




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(1) (2025), 34-45 

34 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 1 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

IMPACT OF GREEN FINANCE ON ENVIRONMENTAL AND 

SOCIAL RISK MANAGEMENT INFLUENCING PERFORMANCES 

OF BANKS: EVIDENCE FROM BANGLADESH      
 

 Md. Jahangir Alam Siddikee (a)1     AHM Ziaul Haq (b)   Shahnaz Parvin (c)   Humaira Begum (d)   Mst. 

Rinu Fatema (e)   
 

(a) PhD Fellow, Institute of Bangladesh Studies (IBS), University of Rajshahi, Rajshahi and Associate Professor, Hajee Mohammad Danesh 

Science and Technology University, Dinajpur, Bangladesh; E-mail: msiddikee@yahoo.com 
(b) Professor, University of Rajshahi, Rajshahi, Bangladesh, E-mail: zia_haq2001@yahoo.com 
(c) Professor, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh; E-mail: shahnazhstu09@gmail.com 
(d) MPhil Fellow, Institute of Bangladesh Studies (IBS), University of Rajshahi, Rajshahi and Associate Professor, Hajee Mohammad Danesh 

Science and Technology University, Dinajpur,  Bangladesh;  E-mail: humaira.begum@hstu.ac.bd 
(e) Assistant Professor, Noakhali Science and Technology University, Noakhali, Bangladesh; E-mail: fatemarenu.thm@nstu.edu.bd 
                 

 
A R T I C L E I N F O 

 
 

Article History: 

 

Received: 26th September 2024 

Reviewed & Revised: 26th September 2024 

to 29th January 2025 

Accepted: 1st February 2025 

Published: 5th February 2025 

 
Keywords: 

 

Green Finance, Environment, Impact, 

Relationship and Performance 

 
JEL Classification Codes: 

 

      C58, G21, Q53 

       
       

      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method. 

        

 
A B S T R A C T      

 

The growing emphasis on green finance (GF) presents significant attention for banks as they navigate 

environmental and social risk management (ESRM) while striving for financial performance. Emerging 

the global phenomenon with respect to environmental issues focuses on highlighting the study of GF 

with ESRM, which leads to the profitability and non-performing loans of banks.  This study investigates 

the impacts of GF on ESRM and, in turn, impacts the performance-profitability and non-performing 

loans of banks, providing empirical evidence from Bangladesh. This study employs the numerical data 

collected from annual reports of the central bank- Bangladesh Bank, from 2015 to 2023. The study has 

focused on variables such as GF sectors, environmental and social risk management, profitability, and 

non-performing loans. Regression models (Panel ordinary least square, quarantine regression, fixed 

effect model, random effect model, and panel generalized method of the moment) are employed to 

examine the effects of GF on ESRM and the impact of ESRM on performance - profitability and non-

performing loans. The result shows that GF significantly influences ESRM because the p-value is 

0.00≤p-value≤.0.10, rejecting the null hypothesis. ESRM has a significant impact on the performance of 

banks as a mediating factor with a p-value of 0.00≤p-value≤ 0.10. The findings of this study suggest that 

GF practices significantly enhance ESRM, which impacts the performance of banks and provides 

valuable insights for policymakers, regulators, and banking stakeholders in mitigating environmental 

and social risks and improving the performance of banks.   

 
 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

 

INTRODUCTION 
Green finance plays a crucial role in environmental and social risks by promoting sustainable banking practices, ensuring 

long-term financial stability, and enhancing the performance of banks. Bangladesh has made significant studies in recent 

decades. However, its financial sector faces its own set of challenges, including concerns related to non-performing loans, 

financial stability, and corporate governance. As a result, there is growing recognition of the importance of GF in addressing 

environmental and social risk management as well as performance. Moreover, this study is significant as it provides 

empirical evidence from a developing country like Bangladesh, where financial organizations increasingly integrate green 

finance to address climate risks, regulatory compliance, and ESRM, ultimately influencing profitability and non-performing 

loans, fostering a responsible and profitable banking sector (Siddikee et al., 2024). 

The study of (Fan et al., 2024) states that investments in clean and affordable energy can promote sustainable 

development. It may reduce the risk of loan defaults by aligning financial support with environmentally friendly practices. 

By incorporating GF practices into their strategic plans, financial institutions can contribute to environmental well-being 

and achieve sustainability goals over short and long periods (Abuatwan, 2023). GF serves as an intermediary between green 

                                                      
1Corresponding author: ORCID ID: 0009-0003-1138-2390  
© 2025 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v10i1.2285 

 
To cite this article: Siddikee, M. J. A., Haq, A. Z., Parvin, S., Begum, H., & Fatema, M. R. (2025). IMPACT OF GREEN FINANCE ON 

ENVIRONMENTAL AND SOCIAL RISK MANAGEMENT INFLUENCING PERFORMANCES OF BANKS: EVIDENCE FROM BANGLADESH. 

Bangladesh Journal of Multidisciplinary Scientific Research, 10(1), 34-45. https://doi.org/10.46281/bjmsr.v10i1.2285 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v10i1.2285
https://orcid.org/0009-0003-1138-2390
https://orcid.org/0009-0001-3944-0025
https://orcid.org/0009-0000-9063-5314
https://orcid.org/0000-0002-5355-415X
https://orcid.org/0009-0001-8409-6737


Siddikee et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 34-45

 

35 

banking and environmental performance, with green banking activities exerting a noteworthy influence on environmental 

performance (Zhang et al., 2022). GF positively influences environmental performance by safeguarding and restoring 

ecosystems (Xi et al., 2021). The worldwide financial landscape is witnessing a paradigm change towards green finance. 

The study of sustainable finance has become a recognized discipline in the literature on finance (Poyser & Daugaard, 2022). 

They emphasize that a mental reset is necessary to map the published information on indigenous sustainable finance (Hasan 

et al., 2025).  

This study employs the panel ordinary least square (POLS) to justify GF's impact on ESRM on profitability and 

non-performing loans on mean distribution. Quarantine regression is employed to determine the conditional impact. Next, 

this study conducts fixed effect models (FEM) and random effect models, and panel generalized method of moment 

(PGMM) in identifying the fixed, random and dynamic impact of GF on ESRM that on performance. Overall, this study 

aims to investigate the effect of GF on ESRM and the impact of ESRM on profitability and bank non-performing loans.  

This article is structured as follows: It begins with a literature review, then materials and methods, followed by 

results, discussion, and conclusion.  
 

LITERATURE REVIEW 

Green finance refers to financial investments that maintain environmentally sustainable development. It encompasses 

various financial instruments, policies, and practices to promote ecological balance and mitigate climate issues. The concept 

aligns with the broader goals of green development, integrating environmental, social, and governance considerations into 

financial decision-making. GF plays a considerable role in driving the green enterprise transition by facilitating market-

oriented governance, green oversight, and green governance, and its promotional impact is more pronounced in regions with 

strong state environmental governance, in firms with less public environmental oversight, and in firms that actively disclose 

green information (Chi & Yang, 2023). On a global scale, GF refers to incorporating environmental protection within 

financial organizations' economic strategies, prioritizing investments in environmental protection initiatives to endorse the 

transition to a green economy (Zheng et al., 2021). Green banking is also social or responsible (Park & Kim, 2020). GF 

highlights the social responsibilities of banks in promoting environmental sustainability. This underscores the frequent 

intersection between social and environmental concerns. 

Economic growth is significantly influenced by the financial performance of financial institutions, especially 

within the banking sector, attracting attention from regulators and scholars. Investments in green sectors, the extension of 

green credit, energy accounting practices, and fostering creativity have been positively connected to the financial 

performance of banks in developing nations (Banani & Sunarko, 2022). Guan et al. (2017) highlight green loans elevating 

credit risk. Qian and Yu (2024) focus on connecting GF and ESG performance. While GF policies can alleviate financing 

constraints on green innovation in general, privately owned enterprises are less likely to have access to green credits (Yu et 

al., 2021). Various factors, such as fossil fuel imports, chemical usage, green funding, and nuclear energy demand, can 

impact regional environmental quality. Because climate risks directly affect central banks' core activities, financial 

institutions must integrate climate-related physical and transition risks into their policy frameworks to safeguard macro-

financial stability (Dikau & Volz, 2021). Additionally, a study shows that government investments in human capital and 

renewable energy contribute to a profitable green economy through advancements in labor and technology, albeit with 

varying impacts across nations (Feng et al., 2022)—financial performance bridges between economic development and 

green finance. Green investment contributes to enhancing financial performance through the advancement of green energy. 

The banks ' green finance and financial performance call for emerging research issues. The relationship between 

green banking and banks' environmental performance is mediated by green financing (Zhang et al., 2022). However, this 

study neglects the linkage between Environmental and Social Risk Management (ESRM) and bank performance. There is 

a positive relationship between green banking practices and the financial performance of banks (Hossain et al., 2020). 

Investing in green sectors that mitigate the adverse effects of business activities on air quality, natural resources, and 

biodiversity enhances corporate reputation and supports sustainable financial performance (Banani & Sunarko, 2022). Green 

investment significantly impacts banks' environmental performance (Ngwenya & Simatele, 2020). Despite the opposite 

correlation between social stability and financial performance, the limited sample size of banks across 21 countries and the 

lack of consideration for banking operations' typical stability restricts the generalizability of these findings (Liu et al., 2021). 

Banks’ green performance is connected to environmental performance (Taneja & Özen, 2023). Firm performance is assessed 

using financial and productivity metrics, yet these indicators are insufficient for sustainable performance (Tangen, 2004). A 

rapid and transformative change toward sustainability requires a financial system that serves as the primary funding source 

for this transition (Esposito et al., 2022). Standardized credit risk assessment is particularly vital for banking sectors in 

developed economies, especially for banks with portfolios heavily dependent on externally rated exposures. On the other 

hand, smaller and transitioning economies have historically encountered externally rated exposures later than their 

developed counterparts (Milojević & Redžepagić, 2020). Financial performance helps to maintain the sustainability of the 

development and developing economy. Effective management of environmental and social exposures contributes to 

enhancing financial performance. 

Risk management challenges in the banking sectors impact bank performance and influence national economic 

growth and business development. Green banking practices have improved environmental performance by reducing 

negative environmental impacts from routine operations, such as minimizing paper usage and reducing energy consumption, 

fuel usage, and emissions (Shaumya & Arulrajah, 2017).  Yang et al. (2020) highlight the relationship between 

environmental outcomes and the financial system. Green regulations moderate and strengthen the connection between green 

financing and investments in renewable energy (Li et al., 2022). GF is a crucial intermediary linking corporate social 

responsibility with environmental performance, demonstrating a notable linkage between GF and environmental 



Siddikee et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 34-45

 

36 

performance (Dai et al., 2022). Additionally, a meaningful relationship exists between bank performance and risk 

management practices (Adeusi et al., 2014). Strengthening capital and risk management practices further incentivizes banks 

to ensure financial steadiness (Milojević & Redžepagić, 2020). Banks engage in risk management to mitigate potential 

adverse effects on their performance. 

Encouraging green finance within the banking sector incentivizes businesses to opt for eco-friendly loans. Yan and 

Gong (2024) find that while green lending and investments enhance credit risk profiles, associated risks can influence their 

profitability. Xi et al. (2021) argue that GF positively influences green performance by protecting and restoring ecosystems. 

Consistent with prior studies, a notable negative correlation exists between risk and profitability (measured by ROA and 

ROE), aligning with findings from (Kwan & Eisenbeis, 1997). Shaumya and Arulrajah (2017) find that green banking 

practices alleviate adverse environmental effects, which, in turn, influence financial performance. Stock prices also reflect 

these financial risks (Husna & Satria, 2019). A proposed dividend increase is anticipated to positively affect stock prices, 

significantly influencing the firm's overall value (Crane et al., 2016). Although beneficial to multiple stakeholders, 

environmentally friendly finance raises concerns regarding the long-term sustainability of banking operations due to not 

handling green credit effectively, as it affects both credit risk and profitability. Purposely, this study investigates the impact 

of GF on ESRM and the impact of ESRM on profitability and non-performing loans. Therefore, this study considers the 

following hypotheses. 

                  H1: GF influences significantly ESRM 

                  H2: ESRM has a significant impact on return on asset (ROA) 

                  H3: ESRM has a significant impact on return on equity (ROE) 

                  H4: ESRM has a significant impact on the expenditure-income ratio (EIR)      

                  H5: ESRM influences significantly on non-performing loans (NPL)    

The conceptual framework of this study is presented in Figure 1 below. 

                                                                                                                             Performance 

 

                                                                                                  

 

                                                                                                    

 

  

 

 

Figure 1. Conceptual Framework and Hypotheses 

MATERIALS AND METHODS 

The selection of samples for this research was based on data obtained from the annual report of Bangladesh Bank. The 

dataset covers various categories of banks, including state-owned commercial banks (SCBs), specialized banks (SBs), 

private commercial banks (PCBs), and foreign commercial banks (FCBs). The study employs balanced panel data, covering 

four different categories of banks: six SCBs, three SBs, forty-three PCBs, and nine FCBs. The research spans nine (9) years, 

from 2015 to 2023. The study categorizes its variables into exogenous (independent) and endogenous (dependent) variables. 

The independent variables are comprised of alternative energy (AE), efficient energy (EE), renewable energy (RE), wastage 

management (WM), recycling and manufacturing of recyclable goods (RMRG), green industry and establishment (GIE), 

environmentally friendly brick production (EFBP), and green other factor investment (GOFI). On the other hand, the 

dependent variables consist of ROA, ROE, EIR, and NPL. Moreover, ESRM is a dependent variable when GF is the 

independent variable, and ESRM is an independent variable when the performance variables are dependent. These 

classifications aim to examine the impact of green banking initiatives on ESRM as well as to investigate the impact of 

ESRM on performance. A detailed overview of such variables is presented in Table 1. 

Table 1. Variable Clarification 

Var. Sectors of green 

investment 

Explanation 

 

 

 

 

 

 

 

RE Renewable energy harnesses solar, wind, and hydropower to engender sustainable electricity. As crucial 

contributors to economic development, these energy sources significantly mitigate environmental issues and 
promote sustainability (Shinwari et al., 2022). 

EE Efficient energy enhances energy efficiency to lessen waste, support sustainability, and boost output. Energy 

efficiency involves achieving an identical output level while consuming less energy (Dunlop, 2022). 
AE 

 

 In contemporary society, energy improvement increasingly emphasizes alternative sources, strongly focusing 

on solar and wind energy (Baitanayeva et al., 2020). 

 
ESRM 

  

ROA 

 

ROE 

EIR 

H2 

H3 
Green 

Finance (GF) 

 

NPL 

H4 

H5 

H1 



Siddikee et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 34-45

 

37 

 

 

     GF 

  

  

WM  Waste management involves the systematic processes of gathering, moving, and removing waste materials to 

reduce waste through proper handling and disposal to ensure environmental safety and cleanliness (Panchal et 

al., 2021). 

RMRG  Recycling involves collecting packaging waste and converting it into new items by reprocessing the materials, 
which helps reduce the need for raw resources and minimizes waste by reusing materials to create different 

products (Filyaw, 2022).  

GIE The green industry encompasses more than just environmentally friendly industrial growth; it includes 
adopting comprehensive, sustainable, and optimized industrial systems that uphold efficiency and 

effectiveness in the long term (Aryani Siregar et al., 2020). 

EFBP Eco-bricks provide supplementary advantages, including enhanced thermal and sound insulation, increased 
durability, and excellent recyclability, making them an environmentally friendly option with long-lasting 

benefits. (Jha & Kewate, 2024) 

GOFI This sector encompasses manufacturing, factory safety, and other environmentally friendly investments. 
Ensuring safety in production also requires implementing both technological and managerial approaches 

(Pačaiová et al., 2024). 

   

 

Profitability 

 

ROA Return on Assets (ROA) is often used to measure the rate of return on total assets. Return on Assets (ROA) 
measures a company's efficiency in generating profit from the assets it utilizes or invests in over a given period 

(Indriani et al., 2022).   

ROE ROE can provide a general idea of a company's financial performance (Fauzi & Nurasik, 2023). 

EIR EIR is the proportion of expenditure and income. Expenditure and income budget include revenue and 
expenditure.  

Non 

performing 

loan 

NPL A loan is considered non-performing when payments are overdue by 90 days or more, indicating a higher risk 

of default within the following years. NPLs are a critical indicator of loan risk, highlighting the chance of 

repayment failure (Alnabulsi et al., 2023). 

 

Model Selection and Methods 

A unit root test validated the econometric analysis to ensure the data were stationary or non-stationary. This study employed 

several statistical methods to analyze the relationship between GF, ESRM, and performance. Panel Ordinary Least Squares 

(POLS) were chosen to estimate the overall impact of independent variables across entities and time periods without 

accounting for specific individual or time-based characteristics. The decision to select POLS was based on the results of 

collinearity tests, including tolerance levels (TOL) and the variance inflation factor (VIF). Panel Vector Autoregression 

(PVAR) was used to explore the internal effects of variables on performance dynamics. PLS also serves as a foundational 

method for more advanced techniques, such as Fixed Effects Models (FEM) or Random Effects Models (REM). The 

Hausman test and the Redundant Fixed Effect Likelihood LM ratio test results determined the choice of FEM in this study. 

Quantile regression was utilized to understand how GF influences ESRM and the performance across various data 

percentiles. Correlation analysis helped assess the relationship between GF, ESRM, and performance. Finally, the study 

used the Panel Generalized Method of Moments (PGMM) to analyze GF's dynamic and multi-dimensional impact on ESRM 

performance. 

 

Unit Root Test 

The unit root test shows strong evidence against unit roots for all examined indicators. Henry Ntarmah et al. (2019) applied 

the unit root test to evaluate the stationarity of their variables at both the level and first difference. Their findings suggest 

that the data for all indicators are not likely to exhibit non-stationary behavior, implying they behave in a stationary way. 

The equations of the unit root test are as follows- 

 P =  −2 ∑ ln (𝑃 𝑖) 

𝑁

𝑖=1

                                            (1) Z =    
1

 √N 
∑ (𝜋 𝑖) 

𝑁

𝑖=1

                                               (2)  

 

Where 

Pi is the p-value of the unit root test with respect to the ith cross-section. 

N is the number of cross-sections in the panel. 

𝜋 𝑖 is the individual Phillips-Perron test statistic concerning the ith cross-section. 

Panel Ordinary Least Squares (POLS) 

POLS method for regression is used in our panel data. The independent variables are GF and ESRM, and the dependent 

variables are performance. Accordingly, the equation for the Panel Ordinary Least Squares regression is- 

                      ESRMt =  β0 + βi∑GFit  + εit                         (3)                
                      PIit =  β0 +  β1ESRM t   + εit                          (4)                
Where: 

PI: Performance 

PIit and ESRMt are the values of the endogenous variables for the ith observation at time t. 
GFit is the value of the explanatory variable (GF) for the ith observation at time t. 
β0 is the intercept. 
βi is the coefficient of explanatory variables. 
εi is the error term.  



Siddikee et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 34-45

 

38 

Quantile Regression 

This allows us to observe how GF influences not just the central tendency but also the extremes or other specific quantiles 

of ESRM that affect performance, thus offering a deeper insight into the variability and distributional aspects of GF on 

ESRM as well as the ESRM influences on performance. The equations for quantile regression are:  

 

                                 Qτ(ESRM/GF)   = 𝐺𝐹′𝛽τ                                  (5)          
                                 Qτ(PI/ESRM)   = 𝐸𝑆𝑅𝑀′𝛽τ                             (6)           
Where 

Qτ(ESRM/GF) defines 𝜏 th conditional quantile of the response variable ESRM given the predictors GF. 

Qτ(ESRM/GF) defines 𝜏 th conditional quantile of the response variable ESRM given the predictors PI. 

𝐺𝐹′ and ESRM′ represent the transpose of the predictor vector, respectively. 

βτ is the vector of coefficients for the 𝜏th quantile. 

Hausman Test   

The Hausman (1978) test for panel data is a general specification test, where rejecting the null hypothesis indicates model 

misspecification rather than endorsing the fixed effects estimator. On the other hand, not rejecting the null suggests that the 

random effects estimator is efficient (Baltagi, 2024). A spatial Hausman test is recommended to compare fixed and random 

effects models (Mutl & Pfaffermayr, 2011). This helps determine whether a time-varying covariate is exogenous in the 

random effects model for panel data (Mainzer, 2018). The test compares estimators from both the random effects (RE) and 

fixed effects (FE) models. The Hausman test statistic (H) equation is as follows: 

                              H = (�̂� 𝐹𝐸 −  �̂� 𝑅𝐸) ′ [Var(�̂� 𝐹𝐸) −Var(�̂� 𝐹𝐸)] −1(�̂� 𝐹𝐸 −  �̂� 𝑅𝐸)                  (7)  
 Where: 

(�̂� 𝐹𝐸) is the estimated coefficient for the fixed effects model, 

             (�̂� 𝑅𝐸)  is the estimated coefficient for the random effects model, 

             Var(�̂� 𝐹𝐸) and Var( �̂� 𝑅𝐸) are the variances of the coefficients for the respective models. 

Redundant Fixed Effect Likelihood LM Ratio Test 

Likelihood ratio tests for fixed model terms are recommended for analyzing linear mixed models with residual maximum 

likelihood estimation, incorporating Bartlett-type adjustments to the test statistics based on an approximate data 

decomposition (Welham & Thompson, 1997). Additionally, a redundant fixed effect likelihood test (RFELRT) was 

performed to confirm the model specification further. The equation of the redundant fixed effect likelihood LM ratio test 

(REFLRT) is:- 

                             RFELRT = − 2(Log L 𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑) − Log L 𝑢𝑛𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑)                      (8) 
Where: 

log L restricted is the log-likelihood from the restricted model (without fixed effects). 

log Lunrestricted is the log-likelihood from the unrestricted model (with fixed effects). 

RFELRT follows a Chi-square (χ2) distribution with degrees of freedom equal to the number of restrictions.  

Fixed Effect Model   

Fixed effects method with time-fixed effects allows us to control for unobserved entity-specific characteristics, focus on 

within-entity changes, account for time-specific factors, and provide consistent estimates of the relationship between GF & 

ESRM GF & PI, and PI & ESRM.              

                              ESRMit   = α0 +  GFitβ + γit  + εit                              (9) 

                                PLit   = α0 + ESRMit. β + γit  + εit                          (10) 

 

Where  
PIit is the dependent variable for individual 𝑖 at time 𝑡. 

αi represents the individual-specific intercept, also known as the fixed effect. 

GFit is a vector of independent variables for individual 𝑖 at time t. 

β  is a vector of coefficients representing the effect of independent variables on the dependent variable. 

γt  stands for the time-fixed effects, accounting for time-specific influences. 

εit is the error term for the individual 𝑖 at time 𝑡, representing unobserved factors affecting the dependent variable. 

Random Effect Model 

Random Effects Model (REM) is commonly used in econometrics and statistics when dealing with panel or hierarchical 

data structures. The general form of a random effects model can be written as:- 

               ESRM it   = B0 +  GF𝐢𝐭β + ui  + εit                       (11) 

                                                           PIit   = B0 +  ESRMitβ + ui  + εit                             (12) 
Where  

RPit PMit, and SLREAPit are the dependent variables for individual 𝑖 at time 𝑡. 

B represents the individual-specific intercept, also known as the fixed-effect. 

GFit is a vector of independent variables for individual 𝑖 at time t. 

β is a vector of coefficients representing the effect of independent variables on the dependent variable. 

ui is the random effect, capturing unobserved heterogeneity specific to individual i (assumed to be normally distributed. 

εit is the idiosyncratic error term. 



Siddikee et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 34-45

 

39 

Panel Generalized Method of Moments 

PGMM is more flexible and accounts for potential endogeneity (when the independent variable correlates with the error 

term). This makes PGMM useful if GF is suspected to be endogenous.  

                       ESRMt =   α0 +  αi∑GFit  + ηit                         (13)                

                       PIit =   γ0 + γ1ESRM t   + ҽit                           (14)                

Where 

β0, α0,γ0,βi, αi,γi  are the parameters to be estimated 

Moment condition 

         E[Zi (ESRMi -α0 - GFi)=0 

         E[Zi (PIi -γ0 - ESRMi)=0 

Z is the instrument that is correlated with GF and correlated with error terms: ε, η, and ҽ 

 

RESULTS 

Correlation Analysis between GF, ESRM, and Performance   

In the realm of econometrics, it's customary to address heteroscedasticity by using a natural logarithm transformation to the 

variables for analysis, as outlined by Charfeddine and Ben Khediri (2016). The data, after logarithm (log10) transformation, 

were utilized in our analysis. Mean, standard deviations, and the correlation matrix are detailed in Table 2. Furthermore, the 

Variance Inflation Factors (VIF) falls below 5, while the tolerance (TOL) value exceeds 20%, affirming the absence of 

multicollinearity issues among our explanatory variables. EE has the largest variability (SD: 11.8) while AE has the lowest 

variability (SD: 1.10). ESRM has the highest positive correlation (r=0.816) with RE and the lowest positive correlation 

(r=0.194) with EFBP. Among the independent variables, a positive correlation exists. Table 3 illustrates the correlation 

between ESRM and performance in which ESRM has the highest positive correlation (r=.710) with ROA and negative 

correlation (r= -.72) with EIR. 

 

Table 2. Correlation between GF and ESRM along with collinearity result 

 

Var. MN and SD Correlation Matrix  Collinearity statistics 

MN SD RE EE AE WM RMGM GIE EFBP GOFI ESRM TOL VIF 

RE 7.95 1.10 1.0         .405 2.47 

EE -1.27 11.8 .606 1.0        .456 2.19 

AE -8.06 10.6 .583 .597 1.0       .439 2.28 

WM 2.07 10.8 .556 .430 .456 1.0      .233 4.28 

RMRG 1.25 10.1 .447 .357 .370 .724 1.0     .439 2.28 

GIE 1.96 11.4 .648 .590 .458 .829 .638 1.0    .201 4.98 

EFBP 0.23 11.6 .257 .340 .539 .271 .345 .175 1.0   .585 1.71 

GOFI 7.18 4.24 .139 .328 .328 .41 .392 .429 .378 1.0  .642 1.55 

ESRM 10.86 1.19 .816 .525 .601 .72 .643 .790 .194 .210 1.00 1.00 1.00 

Note: MN means mean; SD stands for standard deviations; TOL and VIF are tolerance and variance influence factor, respectively. 

Table 3. Correlation between ESRM and performance 

 

Var. MN and SD Correlation matrix 

MN SD ROA ROE EIR NPL ESRM 

ROA .0003 .019 1.0     

ROE .003 0.14 .838 1.0    

EIR .88 0.40 -.96 -.69 1.0   

NPL 11.1 .570 -.09 -.15 -.04 1.0  

ESRM 10.86 1.19 .710 .668 -.72 .440 1.0 

 

Results of Unit Root Test  

Table 4 shows the unit root test results of GF, ESRM, and performance. The analysis is based on the second difference, 

accounting for the individual effects of exogenous variables, and applies the Newey-West automatic bandwidth selection 

with the Bartlett kernel system. The PP Fisher chi-square test statistics range from 16 to 60.11, with p-values between 0.0000 

and 0.015, strongly rejecting the null hypothesis of unit roots. Likewise, the PP Choi Z test statistics range from -6.48 to -

2.11, with p-values below 0.05, confirming stationarity across all indicators. 

 

Table 4. Individual unit root process 

 
Indicators PP Fisher Chi-square            P-value.               PP Choi Z stat                  P-value 

AE 31.869                     0.0000*** -4.733                    0.0000*** 

EE 25.502                     0.0013*** -3.270                    0.0005*** 

RE 41.439                     0.0000*** -4.629                    0.0000*** 

WM 28.489                     0.0004*** -2.814                    0.0024*** 

RMRG 46.227                     0.0000*** -4.878                    0.0000*** 

GIE 52.939                     0.0000*** -6.229                    0.0000*** 



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EFBP 20.974                     0.0019*** -2.999                    0.0014*** 

GOFI 33.441                     0.0001*** -3.833                    0.0001*** 

ESRM 18.254                     0.0194** -2.429                     0.0080*** 

ROA 51.7830                   0.0001*** -5.877                     0.0000*** 

ROE 60.1057                   0.0000*** -6.477                     0.0000*** 

EIR 39.3571                   0.0000*** -4.664                    0.0000*** 

NPL 30.9200                   0.0000*** -3.780                    0.0000*** 

‘***’ ‘**’ and ‘*’ signify the significance at the 1%, at the 5 % and at the 10% level respectively. 

 

The Effect of GF on ESRM Influencing Performance Based on Mean 

POLS has been used to identify the effect of GF on ESRM based on the mean distribution of ESRM and to examine the 

impact of ESRM on performance-ROA, ROE, EIR, and NPL based on the mean distribution of these dependent variables. 

Table 5 depicts that the significant factors containing t value greater than ±2.11 at the 5% level of significance and  p values 

p≤ 0.01 or p≤ 0.01 or p<≤0.10 at the 0.01 or at the 0.05 or the 0.10 level have ensured those findings. Accordingly, RE, AE, 

RMRG, and GIE have a significant positive impact on ESRM. Moreover, ESRM significantly impacts ROA, ROE, and EIR 

at the 0.01 level.  

 

Table 5. Impact of GF on ESRM on performance 

 
DV IV t-statistic p-value IV DV t-statistic p-value 

 
 

 

ESRM 

RE 3.744 .001*** 

ESRM 

ROA 5.884 .000*** 

EE -1.112 .276 ROE 5.240 .000*** 

AE 2.341 .027** EIR -6.030 .000*** 

WM .139 .890 NPL 2.853 .007*** 

RMRG 2.113 .044** - - - 

GIE 2.167 .039** - - - 

EFBP -1.329 .195 - - - 

GOFI -1.117 .274 - - - 

Note: DV stands for the dependent variable, and IV stands for the independent variable. 
 

Conditional effect of GF on ESRM influencing performance 

Quantile regression allows for modeling different conditional quantiles of GF and ESRM indicators. Accordingly, in the 

quartile regression, this section presents the impact of GF on ESRM on performance across the distribution's Q1, Q2, and 

Q3. Table 6 displays the t-statistic (t>±2.230), which provides the significant impact of GF on ESRM performance. 

Furthermore, the p-value confirmed that it was a significant finding. As a result, RE positively influences ESRM at the 0.01 

level of significance. Furthermore, ESRM as a mediating factor influences ROA, ROE, EIR, and NPL at the 1% significance 

level.   

 

 Table 6. Conditional impact of GF on ESRM having impact on performance 

 

IV DV 
                Ist Q                 2nd Q                 3rd Q 

   t-statistic   p-value t-statistic   p-value   t-statistic p-value 

AE  0.466 0.645 -0.320 0.751 -1.256 0.220 

EE  -1.285 0.209 -0.916 0.368 -0.569 0.574 

RE  59.378 0.000*** 37.799 0.000*** 36.664 0.000*** 

WM ESRM -0.026 0.980 -0.415 0.682 0.285 0.778 

RMRG  1.093 0.284 0.529 0.601 0.459 0.650 

GIE  -0.232 0.819 0.662 0.514 0.415 0.681 

EFBP  0.562 0.579 0.241 0.811       -0.199 0.848 

GOFI  -0.152 0.880 0.166          0.870 0.717 0.480 

 ROA -1.469 0.151 1.167 0.251 3.222 0.003*** 

 ROE 3.772 0.001*** 2.230 0.033** 8.853 0.000*** 

 EIR 6.010 0.000*** 6.628 0.000*** 3.772 0.001*** 

 NPL 39.331 0.000*** 34.345 0.000*** 26.857 0.000*** 

               ‘***’ ‘**’ and ‘*’ signify the significance at the 1%, at the 5 % and at the 10% level respectively. 

 

Result of the Hausman Test and Redundant Fixed Effect Likelihood LM Ratio Test 

Table 7 illustrates the results of the Hausman test in that Chi-Square statistics in the corresponding p-value specifies the 

selection of REM because, at the 1% level, this study rejects the alternative hypothesis.  Besides, a redundant fixed effect 

likelihood LM ratio test (RFELT) was done to confirm the model specification. Table 8 presents the results of RFELRT 

where the p-values of all dependent variables are 0.000, which means FEM is appropriate.      

 

Table 7. Hausman test result of ESRM and performance 

 
Variables Chi-square statistics p-value Model specification 

ESRM 2.724 0.951 Random Effect Model (REM) 

ROA 0.569 0.451 REM 

ROE 0.112 0.738 REM 

EIR 1.315 0.252 REM 



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41 

Variables Chi-square statistics p-value Model specification 

NPL 0.036 0.849 REM 

‘***’ ‘**’ and ‘*’ signify the significance at the 1%, at the 5 % and at the 10% level respectively. 

 

Table 8.  Redundant fixed effect likelihood ratio test result 

 
             Variables                t-Statistic                      p-value Model specification 

ESRM 70.626 0.000*** FEM 

ROA 76.060 0.000*** FEM 

ROE 40.889 0.000*** FEM 

EIR 79.851 0.000*** FEM 

 NPL 133.199 0.000*** FEM 

‘***’ ‘**’ and ‘*’ signify the significance at the 1%, at the 5 % and at the 10% level respectively. 

 

Fixed and Random Effect of GF on ESRM Influencing ESRM 

After the Hausman test and RFELT, the effect of GF on ESRM that influences the performance of banks is determined. 

Table 9 shows the findings of the fixed and random effects of GF on ESRM performance. Here, the researcher finds that 

the significant factors contain a t-value greater than ±2.00 at the 10 % significance level, more significant than ±2.17 at the 

5 % significance level, and greater than ±2.96 at the 1 % significance level. Finally, p-value p≤ 0.01 or p≤ 0.01 or p<≤0.10 

at the 0.01 or the 0.05 or the 0.10 level have ensured those findings. Accordingly, EE has a significant fixed effect at the 

0.10 level, RMRG at the 0.05 level, and EFBP at the 0.1 level on ESRM. Besides, RE and RMGM have random effects on 

ESRM at .01 and .10 levels. Moreover, ESRM has a significant random effect on profitability and non-performing loan-

ROA, ROE EIR, and NPL at 0.010 level.  

 

Table 9. Fixed and random effect of GF on ESRM influencing performance 

 

DV IV 
FEM REM 

IV DV 
FEM REM  

t-statistic p-value t-statistic p-value t-statistic p-value t-statistic p-value 

 

 
 

ESRM 

RE 0.321 0.752 2.647 .030** 

ESRM 

ROA -0.891 0.382 8.770 0.000*** 

EE -2.960 .009*** -.819 .437 ROE -0.651 0.522 9.331 0.000*** 

AE 0.989 0.337 1.543 .161 EIR -0.621 0.540 -8.920 0.000*** 

WM -1.033 0.317 .143 .590 NPL 1.247 0.225 7.333 0.000*** 

RMRG 2.175 0.045** 2.235 .056 - - -   

GIE 1.690 0.110 1.434 .190 - - -   

EFBP -2.002 0.0625* -1.024 .336 - - -   

GOFI -0.684 0.504 -1.105 .301 - - -   

‘***’ ‘**’ and ‘*’ signify the significance at the 1%, at the 5 %, and at the 10% level respectively. 

 

Dynamic Effect of GF on ESRM Influencing Performance  

PGMM has been used to determine the dynamic effect of GF on ESRM that influences the performance of banks. Table 10 

shows the results of the dynamic effect of GF on ESRM and the dynamic effect of ESRM on performance. Here, the 

researcher finds that the significant factors contain a t-value greater than ±10.00 at the 1 % significance level. Finally, p-

value p≤ 0.01 at the 0.01 has ensured those findings. Accordingly, AE, RE, and GOFI significantly affect ESRM at the 

0.010 level. Moreover, ESRM significantly affects ROA, ROE, EIR, and   NPL at the 0.01 level. 

 

Table 10. Dynamic effect of GF on ESRM on performance 

 

DV IV t-statistic p-value IV DV t-statistic p-value 

 

 

 

ESRM 

RE 74.699 0.000*** 

ESRM 

ROA 11.173 0.000*** 

EE -0.640 0.527 ROE 2.997 0.005*** 

AE 4.428 0.001*** EIR 3.159 0.003*** 

WM -1.167 0.251  NPL 61.070 0.000*** 

RMRG 0.687 0.497 - - - 

GIE 1.045 0.303 - - - 

EFBP 0.119 0.906 - - - 

GOFI 10.393 0.000*** - - - 

                         ‘***’ ‘**’ and ‘*’ signify the significance at the 1%, at the 5 %, and at the 10% level respectively. 

 

Discussion of GF, ESRM, and Performance 

The findings of this study align closely with the evolving discourse in the literature regarding the ESRM frameworks within 

financial institutions and their broader implications for sustainability, economic performance, and risk management. The 

POLS method, quantile method, EFM, and PGMM approaches collectively elucidate the nuanced relationships between 

ESRM and various financial indicators. Zhang et al. (2022) emphasize the positive effects of environmentally friendly 

schemes within the banking sector, demonstrating significant improvements in environmental performance and green 

financing development. The findings in this study echo these insights by revealing ESRM's significantly positive impact on 

variables such as ROA, ROE, and EIR.   

The quantile method findings, which show ESRM as a mediator influencing variables like ROA, ROE, and other 

performance indicators across multiple quantiles, align with the results (Mavlutova et al., 2023). Their cluster analysis 



Siddikee et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(1) (2025), 34-45

 

42 

revealed a positive correlation between economic performance and overall ESG (Environmental, Social, and Governance) 

risk across European OECD nations. This underscores the growing recognition of ESG factors as pivotal drivers of financial 

performance. Yang et al. (2020) identified the adverse effects of financial instability on carbon emissions in developing 

economies, highlighting the intricate relationship between financial systems and environmental outcomes. Similarly, this 

study demonstrates ESRM's role in mitigating adverse impacts on variables like ANPL and NPLTLR while positively 

influencing green investment practices. This suggests that robust ESRM practices can address systemic risks, improve 

financial stability, and contribute to environmental sustainability goals. 

Qian and Yu (2024) find that GF policies positively impact ESG performance, highlighting the broader societal 

and environmental benefits of aligning the financial system. This resonates with the study’s EFM method findings, which 

indicate that variables such as RMGM and EFBP significantly and positively influence ESRM, leading to enhanced 

performance in areas like TA and sustainable investment programs. 

Xi et al. (2021) GF schemes safeguard ecosystems and support RE implementation, which directly correlates with 

ESRM. These outcomes emphasize the transformative potential of ESRM in fostering RE adoption and ecological 

preservation, thereby advancing financial and environmental objectives. Ngwenya and Simatele (2020) highlight the 

significant influence of green lending on banks' environmental performance. This theme parallels this study's findings on 

ESRM's role in driving positive outcomes in sustainable lending programs. The lagged variable impacts identified in the 

PVAR method, such as the influence of GF on other performance metrics, underscore the dynamic connection between 

green finance and financial performance. Interestingly, Guan et al. (2017) point out the dual role of carbon intensity loans 

(CIL) in meeting societal emission reduction goals while elevating credit risk. This aligns with the study's findings that 

ESRM negatively affects variables like NPL.  

Additionally, the PGMM results demonstrate that ESRM exerts significant dynamic effects on a wide range of 

performance indicators, ROA, and ROE, substantiating its key role in integrating sustainability into financial systems.    

 

CONCLUSIONS 

This study aims to examine ESRM's impact on banks' performance. The POLS method reveals that ESRM has a significantly 

positive impact on variables such as TA, DA, SIA, SID, ROA, ROE, and NPL, while it negatively affects EIR. In the 

quantile method, RE shows a positive influence on ESRM. Additionally, ESRM acts as a mediator, significantly influencing 

ROA, ROE, EIR, and NPL. The EFM and REM indicate that EE, RE, RMGM, and EFBP have significant effects on ESRM 

that have a significant random effect on performance.   

The PGMM method shows that AE, RE, and GOFI have significant multi-effects on ESRM. Furthermore, ESRM 

exerts a significant dynamic effect on ROA, ROE, EIR, and NPL. 

The findings significantly contribute to the theoretical understanding of ESRM. The positive impact of ESRM on 

variables such as NPL challenges existing frameworks by suggesting that effective ESRM practices can drive financial 

performance and steadiness. Furthermore, the mediating role of ESRM across different quartiles supports the idea that it 

might influence financial outcomes differently across various levels of company performance. The negative connection with 

EIR and positive connection with NPL add a nuanced layer to understanding how ESRM can impact financial risk, offering 

new avenues for future research on the link between sustainability practices and risk management. 

The results underline the importance of integrating ESRM practices into organizational strategies for managers. 

The significant positive effects on financial indicators like ROA, ROE, and NPL imply that improving ESRM can lead to 

better profitability and risk management. The adverse effects of EIR suggest that overemphasis on certain ESRM practices 

may result in financial efficiencies. However, managers should aim for a balanced approach, leveraging ESRM to enhance 

long-term value while mitigating potential drawbacks. 

Companies should consider incorporating ESRM practices to force performance and sustainability. The mediation 

effects emphasize that ESRM influences key financial outcomes, such as ROE and ROA, emphasizing its role in shaping 

long-term organizational success. The identified relationships between ESRM and financial indicators indicate that 

implementing robust ESRM strategies could enhance financial stability and operational efficiency, particularly in 

performance matrix- profitability and NPL. 

             This study focuses on some restrictions that must be addressed in future studies. First, the study relies heavily on 

secondary data, which may not entirely confine the complexity of GF and its effects on future research performance. It could 

promote primary data collection through surveys or interviews to add deeper insights. Second, the study is limited by the 

data's time frame and geographical span and suffers from long-term impacts. Future studies should investigate the extensive 

effects of GF over longer periods and across different regions to assess its global applicability. Furthermore, the study 

focuses primarily on financial institutions, leaving out other industries that could also benefit from the GF system. Future 

research should inspect the impact of GF on sectors beyond banking. 
 

 

Author Contribution: Conceptualization, M.J.A.S., AHM.Z.H.; Methodology, M.J.A.S., AHM.Z.H., S.P., H.B. and R.F.; Software, M.J.A.S., Validation, 
M.J.A.S., AHM.Z.H., S.P., H.B. and R.F.; Formal analysis, M.J.A.S., AHM.Z.H., S.P., H.B., and R.F.; Investigation, M.J.A.S., AHM.Z.H., S.P., H.B. and 

R.F.; Data Curation, M.J.A.S., AHM.Z.H., S.P., H.B. and R.F.; Writing-Original draft preparation, M.J.A.S., AHM.Z.H., S.P., H.B. and R.F.; Writing- 

Review and Editing, M.J.A.S., AHM.Z.H., S.P., H.B. and R.F.; Supervision, M.J.A.S. and AHM.Z.H., Funding acquisition, M.J.A.S., AHM.Z.H., S.P., 
H.B. and R.F.  Authors have read and agreed with the published version of the manuscript.  

Institutional Review Board Statement: Ethical review and approval were waived for this study because the researcher does not involve vulnerable groups 

or sensitive issues. 
Funding: Authors received no funding for this research. 

Acknowledgment: Not Applicable. 



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Data Availability Statement:  Data presented in this study are available at the request of corresponding authors, and data are not publicly available due 

to restrictions. 

Conflict of interest: Authors declare no conflict of interest. 

 

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