Browsing by Author "Krishnan, Palaniappa"

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    Achieving Carbon Neutrality: US and India Weigh Policy Options
    (Biden School of Public Policy & Administration, University of Delaware, Newark, DE 19716, USA, 2022-06-01) Krishnan, Palaniappa; Kasturi, Prahlad
    The paper takes a critical look at the US and India positions on achieving carbon neutrality as per their commitment to the Paris Agreement on Climate Change. These are based on the climate change policies of the leaders of the two countries, President Joe Biden, and Prime Minister Narendra Modi, at the COP 26 summit held in Glasgow, Scotland in November 2021. Policy tools to achieve carbon neutrality such as cap and trade and carbon tax (both market-based approaches), regulations (command and control approach) and other economic incentives such as tax credits and subsidies are examined. Based on various empirical research published in the literature regarding the two countries, an assessment is made regarding the use of these tools to achieve the goals of efficiency, equity, liberty, and sustainability in the two countries. Carbon taxation at the national level is currently missing in both countries and has the potential to be a revenue source of climate finance. The US needs to assert its leadership among the OECD donor countries to provide climate finance to developing countries and direct more of such finance for adaptation to climate change among developing countries. Low Carbon Technology (LCT) transfer through trade is low among both countries and there is a need to accelerate this process. Innovations that are occurring in both countries presently in nuclear power, hydrogen power and other clean energy such as solar, hydroelectric, geothermal and biomass can provide a great fillip to early achievement of net zero emissions. International cooperation and partnership between the US and India are growing in pursuing nuclear and solar as clean fuels. However, stepped up co-innovation in clean energy between the two countries holds great dividends to achieve carbon neutrality in both countries.
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    In-Patient Flow Analysis Using ProModelTM Simulation Package
    (Food and Resource Economics Department, 2000-11) Elbeyli, Sema; Krishnan, Palaniappa
    This paper emphasizes the basic modeling approach of general in-patient flow in a major hospital in the East Coast region. Simulation was used to analyze the inpatient flow. The first objective of this study was to determine the bottlenecks for in-in-patient flow. In order to understand the general in-patient flow, some emphasis was also given to the other units such as Medical-Surgical, Telemetry, Intensive Care Units (ICU), etc. Second objective was to study the impact of bed availability on the waiting time of admitted patients in ED before being transferred to assigned beds in other units of the hospital. A preliminary model was developed and validated based on the data collected for the selected time periods (busy four months). Different “what-if” scenarios were studied. This paper presents the basic model and its results.
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    Modeling Nitrate Concentration in Ground Water Using Regression and Neural Networks
    (Department of Food and Resources Economics, 2003-01) Ramasamy, Nacha; Krishnan, Palaniappa; Bernard, John C.; Ritter, William F.
    Nitrate concentration in ground water is a major problem in specific agricultural areas. Using regression and neural networks, this study models nitrate concentration in ground water as a function of iron concentration in ground water, season and distance of the well from a poultry house. Results from both techniques are comparable and show that the distance of the well from a poultry house has a significant effect on nitrate concentration in groundwater.
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    Modeling Nitrate Loading Rate in Delaware Lakes Using Regression and Neural Networks
    (Department of Food and Resources Economics, 2003-01) Sudhakar, Prachi; Krishnan, Palaniappa; Bernard, John C.; Ritter, William F.
    The objective of this research was to predict the nitrogen-loading rate to Delaware lakes and streams using regression analysis and neural networks. Both models relate nitrogen-loading rate to cropland, soil type and presence of broiler production. Dummy variables were used to represent soil type and the presence of broiler production at a watershed. Data collected by Ritter & Harris (1984) was used in this research. To build the regression model Statistical Analysis System (SAS) was used. NeuroShell Easy Predictor, neural network software was used to develop the neural network model. Model adequacy was established by statistical techniques. A comparison of the regression and neural network models showed that both perform equally well. Cropland was the only significant variable that had any influence on the nitrogen-loading rate according to both the models.