Omer Tatari

A macro-level decision analysis of wind power as a solution for sustainable energy in the USA

This study aims to quantify the socio-economic and environmental impacts of producing electricity by wind power plants for the US electricity mix. To accomplish this goal, all direct and supply chain-related impacts of different onshore and offshore wind turbines are quantified using a hybrid economic input-output-based triple bottom line (TBL) life cycle assessment model. Furthermore, considering TBL sustainability implications of each onshore and offshore wind energy technology, a multi-criteria decision-making tool which is coupled with Monte Carlo simulation is utilised to find the optimal choice of onshore and offshore wind energy. The analysis results indicate that V90-3.0 MW wind turbines have lower impacts than V80-3.0 MW for both socio-economic and environmental indicators. The Monte Carlo simulation results reveal that when environmental issues are more important than socio-economic impacts, V90-3.0 MW offshore is selected among the alternatives.

Economic Input–Output Based Sustainability Analysis of Onshore and Offshore Wind Energy Systems

According to the U.S. Department of Energy’s wind energy scenario, 20% share of the U.S. energy portfolio is to come in from wind power plants by the year 2030. This research aims to quantify the direct and supply chain related indirect environmental impacts of onshore and offshore wind energy technologies in the United States. To accomplish this goal, a hybrid life cycle assessment (LCA) model is developed. On average, offshore wind turbines produce 48% less greenhouse gas emissions per kWh produced electricity than onshore wind turbines. It is also found that the more the capacity of the wind turbine, the less the environmental impact when the turbine generates per kWh electricity.

Eco-Efficiency of Construction Materials: Data Envelopment Analysis

Sustainability assessment tools are critical in the process of achieving sustainable development. Eco-efficiency has emerged as a practical concept that combines environmental and economic performance indicators to measure the sustainability performance of different product alternatives. In this paper, an analytical tool that can be used to assess the eco-efficiency of construction materials is developed. This tool evaluates the eco-efficiency of construction materials by using data envelopment analysis, a linear programming-based mathematical approach. Life-cycle assessment (LCA) and life-cycle cost (LCC) are utilized to derive the eco-efficiency ratios, and data envelopment analysis (DEA) is used to rank material alternatives. Developed mathematical models are assessed by selecting the most eco-efficient exterior wall finish for a building. Percent improvement analysis was carried out to investigate target environmental effect categories that need more reduction to reach 100% eco-efficiency. Through this study, the goal is to show that DEA-based eco-efficiency assessment model could be used to evaluate alternative construction materials and offer vital guidance for decision makers during material selection. DOI: 10.1061/ (ASCE)CO.1943-7862.0000484.

Integration of system dynamics approach toward deepening and broadening the life cycle sustainability assessment framework: a case for electric vehicles

PurposeQuantitative life cycle sustainable assessment requires a complex and multidimensional understanding, which cannot be fully covered by the current portfolio of reductionist-oriented tools. Therefore, there is a dire need on a new generation of modeling tools and approaches that can quantitatively assess the economic, social, and environmental dimensions of sustainability in an integrated way. To this end, this research aims to present a practical and novel approach for (1) broadening the existing life cycle sustainability assessment (LCSA) framework by considering macrolevel environmental, economic, and social impacts (termed as the triple bottom line), simultaneously, (2) deepening the existing LCSA framework by capturing the complex dynamic relationships between social, environmental, and economic indicators through causal loop modeling, (3) understanding the dynamic complexity of transportation sustainability for the triple bottom line impacts of alternative vehicles, and finally (4) investigating the impacts of various vehicle-specific scenarios as a novel approach for selection of a macrolevel functional unit considering all of the complex interactions in the environmental, social, and economic aspects.MethodsTo alleviate these research objectives, we presented a novel methodology to quantify macrolevel social, economic, and environmental impacts of passenger vehicles from an integrated system analysis perspective. An integrated dynamic LCSA model is utilized to analyze the environmental, economic, and social life cycle impact as well as life cycle cost of alternative vehicles in the USA. System dynamics modeling is developed to simulate the US passenger transportation system and its interactions with economy, the environment, and society. Analysis covers manufacturing and operation phase impacts of internal combustion vehicles (ICVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). In total, seven macrolevel indicators are selected; global warming potential, particulate matter formation, photochemical oxidant formation, vehicle ownership cost, contribution to gross domestic product, employment generation, and human health impacts. Additionally, contribution of vehicle choices to global atmospheric temperature rise and public welfare is investigated.Results and discussionBEVs are found to be a better alternative for most of sustainability impact categories. While some of the benefits such as contribution to employment and GDP, CO2 emission reduction potential of BEVs become greater toward 2050, other sustainability indicators including vehicle ownership cost and human health impacts of BEVs are higher than the other vehicle types on 2010s and 2020s. While the impact shares of manufacturing and operation phases are similar in the early years of 2010s, the contribution of manufacturing phase becomes higher as the vehicle performances increase toward 2050. Analysis results revealed that the US transportation sector, alone, cannot reduce the rapidly increasing atmospheric temperature and the negative impacts of the global climate change, even though the entire fleet is replaced with BEVs. Reducing the atmospheric climate change requires much more ambitious targets and international collaborative efforts. The use of different vehicle types has a small impact on public welfare, which is a function of income, education, and life expectancy indexes.ConclusionsThe authors strongly recommend that the dynamic complex and mutual interactions between sustainability indicators should be considered for the future LCSA framework. This approach will be critical to deepen the existing LCSA framework and to go beyond the current LCSA understanding, which provide a snapshot analysis with an isolated view of all pillars of sustainability. Overall, this research is a first empirical study and an important attempt toward developing integrated and dynamic LCSA framework for sustainable transportation research.

Evaluating environmental impacts of alternative construction waste management approaches using supply-chain-linked life-cycle analysis

Waste management in construction is critical for the sustainable treatment of building-related construction and demolition (C&D) waste materials, and recycling of these wastes has been considered as one of the best strategies in minimization of C&D debris. However, recycling of C&D materials may not always be a feasible strategy for every waste type and therefore recycling and other waste treatment strategies should be supported by robust decision-making models. With the aim of assessing the net carbon, energy, and water footprints of C&D recycling and other waste management alternatives, a comprehensive economic input–output-based hybrid life-cycle assessment model is developed by tracing all of the economy-wide supply-chain impacts of three waste management strategies: recycling, landfilling, and incineration. Analysis results showed that only the recycling of construction materials provided positive environmental footprint savings in terms of carbon, energy, and water footprints. Incineration is a better option as a secondary strategy after recycling for water and energy footprint categories, whereas landfilling is found to be as slightly better strategy when carbon footprint is considered as the main focus of comparison. In terms of construction materials’ environmental footprint, nonferrous metals are found to have a significant environmental footprint reduction potential if recycled.

A hybrid life cycle assessment of public transportation buses with alternative fuel options

PurposeAlternative 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.MethodsIn 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 discussionImpacts 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.ConclusionsAs 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.

Sustainability Assessment of U.S. Construction Sectors: Ecosystems Perspective

AbstractThe U.S. construction industry accounts for approximately 4% of the gross domestic product. Although quantifying and analyzing the cumulative ecological resource consumption of the construction industry is of great importance, it has not been studied sufficiently. This paper aims to account for the total ecological resource consumption of the construction industry, including its supply chains. This analysis is achieved by using an ecologically based life-cycle assessment model. The impacts on the ecosystem were calculated on the basis of the economic data in terms of cumulative mass, energy, industrial exergy, and ecological exergy. U.S. construction sectors are holistically evaluated by using various sustainability metrics, such as resource intensity, efficiency ratio, and loading ratio. Total ecological exergy values were generally found to be larger for the sectors with higher economic output values. Heavy construction industry sectors, including construction and maintenance of highways, bridge...

INTEGRATED AGENT-BASED CONSTRUCTION EQUIPMENT MANAGEMENT: CONCEPTUAL DESIGN

Abstract Effective management of equipment is crucial for the success of construction firms. Inadequate manual processes of equipment management and the subjective decisions of equipment managers usually result in major losses in construction firms, hence, the economy. The main purpose of this paper is to introduce an agent‐based equipment management system aiming to increase integration and automation, and to minimise decision errors. Recent research on agent technology allows the proposition of an automated and integrated application for equipment management. The proposed application makes use of the current databases of the firm and adds wireless technology to construction equipment for automated data integration.

Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus

This paper investigates the use of artificial neural networks (ANNs) and genetic algorithms to improve the accuracy of the prediction of subgrade resilient modulus (M r) based on soil index properties. Furthermore, it also examines the effect of the accuracy of the M r estimation on the mechanistic empirical pavement design guide (MEPDG) performance prediction. The results of this paper showed that the ANN models had much better prediction of the M r coefficients of subgrade soils than that of the regression models. In addition, the use of the genetic algorithms in the selection of the input variables of the ANN models enhanced the accuracy of the prediction of those models. The results of the MEPDG analyses indicated that the prediction model used to estimate the subgrade M r input value can have a significant effect on the predicted performance of pavements. Furthermore, those results showed that the use of ANN models yielded much more accurate pavement performance prediction than using regression models; in particular when genetic algorithms were used in developing those models.

Evaluation of Conditional Transit Signal Priority Technology for Regional Implementation

This research evaluated the implementation of transit signal priority (TSP) on a test corridor along International Drive in Orlando, Florida, to see whether the implementation was successful and justified expansion to a regional implementation of TSP for bus tie-ins to the new regional SunRail commuter rail in Central Florida. TSP is a technology that provides preferential treatment to buses at signalized intersections. This research demonstrated the effectiveness of TSP in improving bus corridor travel time in a simulated environment by using real-world data for the International Drive corridor. Evaluation was conducted with microsimulation to compare unconditional and conditional TSP with the no TSP scenario. This evaluation looked at performance metrics (for buses and all vehicles), including average speed profiles, average travel times, average number of stops, and crossing street delay. Different conditional TSP scenarios of activating TSP when a bus is 3 or 5 min behind schedule were considered. The simulation demonstrated that conditional TSP significantly improved bus travel times with little effect on crossing street delays. Unconditional TSP resulted in significant crossing street delays at some intersections with only minor improvement to bus travel time compared with both conditional TSP scenarios. The results also showed that using TSP technology reduced the environmental emissions in the International Drive corridor. With a benefit–cost ratio of 7.92 in the International Drive corridor, conditional TSP 3 min behind schedule was determined to be the most beneficial and practical TSP scenario for real-world implementation at corridor and regional levels.