Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 543 Green Banking, Corporate Governance and Performance of Selected SAARC Countries Umara Ikram a , Shahzad Akhtar b a PhD, Scholar Institute of Management Sciences, Bahauddin Zakariya University Multan, Pakistan Email: umarasoe@gmail.com b Assistant professor Institute of Management Sciences, Bahauddin Zakariya University Multan, Pakistan ARTICLE DETAILS ABSTRACT History: Accepted 15 December 2021 Available Online December 2021 This study is designed to estimate impact of green banking disclosure, corporate governance mechanism on performance of listed banks in selected SAARC countries including Pakistan, India, Bangladesh, Sri Lanka and Nepal. With the help of STATA 14.2 this study used PCA (Principal Component Analysis) in addition to content analysis to create green banking disclosure index .For this purpose, central bank’s green banking guidelines are summarized into7 categories and 38 items. Dynamic panel data set (2010-2019) is analyzed by applying system GMM step-one method. The relationships among board independence, board size, female director, institutional ownership, green banking and Tobin’s Q (market value) as performance measure is tested. Institutional ownership and board independence has significant negative impact on market value, green banking does not have any significant impact on market value. On average disclosure practices are different in different categories. Effectiveness of central bank guidelines can be identified at regional level. Results are suggestive that corporate governance mechanism restructuring is needed to increase market value of banks in SAARC countries. To the best of author’s knowledge, this is the very first study which methodologically contributes in the field of green banking disclosure as application of PCA and System GMM step-one. Contextually, one of the most affected area facing higher climate change risk as SAARC region of the world is discussed. Theoretically, study contributes in the theory of change, financial intermediation and agency theory. © 2021 The authors. Published by SPCRD Global Publishing. This is an open access article under the Creative Commons Attribution- NonCommercial 4.0 Keywords: Green Banking, Corporate Governance, Firm Performance JEL Classification: G24, DOI: 10.47067/reads.v7i4.415 Corresponding author’s email address: umarasoe@gmail.com 1. Introduction According to UN agenda 2030 for sustainable development, it is reported that GHG emission levels are increasing. The latest IPCC report (IPCC 2018) declares that human activities are causing global warming which is likely to accelerate further by reaching 1.5 °C. Global climate risk index 2018 Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 544 provides list of countries with long term and short term climate risk. Most affected countries belonging to SAARC regions are Sri Lanka, India, Bangladesh and Pakistan. To reduce negative impacts, central banks, supervisors and policy makers started undertaking various green banking initiatives. Although practices are relatively different between developing and developed countries. At the global level, a network called the Network for Greening the Financial System (NGFS) has also been established by the central banks and regulators to address climate risks. Bangladesh has issued green banking guidelines in year 2011, 2012, 2013. India almost after 2012 all banks are directed to follow green coin rating guidelines. State Bank of Pakistan has announced green banking guidelines in 2017.Corporate governance refers to the rules, regulations procedures and structures by which the affairs of business and institutions are managed and directed, to enhance shareholder’s value through improving corporate accountability and performance while considering the interest of other shareholders (Jenkins on& Mayer, 1992). Alexander (2016) says that there is no universal definition of green banking and it varies across the countries. Park and Kim (2020) declares that green banking term is more similar to ethical banking, social or responsible banking or sustainable banking. Presently, there is need to have a unique, comprehensive measure by which different initiatives regarding green banking practices can be examined in different countries. In addition, there is immense potential to explore unobserved contribution of green banking practices on bank performance. Mostly, green banking literature consists of primary, cross sectional, descriptive and exploratory studies on the topics like, Green banking practices in India (Sudhalakshmi& Chinnadorai,2014).Factors determining adoption of green banking among commercial banks in Malaysia (Arumugan and Chirute,2018)Measuring green banking practices in Sri Lanka (Shumya and Arulrojah,2016).Therefore, wide research gap is identified by research in the following areas, measuring green banking disclosure practices. Linking corporate governance mechanism and green banking with firm performance. The purpose of this research is to create green banking disclosure index by combining all central bank guidelines from selected SAARC countries. In addition, this study aims to identify the influencing effects of green banking disclosure index with corporate governance mechanism such as 1. Board size, 2.Board independence, 3. Female in board and institutional ownership on market value of banks belonging to selected SAARC countries. 2. Related Literature Review Islam et al (2017) examine the impact of regulatory guidance and other factors on the green banking disclosure practices of Bangladeshi commercial banks in the period from 2007 to 2014. They find that, the issuance of green banking regulatory guidance by the Central Bank of Bangladesh in 2011 positively influences the level of green banking disclosure. They also report that green banking disclosure practices in the banking sector have converged over the time and have become a routine process. In addition, by following OLS model they find that corporate governance mechanisms (e.g., board size and institutional ownership) positively affect the level of green banking disclosure. However, this study finds no relationship between the presence of independent directors on the board and green banking disclosure. Dewi and Dewi (2017) provide empirical evidence about influencing role of green banking implementation on the relationship between corporate social responsibility and going concern value of banking companies in stock exchange of Indonesia. By applying moderated regression analysis (MRA) quantitative data (2013-2015) is analyzed and findings indicate implementation of green banking strengthens the relationship between corporate social responsibility and going concern value of banking companies in Indonesia. Wu et al (2019) establish a dynamic panel model for 12 Chinese- listed commercial banks and seven international commercial banks. The impact of green credit on the profitability of commercial banks and the difference between China and other countries is examined by using the generalized method of moments. The research shows that the Equatorial Principles project- financing ratio of international banks positively affects bank profitability, while the ratio of green credit for Chinese commercial banks is inversely related to their profitability. Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 545 Karim et al (2020) examines the effects of green banking practices on the financial performance of banks listed in the DSE of Bangladesh covering the period from 2011 to 2020. By using the panel data set, taking financial variables like return on asset, return on equity, and market value to proxy the banks’ performance, and employing green banking practice variables like green cost and volume of the risk management committee, study concludes that there is a positive relationship between green banking practices and financial performance. Monem et al (2020) provide useful insight to examine whether bank’s green performance can effect financial performance and whether this relation is moderated by bank’s political connection. From Bangladesh , Sample of 172 firm-year observations from 2008-2014 by applying difference-in-difference (DiD),propensity score matching (PMS)analysis and Heckman’s two stage analysis suggest that green banking performance is positively associated with banks financial performance. Robust findings also highlights political connections of banks negatively affects this relationship. Karyani and Obrien (2020) examine the effect of green banking practice on bank performance with foreign and public ownership as moderating variables of 14 Indonesian banks with 98 bank year observations between 2012 and 2018. By applying OLS (ordinary least square) model, this study provides useful insights that green banking practices have a negative impact on bank profitability but a positive impact on firm value. Negative effect of green banking practice on profitability is strengthen by public ownership. Positive impact of green banking practice on bank value is weakened by foreign ownership. Quazi et al (2021) builds on key insight whether combining green banking disclosure with contextual factor such as non-performing loans provides additional understanding about green banking disclosure and firm value. By analyzing seven years data of listed banks in Bangladesh (2008-2014) using multiple regression, they conclude that green banking disclosure gas positive effect on overall firm value. This positive effect is negatively moderated by banks non-performing loan. Gerged and Agwili (2019) identify in what way corporate governance affect firm profitability and firm value. A sample from (2012-2016) of 300 listed non-financial and financial companies from Saudi Arabia is analyzed by fixed effect panel data regression and GMM method. Results are suggestive that better governed firms tend not to improve accounting value but market value. 3. Data, Variable and Methodology Keeping in consideration data protocols, the data covers the listed banks in respective stock exchanges including Bangladesh, Pakistan, Sri Lanka, India and Nepal from 2010-2019 according to their annual reports. Banks with incomplete data were excluded from sample (Rehman,2016).Data regarding stock prices have been calculated either through stock price history information available stock exchanges, annual report year averages or from Investing.com to calculate market value of firms. Final sample includes 32 banks with 320 total 10 year observation. Sample comprises of 9 banks from India, 9 banks from Bangladesh, 5 Banks from Sri Lanka, 5 banks from Pakistan and 4 Banks from Nepal. The study analyses data on green banking disclosure practices by manually coding information on 38 items as 1 if information is present and 0 otherwise from annual reports available on website. These 38 items comprises of following categories, 1.Environment risk management 2.Green banking facilitation 3.Guidelines on own impact reduction. 4. Management related guidelines 5.Organization related guidelines 6.Green business facilitation 7. Specific guidelines. (SBP 2017).Then green banking disclosure index is developed by Principle Component Analysis technique (Al-Homaidi et al, 2021). The method of Principal Component Analysis is considered extremely reliable and accurate technique for empirical investigation of non-financial disclosure (Popa et al, 2021). 4. Definition of Variables Over variables of interest regarding corporate governance are board size (BRDSIZE) which is measured as total number of directors in board, board independence (BRDIND) measured as number of Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 546 independent directors in board, institutional ownership (INSTOWN) is measured as percentage of ownership holdings by institutional investors. Female director (FD) is measured as total number of females .A number of control variables are also included for controlling firm specific characteristics. Firm size (FSIZE) is measured as the natural logarithm of the firm’s total assets. Firm age (FAGE) is measured as total number of years from inauguration. Leverage (LEV) is measured as the ratio of total debt to total assets whereas profitability (ROA) is measured as the ratio of net income over total assets. (Islam et al, 2017).Green banking disclosure practices are measured by constructing green banking disclosure Index (GBDI). List of 38 items with key words is provided in appendix 1. The definition of these variables along with variable type and source is given below in table. Table: 1 Definitions of variables, types and sources. Variables Measurement Variable Type Source BRDSIZE Total number of directors in board. Independent Annual report BRDIND Number of independent directors in board. Independent Annual report INSTOWN Percentage of ownership holdings by institutional investors Independent Annual report FD Total number of females in board. Independent Annual report FSIZE The natural logarithm of the firm’s total assets. Control Annual report FAGE Total number of years from inauguration Control Annual report LEV The ratio of total debt to total assets Control Annual report ROA The ratio of net income over total assets Control Annual report GBDI Green banking disclosure Index Independent Annual report (Tobin’s Q) Tobin’s Q =Total Asset+ Market value of equity-Book value of Equity/Total Asset Dependent Variable Annual Report 5. Econometric Model and Methodology Firm performance is measured as market value of firm by Tobin’s Q.(Batsakis et al, 2018).To examine the impact of corporate governance mechanism and green banking disclosure index on market value of banks, following econometric model is developed by taking in consideration all variables mentioned in table above. Tobin’s Q j,t=β0j,t+β1j,t×Tobin’s Qj,t-1+β2j,t×Boardsizej,t+β3j,t×Board Independencej,t+β4j,t×Femaledirectorj,t+β5j,tInstituionalOwnershipj,t+β6j,t Green Banking Index j,t+ β7j,t×Controlsj,t+€j,t (1)Firm Valuej,t= Tobin’s Q of firm j at time t. (2)Firm Valuej,t-1= Tobin’s Q of firm j at time t-1. (3)Board Sizej,t= Board size of firm j at time t. (4)Board Independence j,t= Total independent director of firm j at time t. (5)Female director j,t= Female directors of firm j at time t. (6)Institutional Ownership j,t= % of institutional ownership of firm j at time t. Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 547 (7)Green Banking Index j,t= Green Banking index of firm j at time t. (8)Controls j,t= Control variables of firm j at time t. According to Jatmiko et al (2020) corporate governance variables including board size, board independence and female in board are dynamic in nature. Firm value is measured as Tobin’s Q which is lag dependent variable. Most of previous work (Bitar et al, 2017) in banking have been practicing pooled OLD estimation However, according to Baltagi (2008), pooled analysis using random or fixed effects are biased even if the error term is not serially correlated. That’s why, this work used system GMM to test the dynamic relationship between Firm value, corporate governance characteristics and green banking disclosure in the presences of control variables which are bank specific. According to Judson and Owen (1999) for dynamic panel data estimation, system GMM one step is highly recommended when Time period is less than or equal to 10 as in the case of current study. Alqahtani and Mayes (2018) in comparison to other panel methods, system GMM have advantages such as dynamic modeling treats autocorrelation, endogeneity, and unobserved heterogeneity. System GMM (Arellano-Bond estimation) is available in two versions, one step and two step. The asymptotic standard error of estimation of one step is more reliable and unbiased to draw inferences but at the same time in the case of heteroscedasticity, it cannot produce Sargan statistics. In this case one can rely Wald-Chi statistics to check over-identification restriction and overall significance of the model. (Pandy and Sahu, 2021) 6. Findings and Discussion on Results Table 2: Summary of methodologies used in green banking literature Author(year) Sample Determinants Methods Islam et al (2017) 30 Bangladesh Bank 2007-2014 Board size, Board independence, Female director ,Institutional ownership, Growth opportunities, Year dummy, Firm size , Lev, ROA Firm age, OLS regression. Dewi & Dewi(2017) 10 Banks Indonesia (2013-2015) CSR disclosure, Green banking regulations, Going concern value. Moderated Regression Analysis (MRA) Karim et al (2020) 10 listed commercial banks china (2011-2020) ROA, ROE, Green credit ratio as cost, Volume of risk management committee. Panel Data Analysis Wu et al (2019) 19 Chinese listed bank (2008-2015) Green credit ratio, ROA, ROE, NPL, Capital adequacy ratio. GMM, Dynamic panel data analysis. Monem et al (2020) 172 firm years observations (2008-2014) Green credit ratio, ROA, Political connections. Difference in Difference (DiD), Propensity Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 548 scoring method, Heckman’s two stage analysis. Karyani &Obrien (2020) Indonesian Bank (2012-2018) ROA, Green banking practices, Foreign Public Ownership. OLS regression. Quazi et al (2021) Listed banks Bangladesh (2008- 2014) Green banking disclosure, non-performing loan, Tobin’s Q Multiple Regression Analysis. Gerged and Agwili (2019) (2012-2016) Corporate governance mechanism , Market value, ROA, ROE, Tobin’s Q, Board Size, Board independence, Board meeting, Fixed Effect Panel data regression. GMM model. Gosh et al (2021) 30 Banks (2011-2017) Board Independence, Board meetings, Board diversity, Tobin’s Q, ROA, Audit committee size, Non- executive directors. Pooled OLS Method. Table: 3 Descriptive Data Statistics Variable Obs Mean Std. Dev. Min Max Board Size 320 11.5812 3.7416 5 22 Board Ind 320 2.0031 2.1327 0 8 Female Director 320 0.8937 0.9958 0 4 IO 320 26.1703 23.4078 0 98.63 Firm Age 320 42 32.3606 11 113 LEV 320 77.6310 11.8855 16.64 92.03 ROA 320 1.2440 1.7385 -7.21 7.31 Firm Size 320 26.7433 1.4741 23.0233 30.0802 Tobin’s Q 320 111.5365 43.8682 18.6244 636.5374 GB 320 1.16 1.4142 -0.3547 5.6200 According to the table above mentioned maximum size of board is 22 members and minimum is 5. Board independence varies from 0 -8. At max there are 4 females in board. Institutional ownership varies from 0 to 98% which is very high. Firm value varies from 18.62% to 636.53%.Green banking disclosure shows very low value -.354 to very high level of disclosure that is 5.62 among the selected SAARC countries. Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 549 Table: 4 Content Analysis Results of Green Banking Disclosure on 38 Items. Variable Obs. Mean Std. De Min Max GB1 320 .5468 .4985 0 1 GB2 320 .4687 .4998 0 1 GB3 320 .5156 .5005 0 1 GB4 320 .3968 .4900 0 1 GB5 320 .2656 .4423 0 1 GB6 320 .4375 .4968 0 1 GB7 320 .0812 .2736 0 1 GB8 320 .3468 .4767 0 1 GB9 320 .4656 .4995 0 1 GB10 320 .3218 .4679 0 1 GB11 320 .4687 .4998 0 1 GB12 320 .5468 .4985 0 1 GB13 320 .4687 .4998 0 1 GB14 320 .2968 .4575 0 1 GB15 320 .0218 .1465 0 1 GB16 320 .4156 .4936 0 1 GB17 320 .0218 .1465 0 1 GB18 320 .0375 .1902 0 1 Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 550 GB19 320 .4937 .5007 0 1 GB20 320 .0093 .0965 0 1 GB21 320 .9250 .2638 0 1 GB22 320 .4593 .4991 0 1 GB23 320 .6250 .4848 0 1 GB24 320 .1593 .3665 0 1 GB25 320 .9187 .2736 0 1 GB26 320 .0562 .2307 0 1 GB27 320 .2500 .4336 0 1 GB28 320 .0468 .2117 0 1 GB29 320 .1625 .3694 0 1 GB30 320 .0812 .2736 0 1 GB31 320 .3687 .4832 0 1 GB32 320 .3156 .4654 0 1 GB33 320 .0593 .2366 0 1 GB34 320 .0593 .2366 0 1 GB35 320 .0062 .0789 0 1 GB36 320 .0031 .0559 0 1 GB37 320 .1250 .3312 0 1 GB38 320 .9500 .2182 0 1 Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 551 All disclosure related items are binary in nature ranging from 0-1 value. Among all 38 items 6 items are having maximum mean values ranging from .46 to .95. Some items are having very low level of average disclosure like .006-.002. Table: 5 Correlation Matrix According to correlation matrix it is clear that all variables in econometric model are perfectly uncorrelated with each other. Board Size Board Ind Female Directo r IO Firm Age Lev ROA Firm Size GB Tobin’ sQ Board Size 1.000 0 Board Ind 0.086 6 1.0000 Female Directo r 0.195 8 0.3632 1.0000 IO 0.076 9 0.6555 0.5204 1.000 0 Firm Age 0.007 9 0.1794 0.0357 0.114 8 1.000 0 Lev 0.230 5 0.2514 0.2802 0.263 8 0.285 8 1.000 0 ROA 0.083 8 0.2480 0.0555 0.138 7 0.312 6 0.1811 1.000 0 Firm Size 0.043 1 0.0379 0.1069 .1121 0.734 1 0.093 4 0.186 6 1.0000 GB 0.150 8 0.2608 0.0694 .1037 0.162 5 0.085 5 0.193 5 0.0631 1.000 0 Tobin’ s Q 0.176 0 0.0371 0.0323 0.098 6 0.167 0 0.067 5 0.196 6 0.2665 0.007 1 1.000 0 A correlation among variable that exceeds 0.9 or VIF value greater than 10 shall indicate multi co-linearity (Gujarati, 2003). Table mentioned above shows there is no such issue among variables at all. Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 552 Table: 6 Multi-Collinearity Diagnostic 7. Empirical Result Principal component analysis is dimension reduction technique which is widely discussed in sustainability and CSR disclosure literature. (Benjamin et al, 2019). In this study, PCA provides 7 components with Eigenvalues > 1. First component caries maximum information having eigenvalue 13.9 and explains 36.5% variation which is very high. Rest of the 6 components collectively explains 34% variation. Rotated Matrix, eigenvectors and Scree plot of eigenvalues is also provided below. After identifying components predicted value of green banking index is calculated. Table 7: Principal Component Analysis Component Eigenvalue Difference Proportion Cumulative Comp1 14.5792 10.9948 .3738 .3738 Comp2 3.58439 1.10395 .0919 .4657 Comp3 2.48044 .0577924 .0636 .5293 Comp4 2.42265 .664594 .0621 .5915 Comp5 1.75806 .175874 .0451 .6365 Comp6 1.58218 .352977 .0406 .6771 Comp7 1.22921 .143176 .0315 .7086 Variable VIF 1/VIF Firm Age years 2.88 0.347719 Firm Size 2.57 0.389607 IO 2.17 0.459984 BoardInd 1.97 0.507800 Female Director 1.55 0.643527 Lev 1.30 0.766859 GB 1.23 0.811943 ROA 1.22 0.822999 Board Size 1.13 0.886665 Mean VIF 1.78 Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 553 Figure-1: Scree Plot of eigenvalues after PCA The scree plot is graphical representation of eigenvalues. The horizontal axis presents components and vertical axis presents eigenvalues while (Klomp and Haan, 2009). Figure-1 presents the eigenvalues of all three components and it can be observed that component-1 has the maximum value and produce the steep slope. Table: 8 Principal Component Eigenvectors Variable Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7 GB1 0.2222 -0.0647 0.0011 -0.0304 -0.0304 -0.2008 -0.1154 GB2 0.2237 -0.0603 -0.0292 -0.0989 -0.0989 -0.0775 -0.1712 GB3 0.2357 -0.0629 0.0262 -0.0470 -0.0470 -0.1836 -0.0047 GB4 0.2199 -0.1497 -0.0385 0.0818 0.0818 -0.0130 -0.0186 GB5 0.2025 0.0380 -0.0769 -0.1206 -0.1206 0.2378 -0.0931 GB6 0.2089 -0.1374 -0.0109 0.0729 0.0729 -0.0260 0.0711 GB7 0.1153 -0.0732 -0.0165 0.0176 0.0176 0.5210 0.0614 GB8 0.2248 -0.0186 -0.0489 -0.0026 -0.0026 0.0474 0.0688 GB9 0.2002 -0.1023 -0.0194 0.0907 0.0907 -0.0208 -0.3748 GB10 0.1886 -0.1831 -0.0258 0.0436 0.0436 0.1521 -0.2347 GB11 0.1803 0.0175 0.0058 -0.0632 -0.0632 -0.0359 0.1950 GB12 0.2180 -0.0641 0.0266 -0.0604 -0.0604 -0.1747 0.0790 GB13 0.2322 -0.0544 0.0078 -0.0465 -0.0465 -0.1166 0.0807 GB14 0.2092 0.0770 -0.0141 -0.0227 -0.0227 0.0138 0.1849 GB15 -0.1136 0.1789 0.1110 0.0671 -0.0402 0.0180 0.0475 GB16 -0.1136 0.1783 0.1110 0.0671 -0.0402 -0.1015 0.3950 GB17 0.1421 -0.1777 0.2290 0.1376 0.1908 -0.0536 0.0343 0 5 10 15 E ig en va lu es 0 2 4 6 8 Number Scree plot of eigenvalues after pca Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 554 GB18 0.0588 -0.0585 -0.0890 -0.0121 -0.0216 0.0072 -0.0731 GB19 0.0365 0.1775 -0.0179 0.0459 -0.0158 0.4550 -0.2629 GB20 0.0362 -0.0910 0.1961 -0.1266 -0.1661 0.0625 -0.0300 GB21 0.1155 0.3341 0.0655 -0.0107 -0.0396 -0.2370 0.0773 GB22 0.1891 -0.0119 0.0544 -0.3874 0.0990 -0.0205 -0.1834 GB23 -0.1059 -0.0330 0.1659 -0.1020 0.2624 0.1260 0.1995 GB24 0.1848 -0.1975 0.0005 0.0676 -0.2862 -0.0187 0.0330 GB25 -0.0277 -0.2335 -0.0431 -0.0568 0.0315 -0.0221 -0.0325 GB26 -0.3763 0.0642 0.0041 0.4534 -0.0370 0.1336 0.0256 GB27 -0.0834 0.0168 0.1180 0.0288 0.1648 -0.0301 0.0954 GB28 0.1155 -0.0938 0.0934 0.0813 0.0041 -0.0261 0.0244 GB29 -0.0161 0.3069 -0.0074 0.0296 0.0027 0.0417 0.0412 GB30 -0.0292 0.0965 -0.2042 -0.0508 -0.0077 -0.0789 -0.0714 GB31 0.2142 -0.2149 0.0183 0.0142 0.1465 0.0273 -0.1136 GB32 0.0059 0.0878 -0.0422 0.2558 -0.3320 -0.2277 -0.0377 GB33 0.0145 -0.0573 0.1250 0.0159 0.0217 0.0588 0.0339 GB34 0.0145 -0.0573 0.1250 0.0159 0.0217 0.0588 0.0339 GB35 0.0429 -0.0914 -0.0523 -0.0181 0.0060 -0.0127 0.0004 GB36 -0.0587 0.1891 -0.0224 0.0160 0.0060 -0.0201 -0.0446 GB37 0.1251 -0.1471 0.0699 -0.0266 0.0077 0.0871 0.0811 GB38 0.0806 -0.0295 0.0012 -0.0301 -0.0064 0.0770 -0.0251 Table: 9 Principal Components Orthogonal Varimax Rotation No. of Obs:320 No. of Comp:37 Traces: 38 Rho:1.0000 Component Variance Difference Proportion Cumulative Comp1 2 1 0.0526 .0526 Comp2 1 1.02700e-09 0.0263 .0780 Comp3 1 1.60119e-09 0.0263 .1053 Comp4 1 2.16254e-08 0.0263 .1316 Comp5 1 2.14184e-08 0.0263 .1579 Comp6 1 2.78397e-11 0.0263 .1842 Comp7 1 9.40026e-12 0.0263 .2105 Comp8 1 -7.27087e-11 0.0263 .2368 Comp9 1 7.04863e-11 0.0263 .2632 Comp10 1 3.07442e-09 0.0263 .2854 Comp11 1 -2.39225e-09 0.0263 .3158 Comp12 1 -6.80650e-10 0.0263 .3421 Comp13 1 1.87759e-11 0.0263 .3684 Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 555 Comp14 1 1.58955e-11 0.0263 .3947 Comp15 1 -9.25814e-09 0.0263 .4211 Comp16 1 8.80252e-09 0.0263 .4474 Comp17 1 4.08009e-10 0.0263 .4737 Comp18 1 1.53375e-10 0.0263 .5000 Comp19 1 -1.00031e-09 0.0263 .5263 Comp20 1 8.55693e-10 0.0263 .5789 Comp21 1 5.25580e-13 0.0263 .6053 Comp22 1 -2.17382e-13 0.0263 .6316 Comp23 1 5.51597e-11 0.0263 .6579 Comp24 1 2.13773e-08 0.0263 ..6842 Comp25 1 -2.14076e-08 0.0263 .7105 Comp26 1 -1.68218e-09 0.0263 .7368 Comp27 1 1.00998e-08 0.0263 .7632 Comp28 1 -2.88725e-09 0.0263 .7895 Comp29 1 -9.07525e-09 0.0263 .8158 Comp30 1 3.77244e-09 0.0263 .8421 Comp31 1 -7.68353e-09 0.0263 .8421 Comp32 1 7.41877e-09 0.0263 .8684 Comp33 1 -5.82249e-10 0.0263 .8947 Comp34 1 1.82356e-09 0.0263 .9211 Comp35 1 3.90585e-10 0.0263 .9474 Comp36 1 -1.55918e-09 0.0263 .9737 Comp37 1 0.0263 1.000 Table 10: System GMM One-Step results for selected SAARC Countries: Corporate Governance characteristic and Market Value Tobin’s Q Coef. Std. Err. z P>|z| [95% Conf. Interval] Tobin’s Q L1 .0528571 .0472455 1.12 .263 -.0397424 .1454566 Board Size -6.335266 1.812093 -3.50 .000 -9.886904 -2.783628 Board Ind 1.03113 3.280271 0.31 .753 -5.398083 7.460344 Female Director 3.285065 4.882335 0.67 .501 -6.284135 12.85427 IO -.8228694 .3143179 -2.62 .009 -1.438921 -.2068176 Firm Age -2.004165 .9467143 -2.12 .034 -3.859691 -.148639 Firm Size 8.013257 2.072315 -3.87 .000 3.951594 12.07492 LEV .5350474 .4963865 1.87 .281 -.4378523 -1.507947 ROA -1.847115 3.636309 0.611 .611 -8.97415 5.279921 Obs. 288 Wald Chi 262.71 Prob 0.000 Sargan test 0.000 Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 556 Table 11: System GMM One-Step results for selected SAARC Countries: Green Banking, Corporate Governance Characteristics and Market Value. Tobin’s Q Coef. Std. Err. z P>|z| [95% Conf. Interval] Tobin’s Q L1 .052121 .0472671 1.10 0.270 -.0405208 .1447628 Board Size -6.467463 1.81805 -3.56 0.000 -10.03078 -2.90415 Board Ind 1.515256 3.322343 0.46 0.648 -4.996416 8.026928 Female Director 3.016476 4.890703 0.62 0.537 -6.569126 12.60008 IO -.7357883 .3276762 -2.25 -0.025 -1.3780022 -0.935548 Firm Age -1.938855 .9493205 -2.04 0.041 -3.799489 -0.0782208 Firm Size 8.023921 2.072781 3.87 0.000 3.3961344 12.0865 LEV .5312251 .4965618 1.07 0.285 -.4420182 1.504468 ROA -1.750552 3.638579 -0.48 0.630 -8.882037 5.380952 GB -4.867581 5.214606 -0.93 0.351 -15.0880 15.352859 Obs. 288 Wald Chi 263.41 Prob 0.000 Sargan test 0.000 To examine the relationship between board characteristics, green banking disclosure and firm value in selected SAARC countries STATA 14.2 software is used t. System GMM-step one method for panel data set covering the period 2010-2019 is applied. It has been recommended by Faitouri (2014) that one lag is sufficient to capture the influence of the past on the current data. First Data set is declared to be dynamic, panel ID is set to be banks and time is years. By clicking (Arellano- Bover/Blundell-Bond estimation option, following command xtdpdsys generates results provided in table 5,6.After controlling the effects of firm specific characteristic such as, firm age, size, leverage and profitability at 5% confidence of interval board size has significant negative influence on market value of firm.( β= 6.33 p=.000). Institutional ownership has significant negative influence on market value. Green banking disclosure does not have any significant influence on market value. Lipton and Lorsch (1992) report that larger board size is ineffective. Agency theory (Jensen, 1993) suggests that large board size is dysfunctional. Optimum board size should be 8 or 7. Beyond this limit board management is costly. The possible reason behind negative impact of board size is average size of board is 11-12 in SAARC countries collectively which is too high according to agency theory. (Jensen, 1993). Charfeddine and Elmarzougui (2010) identify negative impact of institutional ownership and firm performance measured as Tobin’s Q in listed companies in France. According to controlling hypothesis institutional ownership beyond 81% contributes positively in firm value. (Wardhana and Tendililine, 2011).In present study average institutional ownership is between 21-22% which is very low. The conclusive findings are robust in the context of SAARC region. 8. Conclusion The main contribution of this study is to shed light and explore dynamic relationships among green banking disclosure practices, corporate governance mechanism and firm value in selected SAARC Countries. Based upon author’s knowledge, this is the first study which methodologically contributes by applying system GMM step one and PCA in the field of green banking disclosure. Contextually SARRC countries are targeted to explore unobserved dynamic relations as per research model. SAARC region is one of the most effected and threaded area due to climate risk and global warming. By developing a composite green banking disclosure index, a new stream in the field of disclosure is added. This index Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 557 can be used as independent, mediator or moderator variable to explore unobserved relations with firm performance like market value, going concern value, profitability etc. Effectiveness of central bank’s green banking guidelines can be observed in the light of theory of change and financial intermediation theory at regional and global level. Corporate governance mechanism and market value of firm is observed in the light of agency theory and controlling hypothesis. The findings are suggestive that corporate governance mechanism restructuring is needed to have positive contribution in market value of banks belonging to India, Pakistan, Bangladesh, Nepal and Sri Lanka. References Alqahtani, F. and Mayes, D.G. (2018). “Financial stability of Islamic banking and the global financial Crisis: evidence from the Gulf cooperation council”, Economic Systems, Vol. 42 No. 2, pp. 346-360. Al-Homaidi et al (2021). “Corporate Social Responsibility disclosure and profitability, Evidence from Islamic banks working in Yemen” Business properties Vol.2 PP, 91-102 Alexander K (2016.) Greening banking policy. In: Support of the G20 Green Finance Study Group Baltagi, B. (2008). Econometric Analysis of Panel Data, John Wiley and Sons. Dahir, A.M., Mahat, F.B. and Ali, N.A.B. 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Review of Economics and Development Studies, Vol. 7 (4) 2021, 543 - 559 559 22 Waste reduction policies including Water, gas etc. 23 Energy consumption in conducting business operation. 24 Employee travel reduction. 25 Online, automated, mobile banking. 26 Bank’s network about environmental issues. 27 Seminar and trainings about green banking. 28 Bank award winning about environmental friendly activities. 29 Establishment of Climate change fund. 30 Internal marketing caption in annual report about green banking. 31 Actual spending on green banking activities. 32 Separate pages in annual report for green banking reporting. 33 Green branch officer presence in bank branches. 34 Green credit advisory services. 35 Green credit financing targets at regional branch level. 36 International funding for green project investments. 37 Inventory targets for electricity, water, petroleum, paper. 38 Paperless banking.