Life Cycle Assessment and Optimization-Based Decision Analysis of Construction Waste Recycling for a LEED-Certified University Building

The current waste management literature lacks a comprehensive LCA of the recycling of construction materials that considers both process and supply chain-related impacts as a whole. Furthermore, an optimization-based decision support framework has not been also addressed in any work, which provides a quantifiable understanding about the potential savings and implications associated with recycling of construction materials from a life cycle perspective. The aim of this research is to present a multi-criteria optimization model, which is developed to propose economically-sound and environmentally-benign construction waste management strategies for a LEED-certified university building. First, an economic input-output-based hybrid life cycle assessment model is built to quantify the total environmental impacts of various waste management options: recycling, conventional landfilling and incineration. After quantifying the net environmental pressures associated with these waste treatment alternatives, a compromise programming model is utilized to determine the optimal recycling strategy considering environmental and economic impacts, simultaneously. The analysis results show that recycling of ferrous and non-ferrous metals significantly contributed to reductions in the total carbon footprint of waste management. On the other hand, recycling of asphalt and concrete increased the overall carbon footprint due to high fuel consumption and emissions during the crushing process. Based on the multi-criteria optimization results, 100% recycling of ferrous and non-ferrous metals, cardboard, plastic and glass is suggested to maximize the environmental and economic savings, simultaneously. We believe that the results of this research will facilitate better decision making in treating construction and debris waste for LEED-certified green buildings by combining the results of environmental LCA with multi-objective optimization modeling

Socio-Eco-Efficiency Analysis of Highways: A Data Envelopment Analysis

To ensure the large network of highways is performing sustainably, there is a dire need to quantify sustainability for highways. In this paper, data envelopment analysis (DEA) based mathematical model is developed to evaluate sustainability in an attempt to aid these efforts. Sustainability goals pertaining to the three dimensions of sustainability, social, economic and environmental, were utilized. Utilizing the developed model, sustainability scores of thirty highway sections were calculated and ranked accordingly. Percent improvement analysis was carried out to gain more insight. In addition, sensitivity analysis was carried out to understand how different values of input parameters impacted the socio-eco-efficiency of each highway section. The aim of the study was to show that DEA based sustainability assessment model could be used to evaluate highways and assist in strategic planning goals of transportation agencies. Results indicated that 22% to 47% reductions are required to be achieved on negative social and environmental impacts for the inefficiency highway sections to be 100% efficient while keeping the economic indicators the same

Empirical Analysis of Construction Enterprise Information Systems: Assessing the Critical Factors and Benefits

Attaining higher levels of system integration is seen as the primary goal of enterprise information systems in construction (CEIS). Increased system integration resulting from CEIS implementation is expected to lead to numerous benefits. These benefits encompass information technology infrastructure as well as strategic, operational, organizational, and managerial aspects of the firm. By adopting CEIS, firms seek tangible and intangible benefits such as cost reduction, improved productivity, enhanced efficiency, and business growth. However, with the challenge of integrating various business functions within the firm, certain factors become critical for achieving higher levels of integration. Despite ample research on integrated IT systems, there are very few works in the construction field that empirically analyze the critical factors impacting the level of integration and the benefits thereof. This study seeks to address these gaps in the literature and analyzes the impact of critical factors on levels of integration and the ensuing benefits through a systematic and rigorous research design. The conceptual framework in this study draws heavily upon the theory of IT integration infrastructures, while also modifying and expanding it. This study quantifies the critical success factors that impact CEIS integration and the ensuing benefits. Furthermore, it analyzes the effects of system integration on CEIS induced benefits. It also investigates the impact of CEIS strategy on CEIS induced benefits, and identifies the relationship between CEIS strategy and system integration. Finally, it assesses the effects of CEIS induced benefits on user satisfaction and provides a CEIS implementation guide map for construction firms. The study uses multiple regression analysis and ANOVA to test these relationships

A Fuzzy Data Envelopment Analysis Framework for Dealing with Uncertainty Impacts of Input–Output Life Cycle Assessment Models on Eco-efficiency Assessment

The uncertainty in the results of input–output-based life cycle assessment models makes the sustainability performance assessment and ranking a challenging task. Therefore, introducing a new approach, fuzzy data envelopment analysis, is critical; since such a method could make it possible to integrate the uncertainty in the results of the life cycle assessment models into the decision-making for sustainability benchmarking and ranking. In this paper, a fuzzy data envelopment analysis model was coupled with an input–output-based life cycle assessment approach to perform the sustainability performance assessment of the 33 food manufacturing sectors in the United States. Seven environmental impact categories were considered the inputs and the total production amounts were identified as the output category, where each food manufacturing sector was considered a decision-making unit. To apply the proposed approach, the life cycle assessment results were formulated as fuzzy crisp valued-intervals and integrated with fuzzy data envelopment analysis model, thus, sustainability performance indices were quantified. Results indicated that majority (31 out of 33) of the food manufacturing sectors were not found to be efficient, where the overall sustainability performance scores ranged between 0.21 and 1.00 (efficient), and the average sustainability performance was found to be 0.66. To validate the current study\u27s findings, a comparative analysis with the results of a previous work was also performed. The major contribution of the proposed framework is that the effects of uncertainty associated with input–output-based life cycle assessment approaches can be successfully tackled with the proposed Fuzzy DEA framework which can have a great area of application in research and business organizations that use with eco-efficiency as a sustainability performance metric

Socio-eco-efficiency analysis of highways: a data envelopment analysis

To ensure the large network of highways is performing sustainably, there is a dire need to quantify sustain­ability for highways. In this paper, data envelopment analysis (DEA) based mathematical model is developed to evalu­ate sustainability in an attempt to aid these efforts. Sustainability goals pertaining to the three dimensions of sustain­ability, social, economic and environmental, were utilized. Utilizing the developed model, sustainability scores of thirty highway sections were calculated and ranked accordingly. Percent improvement analysis was carried out to gain more insight. In addition, sensitivity analysis was carried out to understand how different values of input parameters impacted the socio-eco-efficiency of each highway section. The aim of the study was to show that DEA based sustainability as­sessment model could be used to evaluate highways and assist in strategic planning goals of transportation agencies. Results indicated that 22% to 47% reductions are required to be achieved on negative social and environmental impacts for the inefficiency highway sections to be 100% efficient while keeping the economic indicators the same

Regional Well-to-Wheel Carbon, Energy, and Water Footprint Analysis of Electric Vehicles

Adoption of alternative vehicle technologies such as electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) have the potential of reducing some of the environmental impacts and reducing oil-dependency of the U.S transportation sector. However, this potential depends on the regional driving patterns and the source of the electricity generation to power PHEVs and EVs. In this study, state-specific electricity generation mix scenarios and driving patterns in Alabama, Florida, and Hawaii are considered to calculate regional impacts associated with alternative vehicle technologies (HEVs, PHEVs, EVs) compared to internal combustion vehicles (ICVs). Three electricity generation mix scenario are evaluated, which are namely; average electricity generation mix, marginal electricity generation mix, and 100% solar electricity generation mix. Well-to-wheel carbon, energy, and water footprint of these vehicles are quantified for each state and potential environmental reductions are evaluated. According to comparative evaluation for the proposed scenarios, shifting to low carbon, energy, and water intensive electricity generation mix by utilization of solar energy is crucial to achieve environmental friendly transportation in the U.S.https://doi.org/10.2991/apte-18.2019.2

Life Cycle Assessment and Optimization-Based Decision Analysis of Construction Waste Recycling for a LEED-Certified University Building

The current waste management literature lacks a comprehensive LCA of the recycling of construction materials that considers both process and supply chain-related impacts as a whole. Furthermore, an optimization-based decision support framework has not been also addressed in any work, which provides a quantifiable understanding about the potential savings and implications associated with recycling of construction materials from a life cycle perspective. The aim of this research is to present a multi-criteria optimization model, which is developed to propose economically-sound and environmentally-benign construction waste management strategies for a LEED-certified university building. First, an economic input-output-based hybrid life cycle assessment model is built to quantify the total environmental impacts of various waste management options: recycling, conventional landfilling and incineration. After quantifying the net environmental pressures associated with these waste treatment alternatives, a compromise programming model is utilized to determine the optimal recycling strategy considering environmental and economic impacts, simultaneously. The analysis results show that recycling of ferrous and non-ferrous metals significantly contributed to reductions in the total carbon footprint of waste management. On the other hand, recycling of asphalt and concrete increased the overall carbon footprint due to high fuel consumption and emissions during the crushing process. Based on the multi-criteria optimization results, 100% recycling of ferrous and non-ferrous metals, cardboard, plastic and glass is suggested to maximize the environmental and economic savings, simultaneously. We believe that the results of this research will facilitate better decision making in treating construction and debris waste for LEED-certified green buildings by combining the results of environmental LCA with multi-objective optimization modeling

Eco-Efficiency Of Construction Materials: Data Envelopment Analysis

Microreactors experience significant deviations from plug flow due to the no-slip boundary condition at the walls of the chamber. The development of stagnation zones leads to widening of the residence time distribution at the outlet of the reactor. A hybrid design optimization process that combines modeling and experiments has been utilized to minimize the width of the residence time distribution in a microreactor. The process was used to optimize the design of a microfluidic system for an in vitro model of the lung alveolus. Circular chambers to accommodate commercial membrane supported cell constructs are a particularly challenging geometry in which to achieve a uniform residence time distribution. Iterative computational fluid dynamics (CFD) simulations were performed to optimize the microfluidic structures for two different types of chambers. The residence time distributions of the optimized chambers were significantly narrower than those of non-optimized chambers, indicating that the final chambers better approximate plug flow. Qualitative and quantitative visualization experiments with dye indicators demonstrated that the CFD results accurately predicted the residence time distributions within the bioreactors. The results demonstrate that such a hybrid optimization process can be used to design microreactors that approximate plug flow for in vitro tissue engineered systems. This technique has broad application for optimization of microfluidic body-on-a-chip systems for drug and toxin studies. © 2012 Biomedical Engineering Society

Development Of An Agent-Based Model For Regional Market Penetration Projections Of Electric Vehicles In The United States

One of the most promising strategies recommended for increasing energy security and for mitigating transportation sector emissions is to support alternative fuel technologies, including electric vehicles. However, there is a considerable amount of uncertainty regarding the market penetration of electric vehicles that must be accounted for in order to achieve the current market share goals. This paper aims to address these inherent uncertainties and to identify the possible market share of electric vehicles in the United States for the year 2030, using the developed Electric Vehicle Regional Market Penetration tool. First, considering their respective inherent uncertainties, the vehicle attributes are evaluated for different vehicle types, including internal combustion engine, gasoline hybrid, and three different electric vehicle types. In addition, an agent-based model is developed to identify the market shares of each of the studied vehicles. Finally, market share uncertainties are modeled using the Exploratory Modeling and Analysis approach. The government subsidies play a vital role in the market adoption of electric vehicle and, when combined with the word-of-mouth effect, may achieve electric vehicle market share of up to 30% of new sales in 2030 on average, with all-electric vehicles having the highest market share among the electric vehicle options

A Hybrid Life Cycle Assessment Of Public Transportation Buses With Alternative Fuel Options

Purpose: Alternative fuel options are gaining popularity in the vehicle market. Adopting alternative fuel options for public transportation compared to passenger vehicles contributes exponentially to reductions in transportation-related environmental impacts. Therefore, this study aims to present total air pollutant emissions and water withdrawal impacts through the lifetime of a transit bus with different fuel options. Methods: In consideration of market share and future development trends, diesel, biodiesel, compressed natural gas (CNG), liquefied natural gas (LNG), hybrid (diesel-electric), and battery electric (BE) transit buses are analyzed with an input-output (IO)-based hybrid life cycle assessment (LCA) model. In order to accommodate the sensitivity of total impacts to fuel economy, three commonly used driving cycles are considered: Manhattan, Central Business District (CBD), and Orange County Transit Authority (OCTA). Fuel economy for each of these driving cycles varies over the year with other impacts, so a normal distribution of fuel economy is developed with a Monte Carlo simulation model for each driving cycle and corresponding fuel type. Results and discussion: Impacts from a solar panel (photovoltaic, PV) charging scenario and different grid mix scenarios are evaluated and compared to the nation’s average grid mix impacts from energy generation to accommodate the lifetime electricity needs for the BE transit bus. From these results, it was found that the BE transit bus causes significantly low CO2 emissions than diesel and other alternative fuel options, while some of the driving cycles of the hybrid-powered transit bus cause comparable emissions to BE transit bus. On the other hand, lifetime water withdrawal impacts of the diesel and hybrid options are more feasible compared to other options, since electricity generation and natural gas manufacturing are both heavily dependent on water withdrawal. In addition, the North American Electricity Reliability Corporation’s (NERC) regional electricity grid mix impacts on CO2 emissions and water withdrawal are presented for the BE transit bus. Conclusions: As an addition of current literature, LCA of alternative fuel options was performed in this paper for transit buses with the consideration of a wide variety of environmental indicators. Although the results indicate that BE and hybrid-powered buses have less environmental emissions, the US’s dependency on fossil fuel for electricity generation continues to yield significant lifetime impacts on BE transit bus operation. With respect to water withdrawal impacts, we believe that the adoption of BE transit buses will be faster and more environmentally feasible for some NREC regions than for others