Corporate social responsibilities of Indian public sector enterprises-a case study of Bharat Heavy Electricals Limited (BHEL)

The high level of public accountability attached to Public Sector Enterprises as a result of public ownership makes them socially responsible. The Committee of Public Undertakings in 1992 examined the issue relating to social obligations of Central Public Sector Enterprises and observed that ``being part of the `State', every Public Sector enterprise has a moral responsibility to play an active role in discharging the social obligations endowed on a welfare state, subject to the financial health of the enterprise''. It issued the Corporate Social Responsibility Guidelines in 2010 where all Central Public Enterprises, through a Board Resolution, are mandated to create a CSR budget as a specified percentage of net profit of the previous year. This paper examines the CSR activities of the biggest engineering public sector organization in India, Bharath Heavy Electricals Limited. The objectives are twofold, one, to develop a case study of the organization about the funds allocated and utilized for various CSR activities, and two, to examine its status with regard to other organizations, the 2010 guidelines, and the local socio-economic development. Secondary data analysis results show three interesting trends. One, it reveals increasing organizational social orientation with the formal guidelines in place. Two, Firms can no longer continue to exploit environmental resources and escape from their responsibilities by acting separate entities regardless of the interest of the society and Three the thrust of CSR in public sector is on inclusive growth, sustainable development and capacity building with due attention to the socio-economic needs of the neglected and marginalized sections of the society.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).