Abstract:The forests of Chir pine (Pinus Roxburgii) encompass 97.4 thousand acres or 16.15 % of the total forest land of Uttrakhand, a state in India. According to Forest Department of India, Dehradun, a massive forest fire in 1995 engulfed 14.7 thousand acres of valuable forest area through 2,272 forest fire incidents in Uttrakhand, which resulted in the loss of crores of rupees and created various long-lasting ecological consequences. The fires damaged the fertile top layer of the soil and left a layer of pine needle litter that prevented rain water from being absorbed by the soil and contributed to early depletion of the groundwater cycle and stopped grass growth, thus depriving livestock of important food. So the question of what to do with these pine needles is an important one for forest and livestock. Regarding this problem, the German organization, Deutsche Gesellschaft für Internationale Zusammenarbeit, has been studying ways to use pine needles as a feedstock for downdraft gasifiers. If successful, the gasifier would provide incentive to collect the pine needles during dry months and improve the condition of the soil by allowing water to permeate top soils. Using chemical analysis, flue gas analysis, and combustion analysis, our paper analyzes the potential of pine needles as a substrate for gasification. We also argue that using pine needles in this way would alleviate carbon dioxide emissions due to forest fire. Average carbon dioxide emissions in forests that have an abundance of pine needles is 15.46%, but reduced to 12.8% when pine needles are used in a gasifier plant.Key words: Chemical analysis, Flue gas analysis, Combustion analysis, India rural people face severe consequences from the overuse of biomass: exposure to smoke from traditional cook stoves and open fires causes more than 1.5 premature deaths annually, with women and young children the most affected. Furthermore, people lose time and money by collecting and purchasing wood and the environment suffers from depletion of natural forests [3]. Materials and MethodsPine needles for gasification were collected from the seven sites in Uttrakhand and tested for chemical and physical properties. Chemical tests were performed at The Energy and Resource Institute (TERI) and Jawaharlal Nehru University (JNU) laboratories. Field work on gasifiers was conducted at the Chanderpur Works Private Limited (Yamunanagar) and TERI (Gurgaon) in Haryana. The schedule of parallel testing of 60 hrs and 40 hrs in Yamunanagar and Gurgaon was adopted for those sites, respectively. The industrial-based Kirloskar Green CNG Engine, SL 90 TA series, and the modified Kirloskar RV 3 series, generator sets have been used.

The objective of this research study is twofold: 1) to evaluate the prediction accuracy of four valuation multiples across three sectors for Indian listed firms and 2) to identify the fundamental drivers for these multiples. The valuation multiples identified for this study are: price to earnings (P/E), price to book value (P/BV), price to sales (P/S) and enterprise value to earnings before interest, depreciation, tax and amortization (EV/EBIDTA) and the sectors taken are steel, banking and automobile. Multiple regression methodology is followed with the valuation multiple as dependent variable and the value drivers as independent variables, to get predicted multiples on 470 firm observations. By regressing the multiples on fundamental variables, the best suited multiple for each sector and the key drivers of the multiple are obtained. The empirical findings based on root mean square error (RMSE) and Theil coefficient reveal that least prediction errors are observed in P/S and EV/EBIDTA for the automobile sector, EV/EBIDTA for the steel sector and P/BV for the banking sector. It is also observed that the significant variables that explain these multiples are beta, return on equity (ROE), return on capital employed (ROC), dividend payout ratio (D/P) and net profit margins (NPM). These findings are in line with the derivation of fundamental drivers for each multiple as explained in Gordon model. Damodaran: 2007 [1]. The present work contributes to emerging market literature on equity valuations and attempts to compare valuations based on market approach using value drivers. A comparison of forecasts with actuals helps in recommendations to buy/sell/accumulate/hold for equity investors and is also pertinent for market participants and financial regulators.

The objective of the research study is to identify the key predictors that can explain default risk for Indian listed companies using survival analysis. The author has applied the semi-parametric Cox proportional hazard model to test the impact of financial ratios, capital market ratios, macro-economic variables, size and age of companies, and the ownership structure of promoters to a dataset of 859 companies panning across 10 sectors. Unlike traditional models on default prediction, survival models focus on "time to default" as the dependent variable. The empirical findings reveal that return on capital employed (ROCE), return on net worth (ROE), interest coverage ratio, exchange rate volatility, GDP growth rate, stock index, promoters holdings % and the percent of shares pledged are all significant predictors of default. Among the market variables, it is seen that beta and the ratio of market value of equity/book value of debt are statistically significant variables in explaining default risk. The empirical findings also generate the hazard ratio for each covariate which examines the predicted change in the hazard for a unit increase in the predictor. The author extends the research by applying the marketbased KMV structural model to obtain continuous observations of default probability and regressing the same against all the 1 covariates (Gupta et al.,. It is observed that the set of significant covariates are almost common to those generated by our survival approach. The study concludes in emphasizing the significance of survival models in default prediction as unlike traditional accounting-based and market-based models, these models assess relationship between survival time and covariates. The application of survival models is strongly recommended for credit risk evaluation and modeling as structuring of loans can be done by lenders by assessing the survival times of different firms across the entire observation period being considered.

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This paper analyzes the impact of open market repurchase (OMR) route of buyback on the stock prices of a data set of 30 Indian listed firms which had gone for buyback in the FY16. The author has applied event study methodology to calculate abnormal returns and cumulative abnormal returns on stocks using BSE 500 as market index. The returns are calculated for 20 days prior and post buyback announcement to test for information signaling hypothesis. The analysis shows that average abnormal return (AAR) on the date of announcement is −0.23 percent while cumulative abnormal return (CAR) is about 5.72 percent on the announcement date with an overall CAR 3.44 percent for 40-day event window. The research findings reveal that unlike tender offers, OMR does not lead to a signaling effect as there is the insignificant impact on stock prices. Market reaction to buyback offer is in contradiction to signaling hypothesis predictions. The results of the study imply that the information related to the announcement of the buyback is already reflected in the share price. This also throws light on the growing maturity and efficiency of the stock market of India. Analyzing the signaling effect through OMR reveals that rather than signaling hypothesis, market reaction to buybacks is better explained by free cash flow hypothesis.