2.0749 0.8381 2.2938 t stats 3.161545 3.276984 3.495039
CtI 0.0727 0.0824 0.0320 CtI 0.110771 0.125485 0.048707
t stats 0.3818 0.8496 0.3101 t stats 0.581732 1.294597 0.472549
HHI − 0.0727 0.0611 0.0716 HHI − 0.11077 ** 0.093027 ** 0.109048 **
t stats − 1.2610 0.1900 0.5405 t stats − 1.99141 2.028952 1.982357
gGDP 0.0188 ** 0.0549 ** 0.0160 ** gGDP 0.028588 0.083607 0.02438
t stats 1.9581 1.9990 1.9845 t stats 0.983567 0.458205 0.237821
MS 0.7818 0.1963 0.4739 MS 1.191291 0.299074 0.722121
t stats 0.6105 0.3624 0.7810 t stats 0.930271 0.552187 1.190083
Country FE YES Country FE YES
Year FE YES Year FE YES
Adj R2 0.79014 0.68527 0.60312 Adj R2 0.642729 0.720854 0.681491

***represent significance at 1 %, ** at 5 % and * at 10 %.

Asia
Constant 0.016566 0.495352 0.500911
and promoting sustainable financial techniques. By taking these steps, t stats 0.386782 0.913521 0.564021
banks can help to achieve COP26 goals and contribute to the transition CRI − 0.04954 ** − 0.09135 *** − 0.0376 ***
towards a low-carbon, sustainable future. t stats − 1.98361 − 3.09719 − 3.95907
IBD 0.875091 0.508609 0.194663
t stats 1.278844 0.15885 0.657097
CRediT authorship contribution statement Spread 0.034516 ** 0.038557 ** 0.05282 **
t stats 2.086014 2.186319 2.065823
Qiaoqi Lang: Conceptualization, Investigation, Writing – review & MVE 0.143412 0.206863 0.166012
editing. Feng Ma: Conceptualization, Supervision, Validation, Project t stats 1.400417 1.626647 1.155926
V(A) 0.044969 0.011732 0.073179
administration, Writing – review & editing. Nawazish Mirza: Data
t stats 0.138271 0.624478 0.194136
curation, Software, Validation, Project administration, Writing – orig­ AQ 0.06571 ** 0.039612 *** 0.041783 ***
inal draft. Muhammad Umar: Conceptualization, Methodology, Visu­ t stats 2.015279 2.531397 2.798767
alization, Writing – original draft. CtI 0.075427 0.085447 0.033166
t stats 0.396119 0.88153 0.321773
HHI − 0.07543 *** 0.063345 *** 0.074254 ***
(continued on next page)

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Q. Lang et al. Technological Forecasting & Social Change 191 (2023) 122480

Table 6 (continued ) Li, J.P., Mirza, N., Rahat, B., Xiong, D., 2020. Machine learning and credit ratings
prediction in the age of fourth industrial revolution. Technol. Forecast. Soc. Chang.
Loan to Desposits LCR Market Funds to Tangible 161, 120309 https://doi.org/10.1016/j.techfore.2020.120309.
Banking Assets Liang, C., Umar, M., Ma, F., Huynh, T.L., 2022. Climate policy uncertainty and world
renewable energy index volatility forecasting. Technol. Forecast. Soc. Chang. 182,
t stats − 3.35601 3.381574 2.934985
121810.
gGDP 0.019466 ** 0.05693 ** 0.016601 *** Louhichi, A., Louati, S., Boujelbene, Y., 2019. Market-power, stability and risk-taking: an
t stats 1.966974 1.99312 2.816194 analysis surrounding the riba-free banking. Rev. Acc. Financ. 18, 2–24. https://doi.
MS 0.811186 0.203648 0.491714 org/10.1108/RAF-07-2016-0114/FULL/XML.
t stats 0.633449 0.376 0.810363 Lu, Z., Gozgor, G., Huang, M., Keung Lau, M.C., 2020. The impact of geopolitical risks on
Country FE YES financial development: evidence from emerging markets. J. Compet. 12, 93–107.
Year FE YES https://doi.org/10.7441/JOC.2020.01.06.
Adj R2 0.731101 0.72093 0.641347 Mili, M., Khayati, A., Khouaja, A., 2019. Do bank independency and diversification affect
bank failures in Europe? Rev. Acc. Financ. 18, 366–398. https://doi.org/10.1108/
***represent significance at 1 %, ** at 5 % and * at 10 %. RAF-09-2017-0181/FULL/XML.
Mirza, N., Rahat, B., Reddy, K., 2015. Business dynamics, efficiency, asset quality and
stability: the case of financial intermediaries in Pakistan. Econ. Model. 46 https://
Data availability doi.org/10.1016/j.econmod.2015.02.006.
Mirza, N., Abbas Rizvi, S.K., Saba, I., Naqvi, B., Yarovaya, L., 2022. The resilience of
Data will be made available on request. Islamic equity funds during COVID-19: evidence from risk adjusted performance,
investment styles and volatility timing. Int. Rev. Econ. Financ. 77, 276–295. https://
doi.org/10.1016/J.IREF.2021.09.019.
Acknowledgment Mirza, N., Afzal, A., Umar, M., Skare, M., 2023. The impact of green lending on banking
performance: evidence from SME credit portfolios in the BRIC. Econ. Anal. Policy 77,
843–850. https://doi.org/10.1016/J.EAP.2022.12.024.
This work is supported by the Natural Science Foundation of China Naqvi, B., Mirza, N., Rizvi, S.K.A., Porada-Rochoń, M., Itani, R., 2021. Is there a green
[72071162] and the Natural Science Foundation of Sichuan Province fund premium? Evidence from twenty seven emerging markets. Glob. Financ. J. 50,
[2022NSFSC0917]. 100656 https://doi.org/10.1016/J.GFJ.2021.100656.
Nguyen, Thach V.H., Nguyen, Thai Vu Hong, 2022. How do banks price liquidity? The
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Ji, X., Chen, X., Mirza, N., Umar, M., 2021a. Sustainable energy goals and investment Qiaoqi Lang is an assistant professor in the School of Management at Wuhan Textile
premium: evidence from renewable and conventional equity mutual funds in the University, Her research interests include economic forecasting, corporate governance and
euro zone. Resour. Policy 74, 102387. https://doi.org/10.1016/J. energy economics. She has published in journal such as: International Journal of Finance &
RESOURPOL.2021.102387. Economics etc.
Ji, X., Zhang, Y., Mirza, N., Umar, M., Rizvi, S.K.A., 2021b. The impact of carbon
neutrality on the investment performance: evidence from the equity mutual funds in
Feng Ma is an Associate Professor in the School of Economics & Management at Southwest
BRICS. J. Environ. Manag. 297, 113228 https://doi.org/10.1016/J.
Jiaotong University. His research interests include time series forecasting, empirical
JENVMAN.2021.113228.
finance, and energy economics. He has published many papers in the Journal of Banking &
Khemakhem, S., Boujelbene, Y., 2018. Predicting credit risk on the basis of financial and
Finance, Journal of Empirical Finance, Quantitative Finance, Energy Economics, Journal
non-financial variables and data mining. Rev. Acc. Financ. 17, 316–340. https://doi.
of Forecasting, Economic Modelling, Applied Economics and Empirical Economics, etc.
org/10.1108/RAF-07-2017-0143/FULL/XML.
Lee, C.C., Wang, C.W., Thinh, B.T., Xu, Z.T., 2022. Climate risk and bank liquidity
creation: international evidence. Int. Rev. Financ. Anal. 82, 102198 https://doi.org/ Nawazish Mirza is the Professor of Finance at Excelia Business School, La Rochelle,
10.1016/J.IRFA.2022.102198. France. He is Editor in Chief of the Journal of Risk Finance, and Review of Accounting and

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Q. Lang et al. Technological Forecasting & Social Change 191 (2023) 122480

Finance. He has also guest-edited Special Issues in Energy Economics, Resources Policy, Muhammad Umar is a Professor of Finance at Qingdao University, China, and an Adjunct
Economic Analysis and Policy, and Climate Change Economics. Dr. Mirza obtained his Research Professor at Lebanese American University, Lebanon. His name appeared
Doctorate in Finance, from the University of Paris Dauphine. His focus of teaching and World’s most influential researchers list (1 % of the World), recognized by the Web of
research are Sustainable Finance, Entrepreneurship, Financial Valuation, Investments, and Science™ as a Highly Cited Researcher in 2022. He is the Associate Editor of the Journal of
Financial Risk Management. Dr. Mirza’s recent research has been published in Techno­ Risk Finance and Review of Accounting and Finance. He has robust research credentials
logical Forecasting and Social Change, International Review of Economics and Finance, with interests in Green Finance, Energy Economics, Financial and Resource Markets, In­
Annals of Operations Research, Economic Modelling, Pacific-Basin Finance, Finance vestment and Financial Analysis, Behavioral Finance, Empirical Finance, Financial Risk
Research Letters, Journal of Asset Management, Resources Policy, and Journal of Envi­ Management, Portfolio Management, and Crypto assets. Umar’s work is featured in
ronmental Management, among others. Before his academic stint, Dr. Mirza worked in various well-reputed journals, including Energy Economics, Annals of Operations
Investment Banking, Asset Management, and Credit Ratings. He regularly contributes his Research, International Review of Financial Analysis, Technological Forecasting & Social
opinions to popular media like Bloomberg, CNBC, China Daily, etc. Change, Finance Research Letter, Journal of Cleaner Production, Energy, Energy Policy,
Pacific-Basin Finance Journal, Journal of Environmental Management, Resources Policy,
Economic Research-Ekonomska Istraživanja, and Science of the Total Environment, etc.