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Cost-Benefit Analysis (CBA) was applied to evaluate the "Blue Bag" recycling program in Muncie, IN. Data was collected from the Muncie Sanitary District, a waste recovery facility and from a sample of Muncie residents. Results were analyzed descriptively and statistically and compared in terms of environmental performance to a landfilling option. Recycling resulted in greater greenhouse gas (GHG) emissions reduction and also in larger savings in energy consumption compared to landfilling (an equivalent of savings of $25,800.52 per year). A willingness to pay (WTP) parameter ranged from $0-175 in WTP in taxes/rent per year and a $0-52 range for WTP per trash container collected. The mean value of the welfare improvement corresponded with values obtained by other researchers (Hanley and Spash, 1993), totaling $960,797.65 for the WTP in taxes/rent scenario and $412,819.95 for the WTP per trash container collected scenario. The project's Net Present Values (NPV) were negative under the 3, 5 and 7`%, discount rates (-5209,961.75, -8242,477.73 and -S269,187.38 in the case of an optimistic scenario and -S675,244.83, -657.262.23 and -640,288.54 for the pessimistic scenario, respectfully) if environmental impacts were not taken into consideration. Environmental benefits associated with the Blue Bag program impacted results of the analysis - the project's NPVs were positive under the range of 3, 5 and 7% interest rates (510,569.501.26, 9,704,811.17 and 8,951,068.00 for the optimistic scenario and S 10, l 04,218.17, 9,290,026.68 and 8.579,966.84 in the case of pessimistic scenario, respectfully). It is concluded, that if the Blue Bag program is justified on CBA grounds, it can be considered as a profitable initiative from the social point of view; otherwise, the recycling program's costs outweigh its incomes and the existence and continuation of the Blue Bag program may he questioned. Further studies should be conducted in order to comprehensively test the overall social impacts of the program that could affect results presented in this study. / Department of Natural Resources and Environmental Management

The environmental impact of consumer goods is becoming a growing concern in the modern world. With the increasing awareness of our daily impact and our effects on such crises as global warming, there has been a recent push to develop better environmental strategies and new industries focused on sustainability and the recycling of a variety of post-consumer goods. In other words, there is a shift towards turning waste into money in the name of the environment. Urban regions provide the perfect setting for such development. The concentration of post-consumer waste makes the mining of recyclable materials economical and the availability of labor needed to support the emerging sustainable industries sets the stage for social, economic and environmental benefits. There are currently several end-of-life (EOL) options for post-consumer products. EOL scenarios include secondary material reclamation, material reclamation, repurpose material reclamation, and waste disposal. Within each of these EOL scenarios exists a myriad of process permutations ranging from various collection schemes and modes of transportation to material processing standards and new recycled product industries. Due to the variety of EOL options for post-consumer products, there is no straight-forward answer to the question Which EOL option is preferred? Thus, under the guidelines of Life Cycle Assessment (LCA) as standardized by the International Standards Organization (ISO) in ISO14040, with the inclusion of social and economic requirements as well, the various EOL scenarios are compared in several impact categories including energy use, greenhouse emissions, waste generation, social implications and economic viability. The results of this comparative analysis provide insight into the potential of a more sustainable urban environment, which is part of a much larger goal of reducing our daily impact on the world around us. One industry sector that contributes to a rather large amount of post-consumer trash each year, nearly 4.7 billion tons, is the carpet industry. Carpet thus occupies a great percentage of overall dedicated waste space according to product density. Within an urban environment, the burden of such a bulky waste product is more evident; thus carpet is used here as a case study for the social, economic and environmental impacts of material mining in urban regions for the improvement of overall industrial sustainability. A comparative EOL study is conducted comparing the social, economic and environmental effects of secondary material reclamation, material reclamation, repurpose material reclamation, and waste disposal of post-consumer carpet (PCC) within the thirteen county urban region of Atlanta, GA.

This thesis deals with the mechanical and physical investigations on a newly discovered porous material based on fly ash cenospheres. The process used to fabricate the material along with the physical properties of the material is first described. Under uniaxial compression, it is observed that the material exhibits a long load plateau that is typical of energy absorbing materials such as thin-walled metallic honeycombs. In tension, the material fractures similar to most traditional brittle materials such as glass and ceramics. As a result, several uniaxial compression and tension tests are performed on different samples to evaluate the influence of the different chemicals, the curing time, and the mass density on the 'plateau' strength. However, in addition to its low processing costs, the new material presents important properties that are desirable for discrete materials such as homogeneity and isotropy. Although its insulating properties were not quantified, it appears that the material can be used as an excellent heat barrier. Finally, metallic tubes as well as bamboo poles reinforced with the new material are tested to investigate the effectiveness of the reinforcement, showing highly improved structural performance.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Thesis (M.A.I.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 74-79). Also available on the World Wide Web.

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2008. / Committee Co-Chair: Ammons, Jane C.; Committee Co-Chair: Realff, Matthew J.; Committee Member: Griffin, Paul M.; Committee Member: Peoples, Robert; Committee Member: Thomas, Valerie.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Bert Bras; Committee Member: Beril Toktay; Committee Member: Shreyes Melkote.