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1 

 

                         FINANCE AND BANKING 
                                                             IJFB VOL 15 NO 2 (2025) P-ISSN 2574-6081  E-ISSN 2574-609X     

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

               Published by American Finance & Banking Society, USA 

GREEN BANKING INITIATIVES FOR SUSTAINABLE 

DEVELOPMENT: PRACTICES AND PERFORMANCE OF 

COMMERCIAL BANKS                                                               

        
 Shaifali Mathur (a)1   Sakshee Lakhani (b)   

 

(a)Sr. Assistant Professor, Department of Financial Studies, IIS (Deemed to be University), Jaipur, India; E-mail: shaifali.mathur@iisuniv.ac.in 
(b)Student, M. Com, Ramjas College, University of Delhi, India; E-mail: saksheelakhani@gmail.com 
 

 
A R T I C L E I N F O 
 

 

Article History: 
 

Received: 20th March 2025 

Reviewed & Revised: 20th March 
to 20th August 2025 

Accepted: 25th August 2025 

Published: 30th August 2025 

 
Keywords: 
 

Green Banking, Banks’ Environmental Performance,  

Green Banking Practices, Sustainability,  

Sustainable Development 

 
JEL Classification Codes: 

 

G21, G41, E44 

 

Peer-Review Model:  

 

External peer review was done through  
double-blind method. 
 

 
  

 
A B S T R A C T 

 
Concerns about environmental sustainability and climate change have prompted commercial banks to 

adopt green initiatives, such as dedicated "Green Funds" and eco-friendly products, to support 

sustainable development. Regulatory frameworks and stakeholder pressures are driving banks to 

integrate environmental responsibility and corporate social responsibility into their operations. Green 

banking is defined as integrating environmentally friendly practices, such as sustainable lending, 

carbon footprint reduction, and financing for renewable energy, into banking operations. Standard 

practices include funding eco-friendly projects and minimizing the bank's carbon footprint. These 
initiatives aim to align financial activities with ecological conservation and long-term economic 

stability. This study examines green banking practices across 10 commercial banks (5 public, 5 private) 

in Jaipur, India, focusing on their impact on banks' environmental Performance. A survey using 

structured questionnaires collected data from 161 bank employees (including branch managers, 

branch heads, relationship managers, and assistant managers) across all ten banks. Subsequently, 

correlation and regression analysis were conducted to assess the relationship between green practices 

and environmental performance indicators. The results reveal a statistically significant and positive 

impact of green banking on the environmental Performance of both public and private sector banks. 
Quantitatively, the regression model explains about 71.3% of the Variance in the ecological 

performance measure (R² =0.713), indicating that sustainability-oriented practices substantially 

improve outcomes. The major finding is that green banking initiatives alone account for roughly 71.3% 

of the variation in banks' environmental outcomes in this sample. Overall, these results highlight the 

strong empirical association between banks' green initiatives and their environmental Performance. 

 
 

© 2025 by the authors. Licensee American Finance & Banking Society, USA. This article is an open-

access article distributed under the terms and conditions of the Creative Commons Attribution (CC 

BY) license (http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

The growing global emphasis on environmental sustainability has prompted commercial banks to adopt green banking 

initiatives as a strategic approach to support sustainable development (Jeucken, 2010). Green banking refers to integrating 

environmentally friendly practices into banking operations, including sustainable lending, reducing carbon footprints, and 

investing in renewable energy projects (Bahl, 2012). These initiatives aim to align financial activities with ecological 

conservation while promoting long-term economic stability (Scholtens, 2017). 

Green banking, an evolving paradigm in the financial sector, has garnered increasing attention for its potential to 

align banking operations with environmental sustainability (Shaumya & Arulrajah, 2017). It integrates environmental and 

social considerations into core banking functions to protect the environment and conserve natural resources (Grover & Kaur, 

2019). These practices include funding eco-friendly projects, reducing banks' carbon footprint, and raising environmental 

awareness among customers and employees (Kumar et al., 2021). 

This transition is driven by rising concerns about climate change and the growing need for ecological balance, 

compelling banks to shift from traditional practices to environmentally responsible alternatives (Wijethunga & Dayaratne, 

2018). Commercial banks are now launching “Green Funds” for projects addressing social and environmental issues, 

reflecting a significant shift in priorities (Prabhu & Aithal, 2021). As environmental concerns gain momentum across 

industries, banks are uniquely positioned to influence economic behaviors and drive sustainable development (Sharma & 

Choubey, 2022). 

                                                      
1Corresponding Author: ORCID ID: 0000-0002-5691-4464 

© 2025 by the authors. Hosting by American Finance & Banking Society. Peer review under the responsibility of the American Finance & Banking Society, 

USA. https://doi.org/10.46281/ijfb.v15i2.2716 
 

To cite this article: Mathur, S., & Lakhani, S. (2025). GREEN BANKING INITIATIVES FOR SUSTAINABLE DEVELOPMENT: PRACTICES AND 

PERFORMANCE OF COMMERCIAL BANKS. Indian Journal of Finance and Banking, 15(2), 1-11. https://doi.org/10.46281/ijfb.v15i2.2716 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/ijfb.v15i2.2716
https://orcid.org/0000-0002-5691-4464
https://orcid.org/0009-0002-1998-3353


Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

2 

The role of commercial banks in fostering sustainable development has gained significant attention, particularly 

due to increasing regulatory pressures and growing stakeholder demands for corporate social responsibility (CSR) (Weber, 

2016). By incorporating environmental risk assessments and offering green financial products, banks can significantly 

contribute to achieving the United Nations' Sustainable Development Goals (SDGs), especially SDG 7 (Affordable and 

Clean Energy) and SDG 13 (Climate Action) (Krstić, 2013). 

Environmental Performance in the banking sector is intricately linked to the adoption of green banking practices. 

These initiatives are now key performance indicators as banks are evaluated not just on financial metrics but also on 

environmental stewardship (Sharma & Choubey, 2022; Prabhu & Aithal, 2021). Green banking encompasses a wide range 

of strategies, including eco-friendly operations, green product development, and environmental risk management, all aimed 

at reducing the sector's ecological footprint (Muchiri et al., 2025). 

By strategically integrating these green practices into their operational frameworks, commercial banks can mitigate 

their environmental impact while fostering a culture of sustainability throughout the value chain. Increasingly, banks are 

reforming their core functions to align with global sustainability standards, reducing carbon emissions and actively 

contributing to climate solutions. 

This research has significant value in promoting sustainable development within the banking sector. It seeks to 

explore the variety of green banking practices adopted by selected public and private-sector commercial banks and to assess 

their impact on the banks' environmental Performance. The primary objective is to evaluate the extent to which green 

principles are integrated into day-to-day banking operations and whether these efforts translate into measurable 

improvements in environmental outcomes. 

By evaluating and comparing the adoption of green banking practices across public and private banks, the study 

provides critical insights into how financial institutions can contribute to environmental sustainability. The findings 

emphasize the dual role of banks as both financial intermediaries and change agents capable of encouraging eco-friendly 

behaviors among customers and employees. Additionally, the research highlights the need to institutionalize sustainability 

in banking operations, policy frameworks, and employee engagement. It serves as a valuable reference for policymakers, 

banking professionals, and regulatory authorities in designing systems that incentivize green practices and foster 

environmentally responsible banking.   

The study aims to examine the green banking initiatives implemented by banks and assess how these initiatives 

enhance their environmental Performance. 

 

LITERATURE REVIEW 

Green banking, also referred to as ethical or sustainable banking, integrates environmental considerations into financial 

operations to minimize ecological footprints while fostering economic growth (Ullah, 2020). The concept has gained traction 

as banks play a pivotal role in promoting sustainability through specialized financial products like green loans and ethical 

investments (Prabhu & Aithal, 2021). Krstić (2013) provides global benchmarks. Research conducted by Wang, Sun, and 

Yu (2023) emphasizes that robust corporate governance enhances firm value when supported by green banking disclosures. 

Several studies have examined the theoretical underpinnings of green banking. Bihari and Pandey (2015) outlined 

a conceptual framework for green banking adoption and identified essential implementation steps. Bihari and Pandey (2015), 

Cholasseri (2016), and Risal and Joshi (2018) establish the foundational components: conceptual frameworks, SWOT 

analyses of green banking products, and empirical links to environmental Performance.   A systematic bibliometric review 

(Goswami, 2024) underscores the need for clear theoretical underpinnings beyond legitimacy and stakeholder theories to 

strengthen future research. 

Similarly, Kala, and Vidyalaya (2020) discussed banking regulations that ensure sustainability across economic, 

environmental, and social dimensions. Kalra (2016) emphasized the banking sector's role in supporting environmentally 

responsible projects in other industries, reinforcing the link between finance and sustainable development. 

Research on the effectiveness of green banking yields mixed findings. Some studies highlight its positive 

environmental impact, while others note implementation challenges. Risal and Joshi (2018) found a statistically significant 

positive relationship between green banking initiatives and banks' environmental Performance in Nepal. Similarly, Shaumya 

and Arulrajah (2017) concluded that sustainable banking practices enhance environmental outcomes. Chen et al. (2022) 

empirically demonstrated that banks' operational policies and green financing significantly improve environmental 

Performance, though employee and customer-related practices had limited effects. The financial impact of green banking 

remains debated. Khanna et al. (2013) found no significant correlation between environmental and financial Performance. 

However, Karim et al. (2020) and Jain and Sharma (2023) reported that green investments enhance profitability and brand 

reputation. Chowdhury (2018) observed that sustainability improves sectoral Performance without necessarily boosting 

individual bank profitability. Sahoo et al. (2016) found younger generations more receptive to green banking. Varghese 

(2018) observed that Indian banks primarily treat green initiatives as CSR rather than core operations. Brar (2016) 

highlighted continued reliance on paper-based promotions despite green commitments. Meena (2013) advocated for stronger 

RBI regulations and incentives, while Jayabal and Soundarya (2016) recommended awareness campaigns to drive adoption. 

Zhang et al. (2022) found that green financing mediates the relationship between banking activities and environmental 

Performance in Bangladesh's private commercial banks. However, low awareness and high operational costs remain barriers. 

Rahal et al. (2023) stressed the need for government-led awareness campaigns in Bangladesh and India. Risal and Joshi 

(2018) confirmed the positive impact of green banking on environmental sustainability, reinforcing the need for policy 

support. Han, Zhang, and Yang (2022) explored how China's green finance reform policies foster green innovation, 

emphasizing the role of regulatory frameworks. Tu and Dung (2017) found low awareness and slow adoption of green 



Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

3 

banking, despite its inclusion in national growth strategies. Sutrisno et al. (2024) highlight how green credit positively 

influences profitability and stability, offering fresh insights into institutional impacts. Han et al. (2022) attribute green 

innovation in banks to robust regulatory frameworks, reinforcing the policy's pivotal role. Studies in Nepal, Bangladesh, 

India, and Vietnam document the uptake of green banking, noting common barriers such as low awareness, high costs, and 

technical constraints (Risal & Joshi, 2018; Zhang et al., 2022; Tu & Dung, 2017). 

Mir and Bhat (2022) observed that there is no universally accepted green banking framework, with adoption 

varying widely across countries. Thomas and Linson (2018) noted a disparity in customer awareness: while digital banking 

is well known, green financial products (e.g., solar ATMs, eco-friendly loans) remain underutilized. Chen et al. (2022) 

confirm that green policies and project financing bolster environmental Performance, whereas Bimha and Nhamo (2017) 

caution against expecting rapid sustainability gains. A global meta-analysis by Goswami (2024) finds a small but positive 

(though statistically insignificant) link between green banking and profitability, with effects influenced by regional context. 

Investigating emerging markets, Mahmud and Lee (2025) show that ATM deployment increases ROE, while board-level 

risk governance enhances net interest margins. In India, employee perspectives significantly shape the implementation of 

green banking (Kumar et al., 2021). 

Cholasseri (2016) conducted a SWOC analysis of green banking products, identifying strengths (e.g., sustainability 

benefits) and challenges (e.g., high costs). Wisetsri et al. (2022) found digital banking tools (mobile banking, e-statements) 

to be the most popular green products, while SMS banking saw minimal usage. Digital tools such as mobile banking and e-

statements are popular eco-practices (Wisetsri et al., 2022), while SMS banking remains underpenetrated. Psychological 

factors (Ahuja, 2015; Habibullah & Natalwala, 2023) and information asymmetry (Mir & Bhat, 2022) strongly affect 

adoption.  

Habibullah and Natalwala (2023) emphasized affective, behavioral, and cognitive dimensions in green banking 

adoption. Mathur and Chaturvedi (2022) found that awareness and perceived benefits significantly influence investor 

willingness to engage with green financial products. Trehan (2015) highlighted the importance of regulatory enforcement 

and government policies in promoting green banking. Zhang (2021) found that environmentally sustainable firms benefit 

from easier access to credit with lower collateral requirements, reinforcing financial incentives for green practices. Customer 

loyalty studies (Dewi & Indudewi, 2024) reveal that green image and trust mediate the relationship between green practices 

and loyalty.  

Climate/environmental risks pose systemic threats: African stress tests reveal fragility in banking systems linked 

to ecosystem loss.  Zheng et al. (2024) show that monetary policy's effects on green and traditional financial markets are 

evolving and increasingly interdependent. The surge in fossil-fuel financing by central global banks highlights the critical 

need for policy oversight to ensure green commitments are meaningful. 

Collectively, these studies underscore the multifaceted nature of green banking, its potential to drive sustainability, 

the barriers to widespread adoption, and the evolving strategies to align financial systems with environmental goals. While 

progress is evident, coordinated efforts among governments, banks, and consumers remain crucial for achieving meaningful 

impact. 

The literature review reveals a need for more comprehensive frameworks and standardized practices in green 

banking, especially in developing countries. Additionally, there is limited research on the long-term financial impacts of 

green banking practices and the effectiveness of government and regulatory interventions in promoting green banking 

initiatives. While numerous studies have examined Green Banking initiatives, particularly from the customer's perspective, 

there remains a significant gap in understanding bankers' viewpoints. It is essential to explore how banking professionals 

perceive and implement Green Banking practices and to assess the impact of these initiatives on overall bank performance. 

Therefore, the research aims to examine environmentally friendly practices implemented through green banking 

and to assess how these practices influence banks' environmental Performance. The following hypotheses are proposed for 

this study:  

 

H0: There is no significant impact of Green banking practices of the banks on their environmental Performance.  

 

Theoretical Framework of the Study 

 

 

 

 

 

 

 

 

Figure 1. Theoretical framework of the study, created by the author 

 

The study's theoretical framework suggests that green banking practices significantly affect a bank's environmental 

Performance. It highlights the cause-and-effect relationship, suggesting that implementing sustainable banking practices 

enhances environmental outcomes and promotes environmentally responsible financial practices, thereby augmenting the 

bank's environmental Performance. 

 

Green Banking 

Practices  

IMPACT 

Bank’s 

Environmental 

Performance



Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

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MATERIALS AND METHODS 

The present study is empirical and aims to examine the perspectives of bankers working in public and private sector banks 

on green banking practices within Jaipur District, Rajasthan. The research seeks to identify and analyze the key factors that 

influence the adoption and implementation of green banking initiatives. 

 

Sample Selection and Size 

A purposive sampling method was adopted to select respondents who hold Officer Grade positions in both public and private 

sector banks. The sample comprises 161 banking professionals, including Branch Managers, Branch Heads, Relationship 

Managers, Assistant Managers, and Probationary Officers, ensuring representation across multiple hierarchical levels and 

departments involved in decision-making for banking operations. 

 

Scope and Coverage 

The study covers 10 leading banks in Jaipur District: 5 private-sector and five public-sector. The selected private sector 

banks include: 

 HDFC Bank 

 ICICI Bank 

 Kotak Mahindra Bank 

 Axis Bank 

 IndusInd Bank 

The selected public sector banks are: 

 State Bank of India (SBI) 

 Punjab National Bank (PNB) 

 Indian Overseas Bank (IOB) 

 Bank of Baroda (BOB) 

 IDBI Bank 

These banks were chosen based on their operational presence, service outreach, and involvement in environmental 

sustainability initiatives. 

 

Data Collection Method 

Primary data was collected through a structured questionnaire administered to targeted banking professionals.  The survey 

instrument was specifically designed to assess three key dimensions: (1) Demographic data of the respondents, (2) 

respondents' awareness, perception, and implementation of green banking practices in their respective institutions, and (3) 

the Banks' environmental Performance. The identified green banking practices were categorized into five distinct 

dimensions: (1) Changing Client's Habits, (2) Technological Changes, (3) Operational Changes, (4) Innovation in Products 

and Services, and (5) Green Engagements. This enables a systematic examination of how each dimension contributes to the 

banks' overall environmental Performance. The study measured banks' environmental Performance using four key 

indicators: (1) minimization of carbon emissions from operations, (2) adoption of green banking policies, (3) reduction in 

energy consumption, and (4) provision of staff training on environmental protection and energy efficiency. To evaluate the 

impact of green banking practices, the mean values of these parameters were aggregated, offering a quantifiable metric of 

sustainability effectiveness. 

 

Statistical Tools and Techniques 

To ensure the instrument's reliability and internal consistency, Cronbach’s Alpha was computed. To uncover latent 

constructs and reduce the data into meaningful factors influencing green banking practices, an Exploratory Factor Analysis 

(EFA) was employed. Additionally, Regression Analysis was applied to examine the relationship between the identified 

factors and the extent of green banking practices followed in the banks. 

 

Exploratory Factor Analysis 

To explore the environmentally friendly practices adopted by various banks under the green banking approach, EFA is 

applied. Exploratory Factor Analysis (EFA) is a method used to simplify the complexity of a study. Its purpose is to condense 

many dimensions or questions into a few labeled components, known as Principal Components. These components capture 

most of the variances present in the study's variables. Another key goal of exploratory factor analysis is to identify distinct 

factors, each comprising questions with similar meanings. These factors represent specific aspects of the study. The 

technique ensures that these factors are not correlated, preventing redundancy and repetition in the analysis. 

 

Measurement of Sample Adequacy and Strength of the relationship among factors 

Table 1. KMO and Bartlett tests 

 

Source: Generated using primary data with the help of SPSS 22.0 software 

KMO and Bartlett's Test 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .717 

Bartlett's Test of Sphericity Approx. Chi-Square 3267.568 

df 160 

Sig. .000 



Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

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The KMO measure assesses whether the data are suitable for factor analysis. It ranges from 0 to 1, and values 

above 0.5 are generally considered acceptable. In this case, Table 1 shows that the KMO value of 0.717, which exceeds 

0.05, is considered sufficient for sample adequacy and subsequent factor analysis, indicating that there are underlying 

relationships among the variables that could be explored. The p-value for Bartlett's Test is very close to 0 (0.000), indicating 

the test is significant. In simpler terms, it suggests that meaningful relationships exist among the variables and that factor 

analysis may be appropriate. 

 

 Table 2. Total Variance Explained 

 
Eigenvalues and Total Variance Explained 

Compon

ent 

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings 

Total % of 
Variance 

Cumulative 
% 

Total % of 
Variance 

Cumulative 
% 

Total % of 
Variance 

Cumulative 
% 

1 9.234 41.974 41.974 9.234 41.974 41.974 4.971 22.596 22.596 

2 2.817 12.804 54.778 2.817 12.804 54.778 3.460 15.728 38.324 

3 1.925 8.748 63.526 1.925 8.748 63.526 3.459 15.724 54.049 

4 1.230 5.591 69.117 1.230 5.591 69.117 2.539 11.540 65.589 

5 1.141 5.185 74.302 1.141 5.185 74.302 1.917 8.713 74.302 

6 1.004 4.563 78.865       

7 .865 3.932 82.797       

8 .613 2.785 85.582       

9 .516 2.345 87.927       

10 .486 2.207 90.134       

11 .408 1.855 91.989       

12 .336 1.528 93.516       

13 .283 1.285 94.801       

14 .255 1.160 95.962       

15 .201 .912 96.874       

16 .192 .871 97.744       

17 .157 .712 98.456       

18 .119 .540 98.997       

19 .084 .380 99.377       

20 .065 .296 99.673       

21 .043 .194 99.867       

22 .029 .133 100.000       

Extraction Method: Principal Component Analysis. 

Source: Generated using primary data with the help of SPSS 22.0 software 

 

Table 2 data represents the results of a Principal Component Analysis (PCA), which helps in understanding the 

underlying patterns in data. Table 2 displays initial eigenvalues, Variance explained, and cumulative Variance at each 

component. It can also be noted that the first factor accounts for 22.596% of the Variance, the second 38.324% cumulative, 

the third 54.049%, the fourth factor 66.589%%, and the fifth factor accounts for 74.302% cumulative variance, i.e, 

cumulative Variance explained by all five factors having Eigen value of more than one is 74.302%. Figure 1 and Table 1.1 

suggest that, before extraction, 22 linear components are identified within the dataset. After extraction, there are 5 distinct 

linear components within the dataset. 

 
Figure 2. Scree Plot 

Source: Scree Plot of Eigenvalues, Created by SPSS 22 

 

The scree plot, Figure 2 displays the eigenvalues of 22 variables, where each eigenvalue represents the Variance 

explained by a corresponding principal component. The plot helps identify the optimal number of components to retain in 

a principal component analysis (PCA). Based on the Scree Plot, it is appropriate to retain the first 5 components, as they 

account for the major Variance in the dataset.  

 



Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

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Table 3. Rotated Component Matrix 

 

 

The rotation in Table 3 above is used to decrease the number of factors on which the variable under investigation 

has high loading. The data represent the results of factor analysis in the context of environmental initiatives within an 

organization. Each row corresponds to a specific environmental variable, with its factor loading (the correlation between 

the variable and the underlying factor). 

The first extracted factor, labeled Changing Client’s Habits, includes variables such as E-statements, Awareness 

programs, and Solar ATMs, which exhibit high communality and factor loadings. This suggests that these initiatives are 

closely linked to a common underlying factor, likely reflecting efforts aimed at enhancing client awareness and promoting 

technological adoption. 

The second factor, Technological Changes, shows that Solar-powered ATMs and the Latest technology have strong 

correlations with the underlying factor, indicating their importance in technological advancements within the organization. 

LEDs and CFLs, while having a relatively lower factor loading, are still somewhat related. 

The third factor, Operational Changes, is characterized by strong loadings from variables such as reducing paper 

wastage, recycling sewage water, and sustainable lending, highlighting their central role in enhancing operational 

sustainability. Sustainable reporting also contributes to this factor, though with a relatively lower loading, suggesting it 

supports these efforts while playing a secondary role. Collectively, these variables reflect a focus on eco-friendly operational 

practices and long-term sustainability initiatives within the organization. 

The fourth Factor Innovation in Products and Services displays that Green ATMs installed strongly align with the 

underlying factor, emphasizing the significance of this innovation. Green loans, Paperless banking, and Green savings, 

bonds, and investments also contribute to the factor with comparatively lower but significant factor loadings. 

Finally, under the fifth factor, that is  Green Engagements, 5 June World Environment Day, and Swacchta drives 

show a strong association with the underlying factor, suggesting their pivotal role in the organization's environmental 

engagement efforts. While No Plastic Days and Carpool Days show somewhat weaker statistical associations, they remain 

relevant components of this sustainability framework. Their inclusion reinforces the organization's holistic approach to 

ecological responsibility, signifying how even smaller-scale initiatives contribute to building a culture of environmental 

consciousness and participatory action among stakeholders. This pattern reflects a strategic multi-tiered engagement model, 

where flagship programs anchor the effort while supplementary activities broaden its reach and impact. 

After factor extraction, Reliability analysis is conducted. 

 

Table 4. Reliability Analysis 

 

Source: Generated using primary data with the help of SPSS 22.0 software 
  

Components 

 1 2 3 4 5 

E statements 0.831     

Awareness programs 0.733     

Solar ATMs 0.89     

Training and services 0.699     

Due diligence 0.662     

Solar-powered ATMs  0.782    

LEDs CFLs  0.502    

Latest technology  0.823    

Organization communication  0.748    

Waste recycling  0.605    

Sustainable reporting   0.689   

Reduce paper wastage   0.821   

E-waste management   0.712   

Recycling sewage water   0.744   

Sustainability lending   0.669   

Green loans    0.545  

Green ATMs installed    0.874  

Paperless banking    0.659  

Green savings, bonds, and investments     0.655 

5 June World Environment Day     0.789 

Swacchta drives     0.631 

No plastic days, Carpool days     0.574 

Extraction Method: Principal Component Analysis.  

Rotation Method: Varimax with Kaiser Normalization. 

Source: Generated using primary data with the help of SPSS 22.0 software 

Reliability Statistics 

Cronbach's Alpha N of Items 

.917 22 



Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

7 

To measure internal consistency, that is, how closely related a set of extracted items is, as a group, Reliability 

analysis is done. The Cronbach's alpha is 0.917, which indicates a high level of internal consistency for our scale with this 

specific sample. 

 

Hypothesis:  
H0 = There is no significant impact of Green banking practices of the banks on their environmental Performance.  

 

The multiple regression analysis is performed to accomplish the study's objective and test the hypothesis under study. 

Various assumptions of regression analysis have been checked. The normality test has been conducted, the data are found 

to be normally distributed, and there is a linear relation between the variables. 

 

Correlation and Regression Analysis 

Regression Equation 

 

Y= a+b1x1+b2x2+b3x3+b4x4+b5x5+e 

 

Where, 

Y (dependent variable) = Banks’ Environmental Performance 

a = constant (intercept) 

X1 = Changing Client’s Habit  

X2 = Technological Changes 

X3 = Operational Changes 

X4 = Green Engagements 

X5 = Innovation in Products and Services 

       e = error term 

       b1, b2 ,b3, b4, b5 = are the coefficient of regression  

 

Table 5. Correlation Analysis 

 
Correlations 

 Banks’ 

Environment

al 

Performance 

Changing 

the Client's 

Habit 

Technolo

gical 

Changes 

Operation

al 

Changes 

Green 

Engage

ments 

Innovat

ion In 

Product

s And 

Services 

Pearson 

Correlation ( r )  

BANKS’ 

ENVIRONMENTA

L PERFORMANCE 

1.000 .547 .743 .807 .648 .624 

Sig. (1-tailed) BANKS’ 

ENVIRONMENTA

L PERFORMANCE 

. .000 .000 .000 .000 .000 

Source: Generated using primary data with the help of SPSS 22.0 software 

 

The result in table  5 shows, the Coefficient of correlation  (r) between the Banks’ Environmental Performance and 

Changing Client’s Habits is 0.547; the Coefficient of correlation between the Banks’ Environmental Performance and 

Technological Changes in the bank is  0.743; the Coefficient of correlation between Banks’ Environmental Performance 

and Banks’ Operational changes is 0.807; Coefficient of correlation between Banks’ Environmental Performance and Green 

Engagements is 0.648 and Coefficient of correlation between Banks’ Environmental Performance and Innovation in the  

Banks’ Products and Services is 0.624, indicating a strong positive correlation between all the green banking practices and 

Banks' environmental Performance with the level of significance at 0.000 (p<0.05) for  Changing Client’s Habits, Banks’ 

Operational changes, Technological Changes in the bank, Green Engagements and Innovation In Banks’ Products and 

Services respectively. 

 

Table 6. Model Summaryb 

 
Model Summaryb 

Model R R Square Adjusted R Square Std. Error of the 

Estimate 

Durbin-Watson 

1 .844a .713 .704 .33385 1.506 

a. Predictors: (Constant), Innovation In Products And Services, Green Engagements, Changing Clients' Habits, Operational Changes, 

Technological Changes 

b. Dependent Variable: BANKS’ ENVIRONMENTAL PERFORMANCE 

Source: Generated using primary data with the help of SPSS 22.0 software 

 



Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

8 

Table 6 shows multiple regression model summaries and overall fit statistics. The analysis revealed an adjusted R2 

of 0.704 with R2 = 0.713, implying 71.3% of the variability in the Banks' environmental Performance is accounted for by 

green banking practices. 

 

To check the fitness of the Model 

Table 7. ANOVA 

 
ANOVAa 

Model Sum of Squares df Mean Square F Sig. 

1 Regression 42.927 5 8.585 77.027 .000b 

Residual 17.276 155 .111   

Total 60.203 160    

a. Dependent Variable: BANKS’ ENVIRONMENTAL PERFORMANCE 

b. Predictors: (Constant), Innovation In Products and Services, Green Engagements, Changing Clients' Habits, Operational Changes, 

Technological Changes 

 

Table 7 shows the overall fitness of the Model. The significant value is 0.000, which is less than the 0.05 level of 

significance. Therefore, the Model is fit for prediction. 

 

Table 8. Coefficients 

 

 

Table 8 shows multiple linear regression estimates, including intercept and significance levels. By taking all the variables 

measuring green banking initiatives in multiple linear regression, we find that Operational Changes and innovations in 

Banking products and services are significant predictors. We can also see that operational changes have a higher impact 

than innovation in products and services by comparing standardized coefficients. 

 

Regression Equation 

Y= a+b1x1+b2x2+b3x3+b4x4+b5x5+e 

 

Banks’ Environmental Performance = 0.130 + (-.127 x Changing Clients habit) + 0.063 x Technological Changes + 0.592x 

Operational Changes + .248x Innovation in products and services + 0.159 x Green Engagements + 0.33385 

 

The present study revealed several important findings regarding the adoption and impact of green banking practices 

among public and private sector banks in Jaipur, Rajasthan. Using exploratory factor analysis, the research identified five 

key dimensions of green banking practices: Changing Clients’ Habits, Technological Changes, and Operational Changes, 

Innovation in Products and Services, and Green Engagements. These dimensions encompass a wide range of eco-friendly 

banking initiatives such as promoting e-statements, using solar-powered ATMs, reducing paper wastage, offering green 

loans, and organizing environmental awareness activities. 

The reliability analysis indicated a high level of internal consistency among the survey items, with a Cronbach's 

Alpha of 0.917. Correlation analysis demonstrated a strong and statistically significant positive relationship between all five 

green banking dimensions and the environmental Performance of banks. Among these, Operational Changes showed the 

strongest correlation with environmental Performance, followed by Technological Changes and Green Engagements. 

Regression analysis confirmed that Operational Changes (β = 0.592) and Innovation in Products and Services (β = 

0.248) were the strongest predictors of environmental Performance. Green Engagements also had a positive and statistically 

significant impact, while Technological Changes and Changing Clients' Habits, although positively correlated, did not 

significantly contribute to the regression model. The overall regression model was found to be statistically significant, 

explaining 71.3% of the Variance in banks' environmental Performance, thus confirming the robustness of the Model. 

Model Unstandardized 

Coefficients 

Standardized 

Coefficients 

t Sig. Collinearity Statistics 

B Std. 

Error 

Beta Tolerance VIF 

1 (Constant) .130 .242  .538 .591   

Changing the 

Client's Habit 

-.122 .064 -.127 -

1.905 

.059 .419 2.387 

Technological 

Changes 

.079 .133 .063 .593 .554 .163 6.134 

Operational 

Changes 

.646 .090 .592 7.204 .000 .274 3.653 

Innovation in 

Products and 
Services 

.195 .053 .248 3.662 .000 .404 2.477 

Green 

Engagements 

.128 .049 .159 2.612 .010 .502 1.991 

a. Dependent Variable:  BANKS’ ENVIRONMENTAL PERFORMANCE 



Mathur & Lakhani, Indian Journal of Finance and Banking 15(2) (2025), 1-11 

 

9 

As a result of these findings, the null hypothesis stating that green banking practices have no significant impact on 

banks' environmental Performance was rejected. The study found that green banking initiatives play a vital role in enhancing 

the environmental sustainability of banking operations.  

 

CONCLUSIONS 

The study seeks to identify the green banking practices adopted by banks and evaluate their effect on improving overall 

environmental Performance. Green banking revolutionizes the financial sector by aligning banks' roles with economic and 

environmental sustainability. It embodies a commitment to fostering sustainable development by aligning banking practices 

with environmental responsibility. Essentially, green banking entails the adoption of inclusive banking strategies geared 

towards ensuring sustainable economic growth. This paradigm emphasizes environmentally friendly industry practices 

within the banking sector, resulting in reduced internal and external carbon footprints. Green banking involves implementing 

eco-conscious measures, such as embracing technological advancements and operational enhancements, and encouraging 

shifts in client behavior within the banking industry. Recent advancements in Indian banking technology have catalyzed a 

transformation from conventional banking methods to a more inclusive approach that prioritizes the interests of customers, 

financial institutions, and the environment. 

Consequently, the primary objective of this study is to assess the impact of Green banking practices on the Banks' 

Environmental Performance. The study involves exploratory factor analysis, Cronbach's alpha, and Regression analysis. 

The present study has confirmed the statistically significant and positive impact of green banking practices on the 

environmental Performance of the public and private sector banks of Jaipur city, explaining 71.3% variation in the dependent 

variable. It can be concluded that Green Banking Initiatives have a significant and positive impact on the Banks' 

Environmental Performance, and the Null Hypothesis has been rejected. The banking industry is making a significant 

contribution to sustainable development. Operational Changes in the bank, like reducing paper wastage, keeping in mind 

environmental sustainability while performing lending activities, and using e-waste management policies, have proved to 

be the most significant predictors of the bank's environmental Performance. 

The research findings highlight the need for policy interventions in the banking sector to enhance environmental 

sustainability. Policymakers should incentivize green banking practices through tax benefits and reduced regulatory 

constraints. Awareness campaigns targeting clients, promoting eco-friendly habits, and encouraging innovation through 

financial incentives are crucial. Furthermore, facilitating knowledge exchange between private and public sector banks via 

capacity-building programs can ensure uniform adoption of green practices, fostering a more sustainable banking sector. 

 

 
Author Contributions: Conceptualization, S.M. and S.L.; Methodology, S.M.; Software, S.M.; Validation, S.M.; Formal Analysis, S.M. and S.L.; 

Investigation, S.M.; Resources, S.M.; Data Curation, S.M.; Writing – Original Draft Preparation, S.M. and S.L.; Writing – Review & Editing, S.M. and 
S.L.; Visualization, S.M.; Supervision, S.M.; Project Administration, S.M.; Funding Acquisition, S.M. and S.L. Authors have read and agreed to the 

published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups 
or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: Not applicable. 
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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