Wind power is one of the most promising renewable energy sources that has gained enormous attention, especially in the electricity generation sector over the past decade in the United States. In this study Data Envelopment Analysis (DEA) is implemented to quantitatively evaluate the relative efficiencies of the 236 large utility-scale wind farms. Input- and output-oriented CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models are applied to pre-determined three input and three output variables. The sensitivity analysis is conducted for the robustness of DEA by introducing seven new models with the various combinations of input and output variables of the original model. Tobit regression models are developed for the second stage of the analysis to investigate the effects of specifications of the wind turbine technologies. DEA results indicate that two-thirds of the wind farms are operated efficiently. On average, 70% of the wind farms have a considerable potential for further improvement in operational productivity by expanding these wind farm projects, 24% of them should reduce their operational size to increase their productivity level, and 6% of them are operating wind power at the most productive scale size. Nonparametric statistical tests show that the most efficient wind farms are located in Oklahoma because of the relatively high wind speed resources. Tobit regression model indicates the selection of the brand of the wind turbine has a significant contribution to the productive efficiency of the wind farms. The results of this study shed some light on the current efficiency assessments of the 236 large utility-scale wind farms in the United States and the future of wind energy for both energy practitioners and policy makers.

The average global surface temperature increased by 0.85 °C since 1850 because of irrepressible increase of the concentration of greenhouse gases (GHG). Electricity generation is the primary source of GHG emissions in the United States. Hence, renewable energy sources, which produce a negligible amount of GHG emissions, have gained enormous attention, especially in the electricity generation sector over the past decade. Wind power is the second largest renewable energy source to generate electricity in the United States. Therefore, in this study, a two-stage Data Envelopment Analysis (DEA) is developed to quantitatively evaluate the relative efficiencies of the 39 state's wind power performances for the electricity generation. Both input- and output-oriented CCR (Charnes, Cooper, and Rhodes (1978)) and BCC (Banker, Charnes, and Cooper (1984)) models are applied to pre-determined four input and six output variables. The sensitivity analysis is conducted to test the robustness of the DEA models. Tobit regression models are conducted by using the DEA results for the second stage analysis. The DEA results indicate that more than half of the states operate wind power efficiently. Tobit regression indicates that early installed wind power was more expensive and less productive relative the currently installed wind power. Findings of this study shed some light on the current efficiency assessments of the states and the future of wind energy for both energy practitioners and policy makers.

Die Identifikation von flexibel einsetzbaren, mächtigen Verfahren zur Massendatenanalyse und die Schaffung von standardisierbaren Vorgehensmodellen zur Integration dieser Verfahren in IT-Systeme sind zentrale Herausforderungen für die moderne Wirtschaftsinformatik. Insbesondere für KMU ist die Entwicklung standardisierter Lösungsansätze von großer Relevanz. Dies gilt über alle Branchen. Finanzdienstleister sind ebenso betroffen wie der Maschinen- und Anlagenbau. Im Rahmen dieser Forschungsarbeit wird eine Wissensbasis geschaffen werden, welche es einer breiten Masse an Unternehmen ermöglicht, geeignete quantitative Methoden zur Datenanalyse zu erkennen und diese für sich nutzbar zu machen.

The general practice in performance and production efficiency measurement has been to ignore additional products of most transformation processes that can be classified as "undesirable outputs" — which are a subset of the output set. Without the inclusion of these factors, the efficiency evaluation becomes a purely technical measure of the system alone, and does not account for the interaction of the system with the surrounding environment and the impact of policy decisions on the system. In addition, there are also technological dependencies arising due to the relationships between the desirable and the undesirable outputs. One of the analytical tools normally used in efficiency evaluation is Data Envelopment Analysis, DEA. In the course of addressing these problems, a decision-maker encounters multiple and contradictory objectives with respect to the output set. This motivates the exploration of new arenas of measurement of efficiency to facilitate policy decisions and address technological relationships. This research presents five modifications of the traditional DEA technique to give a more realistic and comprehensive score of production efficiency considering both, desirable and undesirable outputs. The models address the following problems: (i) technological dependency between desirable and undesirable outputs; (ii) decision-maker's preferences over inputs, desirable outputs and undesirable output performance and finally (iii) conflicting production objectives through a formulation that uses Goal Programming in conjunction with DEA, a concept known as GoDEA. / Master of Science

No description available.

Real Estate Investment Trusts (REITs) are firms that own and manage income producing commercial real estate for the benefit of their shareholders. The three studies in this dissertation explore topics relating to best practices of REIT management and portfolio composition. Managers and investors can use the findings herein to aide in analyzing a REIT’s performance and determining optimal investment policies. Utilizing REIT from SNL Real Estate and CRSP, the first two studies examine the role of international diversification upon performance, technical efficiency, and scale efficiency. The third study utilizes REIT data to examine technical and scale efficiency over a 21 year window and investigates characteristics of the REITs that affect the levels of efficiency. CHAPTER 1 – PROFITABILITY OF REAL ESTATE INVESTMENT TRUST INTERNATIONALIZATION Real Estate Investment Trusts (REITs) in the United States have grown extremely fast in terms of assets and market capitalization since the early 1990’s. As with many industries, U.S. REITs began acquiring foreign properties as their size grew and they needed to seek new investment opportunities. This paper investigates the role of holding foreign assets upon the total return of U.S. based REITs from 1995 through 2010. We find that holding foreign properties in associated with negative relative performance when risk, size, and other common market factors are controlled for. Interestingly, the source of the negative performance is not related to the two largest areas for foreign investment, Europe and Canada. Instead, the negative performance is iii detected when a REIT begins acquiring properties in other global regions such as Latin America and Asia/Pacific. This paper has broad ramifications for REIT investors and managers alike. CHAPTER 2 – EFFECT OF INTERNATIONAL DIVERSIFICATION BY U.S. REAL ESTATE INVESTMENT TRUSTS ON COST EFFICIENCY AND SCALE As U.S. based Real Estate Investment Trusts (REITs) have increased their degree and type of holdings overseas, there has yet to a study that has investigated such activity on the REIT’s measures of cost efficiency and scale. Using data from 2010, Data Envelopment Analysis techniques are used to estimate measures of technical and scale efficiency that are then regressed against measures of international diversification and other controls to measure the impact of this global expansion. It is determined that REITs with foreign holdings are significantly larger than domestic REITs and are correspondingly 96% of foreign investing REITs are operating at decreasing returns to scale. Further almost every measure of foreign diversification is negative and significantly impacting scale efficiency. However, simply being a REIT with foreign holdings did positively and significantly associate with higher levels of technical efficiencies. Thus REITs that expand globally may have some advantages in operational efficiency but lose considerably in terms of scale efficiency by increasing their size as they move cross-border. iv CHAPTER 3 – THE EVOLUTION OF TECHNICAL EFFICIENCY AND ECONOMIES OF SCALE OF REAL ESTATE INVESTMENT TRUSTS Data Envelopment Analysis (DEA) is used to measure technical and scale efficiency of 21 years of Real Estate Investment Trust (REIT) data. This is the longest, most complete dataset ever analyzed in the REIT efficiency literature and as such makes a significant contribution as prior efficiency studies’ data windows end in the early 2000’s at latest. Overall, REITs appear to continue to operate at decreasing returns to scale despite rapid growth in total assets. Further, there is some evidence of improving technical efficiency overtime; however the finding is not strong. In summation, it appears that REITs have not improved on a relative basis despite the rapid growth, a finding that suggests a potential of a high degree of firm competition in the REIT industry. Finally, firm characteristics such as debt utilization, management and advisory structure, and property type specialization are tested for their impact upon technical and scale efficiency.

In a time of strained resources and dynamic environments, the importance of effective and efficient systems is critical. This dissertation was developed to address the need to use feedback from multiple stakeholder groups to define quality and assess an entity’s efficiency at achieving such quality. A decision support model with applicability to diverse domains was introduced to outline the approach. Three phases, (1) quality model development, (2) input-output selection and (3) relative efficiency assessment, captured the essence of the process which also delineates the approach per tool applied. This decision support model was adapted in higher education to assess academic departmental efficiency at achieving stakeholder-relative quality. Phase 1 was accomplished through a three round, Delphi-like study which involved user group refinement. Those results were compared to the criteria of an engineering accreditation body (ABET) to support the model’s validity to capture quality in the College of Engineering & Computer Science, its departments and programs. In Phase 2 the Analytic Hierarchy Process (AHP) was applied to the validated model to quantify the perspective of students, administrators, faculty and employers (SAFE). Using the composite preferences for the collective group (n=74), the model was limited to the top 7 attributes which accounted for about 55% of total preferences. Data corresponding to the resulting variables, referred to as key performance indicators, was collected using various information sources and infused in the data envelopment analysis (DEA) methodology (Phase 3). This process revealed both efficient and inefficient departments while offering transparency of opportunities to maximize quality outputs. Findings validate the potential of the ii Delphi-like, analytic hierarchical, data envelopment analysis approach for administrative decision-making in higher education. However, the availability of more meaningful metrics and data is required to adapt the model for decision making purposes. Several recommendations were included to improve the usability of the decision support model and future research opportunities were identified to extend the analyses inherent and apply the model to alternative areas.

The thesis examines technical efficiency using frontier efficiency estimation techniques from parametric and non-parametric approaches. Five different frontier efficiency estimation techniques are considered which are SFA, DFA, DEA-CCR, DEA-BCC and DEA-RAM. These techniques are then used on an artificially generated panel dataset using a two-input two-output production function framework based on characteristics of German life-insurers. The key contribution of the thesis is firstly, a study that uses simulated panel dataset to estimate frontier efficiency techniques and secondly, a research framework that compares multiple frontier efficiency techniques across parametric and non-parametric approaches in the context of simulated panel data. The findings suggest that, as opposed to previous studies, parametric and non-parametric approaches can both generate comparable technical efficiency scores with simulated data. Moreover, techniques from parametric approaches, i.e. SFA and DFA are consistent with each other whereas the same applies to non-parametric approaches, i.e. DEA models. The research study also discusses some important theoretical and methodological implication of the findings and suggests some ways whereby future research can enable to overcome some of the restrictions associated with current approaches.

No description available.

No description available.