Jyoti Tanwar

Fulfilment of environmental and social obligations is required for sustainable development. Green accounting integrates all environmental consequences into corporate accounting accounts. Disclosure of Corporate social responsibility is a component of accounting performance tied to the social, economic, and environmental aspects of a company's programme that can be vital in cultivating stakeholders. The current research aims to investigate the effect of social responsibility disclosure on integrating green accounting and corporate governance of highly polluting industries in India. The study employed a descriptive study design and qualitative research methods in accordance with the interpretative research philosophy and collected the sample data from 52 highly polluted industries from Central Pollution Board in India. The study employs a structural model, descriptive statistics, and F-test analysis. The result reveals that green accounting and firm characteristics significantly enhance the selected firms' social responsibility disclosure. Furthermore, it was discovered that corporate governance and social responsibility disclosure impact the chosen firms' performance and value. However, it has been determined that green accounting does not affect the profitability or market value of the selected firms. Also, this work concludes that a good firm gives its surroundings and surrounding community enough consideration in addition to its pursuit of profit. As the research is based on 52 industries, the generalized result can not bed determined. The knowledge of the respondent also impacts the research.

Asset-Liability Management is critical for the successful working of banks. The Indian Financial system is very dynamic and growing rapidly. Banks focus on both the assets and liabilities due to liquidity risk, interest rate risk and foreign exchange risk. The implementation and understanding of ALM policies, and procedures will provide insight into the approach of banks towards ALM. A primary survey is conducted among employees of twelve public and twelve private banks to collect data related to employees' experience, understanding, and knowledge about the ALM. The questionnaire used in the study carries Yes/No type questions, multiple-choice questions, checklist questions, scaled questions. The data is analyzed using SPSS. The results suggest that public and private banks' employees have a similar understanding and knowledge of the ALM. Both types of banks follow identical practices and policies in the implementation of ALM with few deviations. The employees of the banks were hesitant to answer complex questions, and therefore questionnaire has to be kept generic and straightforward. This paper studies ALM in banks using primary data. All other studies have been conducted using secondary data or literature surveys. These studies did not feature the banks' approach in ALM policies, practices, and procedures. JEL Classification Codes: E58, E43, G33, G32, D81.

In earlier years, there was abundance of funds in banks in the form of demand and savings deposits. Hence, the focus of banks was mainly on asset management. But intense competition and volatility of interest rate due to banking reforms reduced the availability of low-cost funds and therefore, banks focused on liability management as well. These pressures call for structured and comprehensive measures and not just ad hoc action. This is how banks started to concentrate more on the management of both sides of the balance sheet. As a result, the concept of asset-liability management originated in India and introduced in the Indian banking industry since 1st April 1999 to administer the risk management aspects. This paper attempts to optimize assets and liabilities of banks using goal programming technique. Secondary data is collected from annual reports of Allahabad bank from 2010-2019 and RBI website is used for modelling. The findings show that in Allahabad bank, goal programming help in achieving optimization and increase profitability. The model incorporating constraints and set objectives. It model can support banks in decision making process, planning, budgeting, and forecasting. An attempt is made to use realistic goals and constraints after discussing with bank officials. JEL Classification Codes: C61, G21, G32.

Asset Liability Management has gained popularity in the banking sector. Earlier banks focused on asset allocation, but now the management of assets and liabilities is equally essential. Asset liability management targets the optimum distribution of funds in assets and managing liabilities so that banks can earn higher profits and minimize risk. In this paper, the optimization of assets and liabilities of Indian banks has been concentrated using mathematical models. Combining the Analytical Hierarchy Process (AHP) and Goal Programming (GP) model has been used to solve the optimization problem. AHP is a multi-criteria decision-making approach for deriving priority weights. Goal Programming is a linear programming model to solve complex issues having multiple objectives. In this paper, the primary data gathered from Bank senior managers have been analyzed using the AHP approach to derive weights for criteria. These weights are assigned to goals in goal programming to prioritize the goals. Secondary data on OBC bank is used in goal programming from 2010-2019 collected from OBC bank's annual reports and RBI websites. The findings show that OBC bank has the scope of improving its assets and liabilities position to increase its profit and minimize the risk. The model generates an optimum balance sheet that achieves the set goals and satisfies all the statutory and planning constraints. The same model can be useful for scheduled commercial banks in India with modifications concerning banks' targets and controls. The model developed in this paper is helpful for bank managers in planning and forecasting. AHP and GP's combined approach is unique in this paper, which uses experts' knowledge and applies it in the model. The model is created on the bank's realistic goals and constraints after carefully considering the issues faced by bank officials. The paper is limited to the Indian Banking system as other countries have different balance sheet structures and constraints.

This study examines the efficiency of the overall Indian banking industry using Data Envelopment Analysis (DEA) and to perform a comparative efficiency analysis of public, private, and foreign banks using six varied forms. Also, providing ranks to the banks based on their efficiency. The study incorporates BCC output-oriented DEA model using a sample of 50 Indian banks (public banks = 17, private banks = 18, foreign banks = 15) for a period ranging from 2009-10 to 2018-19, hence incorporating the after-effects of the financial crisis and demonetization, this study uses panel data from 2009-10 to 2018-19. The results showed that most of the Indian banks fall on the efficient side or are near to full efficiency. However, public banks outperform private and foreign banks in terms of their average efficiency. Results also specify that the performance of banks is sensitive to input-output variables, units under evaluation, and choice of the model. The current study has just focused on the internal factors for analyzing the efficiency of Indian banks; however, certain external factors might also impact the banks’ efficiency.