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With the increasing concerns related to integration of social and economic dimensions of the sustainability into life cycle assessment (LCA), traditional LCA approach has been transformed into a new concept, which is called as life cycle sustainability assessment (LCSA). This study aims to contribute the existing LCSA framework by integrating several social and economic indicators to demonstrate the usefulness of input–output modeling on quantifying sustainability impacts. Additionally, inclusion of all indirect supply chain-related impacts provides an economy-wide analysis and a macro-level LCSA. Current research also aims to identify and outline economic, social, and environmental impacts, termed as triple bottom line (TBL), of the US residential and commercial buildings encompassing building construction, operation, and disposal phases. To achieve this goal, TBL economic input–output based hybrid LCA model is utilized for assessing building sustainability of the US residential and commercial buildings. Residential buildings include single and multi-family structures, while medical buildings, hospitals, special care buildings, office buildings, including financial buildings, multi-merchandise shopping, beverage and food establishments, warehouses, and other commercial structures are classified as commercial buildings according to the US Department of Commerce. In this analysis, 16 macro-level sustainability assessment indicators were chosen and divided into three main categories, namely environmental, social, and economic indicators. Analysis results revealed that construction phase, electricity use, and commuting played a crucial role in much of the sustainability impact categories. The electricity use was the most dominant component of the environmental impacts with more than 50 % of greenhouse gas emissions and energy consumption through all life cycle stages of the US buildings. In addition, construction phase has the largest share in income category with 60 % of the total income generated through residential building’s life cycle. Residential buildings have higher shares in all of the sustainability impact categories due to their relatively higher economic activity and different supply chain characteristics. This paper is an important attempt toward integrating the TBL perspective into LCSA framework. Policymakers can benefit from such approach and quantify macro-level environmental, economic, and social impacts of their policy implications simultaneously. Another important outcome of this study is that focusing only environmental impacts may misguide decision-makers and compromise social and economic benefits while trying to reduce environmental impacts. Hence, instead of focusing on environmental impacts only, this study filled the gap about analyzing sustainability impacts of buildings from a holistic perspective.

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.

Purpose In the USA, several studies have been conducted to analyze the energy consumption and atmospheric emissions of Warm-mix Asphalt (WMA) pavements. However, the direct and indirect environmental, economic, and social impacts, termed as Triple-Bottom-Line (TBL), were not addressed sufficiently. Hence, the aim of this study is to develop TBLoriented sustainability assessment model to evaluate the environmental and socio-economic impacts of pavements constructed with different types of WMA mixtures and compare them to a conventional Hot-mix Asphalt (HMA). The types of WMA technologies investigated in this research include Asphamin® WMA, Evotherm™ WMA, and Sasobit® WMA. Methods Toachievethisgoal,supplyandusetablespublished by the U.S. Bureau of Economic Analysis were merged with 16 macro-level sustainability metrics. A hybrid TBL-LCA model was built to evaluate the life-cycle sustainability performance of using WMA technologies in construction of asphalt pavements. The impacts on the sustainability were calculated in terms of socio-economic (import, income, gross operating surplus, government tax, work-related injuries, and employment) and environmental (water withdrawal, energy use, carbon footprint, hazardous waste generation, toxic releases into air, and land use). A stochastic compromise programming model was then developed for finding the optimal allocation of different pavement types for the U.S. highways. Results and discussion WMAsdid not perform betterinterms of environmental impacts compared to HMA. Asphamin® WMA was found to have the highest environmental and socio-economic impacts compared to other pavement types. Material extractions and processing phase had the highest contribution to all environmental impact indicators that shows the importance of cleaner production strategies for pavement materials. Based on stochastic compromised programming results, in a balanced weighting situation, Sasobit® WMA had the highest percentage of allocation (61 %); while only socio-economic aspects matter, Asphamin® WMA had the largest share (57 %) among the asphalt pavements. The optimization results also supported the significance of an increased WMA use in the U.S. highways. Conclusions This research complemented previous LCA studies by evaluating pavements not only from environmental emissions and energy consumption standpoint, but also from socio-economicperspectives.Multi-objectiveoptimizationresults also provided important insights for decision makers whenfinding the optimum allocationof pavement alternatives based on different environmental and socio-economic priorities. Consequently, this study aimed to increase awareness of the inherent benefits of economic input–output analysis and multi-criteria decision making through application to emerging sustainable pavement practices.

Analyzing building related carbon emissions remains as one of the most increasing interests in sustainability research. While majority of carbon footprint studies addressing buildings differ in system boundaries, scopes, GHGs and methodology selected, the increasing number of carbon footprint reporting in response to legal and business demand paved the way for worldwide acceptance and adoption of the Greenhouse Gas Protocol (GHG Protocol) set by the World Resources Institute (WRI) and World Business Council for Sustainable Development (WBCSD). Current research is an important attempt to quantify the carbon footprint of the U.S. residential and commercial buildings in accordance with carbon accounting standards and Scopes set by WRI, in which all possible indirect emissions are also considered. Emissions through the construction, use, and disposal phases were calculated for the benchmark year 2002 by using a comprehensive hybrid economic input–output life cycle analysis. The results indicate that emissions from direct purchases of electricity (Scope 2) with 48% have the highest carbon footprint in the U.S. buildings. Indirect emissions (Scope 3) with 32% are greater than direct emissions (Scope 1) with 20.4%. Commuting is the most influential activity among the Scope 3 emissions with more than 10% of the carbon footprint of the U.S. buildings overall. Construction supply chain is another important contributor to the U.S. building's carbon footprint with 6% share. Use phase emissions are found to be the highest with 91% of the total emissions through all of the life cycle phases of the U.S. buildings.

Due to the fact that food manufacturing is one of the major drivers of the global environmental issues, there is a strong need to focus on sustainable manufacturing toward achieving long-term sustainability goals in food production of the United States. In this regard, current study assessed the direct and indirect environmental footprint of 33 U.S. food manufacturing sectors by using the Economic Input-Output Life Cycle Assessment (EIO-LCA) model. Then, a non-parametric mathematical optimization tool, namely Data Envelopment Analysis (DEA), is utilized to benchmark the sustainability performance of food manufacturing sectors by using the results of the EIO-LCA model. Next, sustainability performance indices (SPIs), rankings, target improvements, and sensitivity of environmental impact indicators are presented. The average SPI score of U.S. food manufacturing sectors is found as 0.76. In addition, 19 out of 33 food sectors are found as inefficient where an average of 45–71% reduction is indicated for various environmental impact categories. Analysis results also indicate that supply chains of food manufacturing sectors are heavily responsible for the impacts with over 80% shares for energy, water and carbon footprint, fishery and grazing categories. Especially, animal (except poultry) slaughtering, rendering and processing sector is found as the most dominant sector in most of the impact categories (ranked as 2nd in fishery and forest land). Sensitivity analysis indicated that forest land footprint is found to be the most sensitive environmental indicator on the overall sustainability performance of food manufacturing sectors.

Millions of culverts exist in the United States, and they are aging rapidly. Inspection of all the culverts consumes a lot of time and resources. Instead of inspecting each culvert every 5 years, this study presents a more intelligent approach to predict the condition of each culvert. An artificial neural network (ANN) model is built to assess the condition of the culverts based on culvert inventory data. The overall condition-rating predictions are compared with the condition rating based on manual inspection. The results of this study have shown that ANN was able to predict culvert adjusted overall rating with high precision, as the course of action score prediction rate was 100%. Sensitivity analysis of the ANN model is provided to assess the effect of variables. The goal of this study is to show that more intelligent culvert-management systems could be devised by taking advantage of artificial intelligence.

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.

Due to increasing concerns related to emerging environmental problems as a result of industrial activities, sustainable manufacturing has become a topic of considerable interest worldwide. In this study, Economic Input-Output Life Cycle Assessment (EIO-LCA) and Data Envelopment Analysis (DEA), a linear programming-based mathematical optimization model, were integrated to analyze the eco-efficiency of manufacturing sectors in the United States. This integration was achieved by aggregating different environmental pressures into a single eco-efficiency score. First, greenhouse gas emissions, energy use, water withdrawals, hazardous waste generation, and toxic releases of each manufacturing sector were quantified using the EIO-LCA model. Second, an input-oriented DEA multiplier model was developed. Third, eco-efficiency scores and rankings, target and performance improvement values of each environmental category were determined. Finally, the sensitivity of each environmental impact category was analyzed. Analysis results showed that five industrial sectors, such as “Petroleum and Coal Products Manufacturing”, “Food Manufacturing”, “Printing and Related Support Activities”, “Ordinance and Accessories Manufacturing”, and “Motor Vehicle Manufacturing” were 100% eco-efficient compared to other manufacturing sectors. On the other hand, approximately 90% of U.S. manufacturing sectors were found to be inefficient and require significant improvements in their life cycle performance. Among the environmental impact categories, energy use had the highest sensitivity on the eco-efficiency of U.S. manufacturing sectors, and therefore improved energy efficiency in industrial processes and successful policy making toward increasing the share of renewable energy utilization were highly recommended.

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 model...

The construction industry has considerable impacts on the environment, economy, and society. Although quantifying and analyzing the sustainability implications of the built environment is of great importance, it has not been studied sufficiently. Therefore, the overarching goal of this study is to quantify the overall environmental, economic, and social impacts of the U.S. construction sectors using an economic input–output-based sustainability assessment framework. In this research, the commodity-by-industry supply and use tables published by the U.S. Bureau of Economic Analysis, as part of the International System of National Accounts, are merged with a range of environmental, economic, and social metrics to develop a comprehensive sustainability assessment framework for the U.S. construction industry. After determining these sustainability assessment metrics, the direct and indirect sustainability impacts of U.S construction sectors have been analyzed from a triple bottom-line perspective. When analyzing the total sustainability impacts by each construction sector, “Residential Permanent Single and Multi-Family Structures" and "Other Non-residential Structures" are found to have the highest environmental, economic, and social impacts in comparison with other construction sectors. The analysis results also show that indirect suppliers of construction sectors have the largest sustainability impacts compared with on-site activities. For example, for all U.S. construction sectors, on-site construction processes are found to be responsible for less than 5 % of total water consumption, whereas about 95 % of total water use can be attributed to indirect suppliers. In addition, Scope 3 emissions are responsible for the highest carbon emissions compared with Scopes 1 and 2. Therefore, using narrowly defined system boundaries by ignoring supply chain-related impacts can result in underestimation of triple bottom-line sustainability impacts of the U.S. construction industry. Life cycle assessment (LCA) studies that consider all dimensions of sustainability impacts of civil infrastructures are still limited, and the current research is an important attempt to analyze the triple bottom-line sustainability impacts of the U.S. construction sectors in a holistic way. We believe that this comprehensive sustainability assessment model will complement previous LCA studies on resource consumption of U.S. construction sectors by evaluating them not only from environmental standpoint, but also from economic and social perspectives.

With the dramatic increase of traffic volume, traffic congestion has become a topic of considerable interest in the United States. Congestion has resulted in enormous economic and environmental losses, and the use of intelligent transportation systems (ITS) has been found to be an effective solution to relieve congestion in urbanized areas. The study presented in this paper aimed to advance the body of knowledge on sustainability impacts through a triple bottom line (TBL) evaluation of congestion relief in Florida. Rather than consider only the direct economic benefits as in traditional projects, this study strove to fill the gap for decision makers in the analysis of sustainability impacts from a holistic perspective. A critical approach to this research was to include both the direct and the indirect environmental, economic, and ecologic impacts associated with the chain of supply paths of ITS. To meet this goal, economic input-output tables, published by the Bureau of Economic Analysis, were linked to various TBL sustainability indicators to gain better insight into the sustainability impact of congestion relief. Study results indicated that 1.38 E+05 tons of greenhouse gas emissions (tons of carbon dioxide equivalent) and 3.00 E+04 global hectares of carbon dioxide uptake land were saved in Florida in 2010. Moreover, annual delay reduction costs savings were $420 million, of which the net fuel-based savings were $17.2 million.

In 2008 the City of Orlando, Florida, embarked on construction of a new Amway Center to replace the existing Amway Arena. The new center was designed to host a variety of events such as concerts, arena football, graduation ceremonies, National Collegiate Athletic Association basketball, and the National Basketball Association's (NBA's) Orlando Magic home games. The Amway Center, which opened in October 2010, had a capacity of 19,000 patrons for NBA games. The Amway Center was adjacent to Interstate 4 and State Road 408 in the downtown core. To prepare for the Amway Center's event management challenges, the City of Orlando's Transportation Engineering Department utilized smart event traffic management techniques, which integrated the following system and plan components: intelligent transportation system infrastructure (software and hardware), traffic control device upgrades, regional planning efforts, and state-of-the-art safety and security protocols. These components also helped minimize the environmental impacts of traffic congestion. When designing smart event plans, it is important to consider high-threshold events such as the NBA All-Star Game for system performance and to understand the balance required for operational efficiency and security in today's global climate. In addition, infrastructure performance is critical during high-profile events so that the local economy can benefit and the municipality can stay competitive to attract more of these types of events. Finally, the resultant operational efficiency is vital for reducing the carbon footprint and negative societal impacts.

Previous performance-based earthquake engineering investigations on highway bridges focused on repair cost and repair time metrics. These are primarily related to economic life-cycle cost analysis decision-making strategies for evaluating the vulnerability of bridges. However, environmental impacts considered during life-cycle assessment are also of interest. A methodology for quantifying the sustainability of bridge designs using carbon footprint as the performance metric is presented. Point estimates of the carbon footprint, repair cost, and repair time are used to compute resilience of several bridge-ground scenarios. Several multispan reinforced concrete highway overpass bridges in California from previous studies were selected for study because post-earthquake probabilistic repair cost and time data are already available. It is demonstrated that the disaggregation of the structure into performance groups, or components that are repaired together, and the probabilistic methodology previously e...

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.

The need for sustainable development is increasing as the industrial and service activities keep putting such a strain on the natural functions of the Earth, thus the ability of the planet’s to sustain future generations. Since most of the industrial and service activities are provided via transportation, it is one of the most crucial elements of sustainable development. In this paper, US highway system sustainability problem is studied. System dynamics modeling approach is employed due to the causal relationships and feedback loops that are observed in the problem structure. The reference mode is considered as the increasing CO2 emission trend. The objective is to meet the Liberman and Warner Climate Act’s targets by 2050. Three potential strategies for policy making are tested with the developed dynamic simulation: fuel efficiency, public transportation and electric vehicle usage. The results indicate that hybrid implementation of individual policies has a crucial impact on the success of policy making.

The 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...

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. © 2012 American Society of Civil Engineers. CE Database subject headings: Construction materials; Sustainable development; Data analysis; Computer programming. Author keywords: Eco-efficiency; Sustainable development; Data envelopment analysis; Linear programming.

An ecologically-based hybrid life cycle assessment model is used to evaluate the resource consumption and atmospheric emissions of continuously reinforced concrete and a hot-mix asphalt pavements. The cumulative mass and ecological resource consumption values are lower for continuously reinforced concrete, but the median values of cumulative energy and industrial energy consumption were lower for hot-mix asphalt. In addition, the use of reclaimed asphalt pavement results in a higher sensitivity for the ecological resource consumption of hot-mix asphalt compared to that of fly ash when use on the natural capital utilization of continuously reinforced concrete pavement. The cumulative and industrial exergy consumption values are significantly reduced with increases in reclaimed asphalt pavement and fly ash, and the use of low fuel transportation modes.

Warm-mix asphalt (WMA) has received considerable attention in the past few years as a potential solution for the reduction of energy consumption and emissions during production and construction of asphalt mixtures. However, many concerns and questions are still unanswered regarding its environmental benefits. Although several studies have been conducted to quantify the atmospheric emissions of WMA pavements, the direct and indirect role of ecological resource consumption was generally excluded in these studies. In this study, a thermodynamic based hybrid life cycle assessment model was developed to evaluate the environmental impacts of different types of WMA pavements and compare it to that of a conventional hot mix asphalt (HMA) one. The impacts on the ecosystem were calculated in terms of cumulative mass, energy, industrial exergy, and ecological exergy. Monte Carlo simulation was also conducted to analyze the variability of critical input parameters. The results of this study showed that although the mixing phase is important, it should not be the only phase to evaluate the decreased amount of atmospheric emissions of WMA pavements.

Algae cofiring scenarios in a 360 MW coal power plant were studied utilizing an ecologically based hybrid life cycle assessment methodology. The impacts on the ecological system were calculated in terms of cumulative mass, energy, industrial exergy, and ecological exergy. The environmental performance metrics, including efficiency, loading, and renewability ratios were also quantified to assess the sustainability of cofiring scenarios from a holistic perspective. The analysis results revealed that cumulative mass and ecological exergy consumption were higher for algae cofiring compared to single coal firing due to high material and energy inputs for the algae cultivation. On the contrary, total energy and industrial exergy utilization were reduced with an increasing share of algae cofiring where algae is dried with solar energy. Additionally, natural gas dried algae cofiring scenarios had a lower renewability ratio in comparison with single coal firing. The results of this study are vital for the policy makers to decide on more environmentally friendly algae cofiring options by considering the potential impacts on ecological system.

Attaining higher levels of system integration is seen as the primary goal of construction enterprise information systems (CEIS). Increased system integration resulting from CEIS implementation is expected to lead to numerous benefits. These benefits encompass information technology infrastructure and 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. Through the use of statistical analysis, 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 impleme...

Built environment has a substantial impact on the economy, society, and the environment. Along with the increasing environmental consideration of the building impacts, the environmental assessment of buildings has gained substantial importance in the construction industry. In this study, an artificial neural network model is built to predict cost premium of LEED certified green buildings based on LEED categories. To verify the viability of the model, multiple regression analysis is used as a benchmarking model. After validating the prediction power of the neural network model, a global sensitivity analysis is utilized to provide a better understanding of possible relationships between input and output variables of the prediction model. Sustainable Sites and Energy & Atmosphere LEED categories were found to have the highest sensitivity in cost premium prediction. In this study, our goal was to reveal the significant relationships between LEED categories and the cost premium, and offer a decision model that can guide owners to estimate cost premiums based on sought LEED credits.

To successfully evaluate investments related to integrated information management in the construction industry, causal loop diagramming was used to depict the qualitative system dynamics model for the study of the dynamics of construction enterprise resource planning systems (C-ERP). With the aid of system dynamics principles, together with literature review and two case studies, the major variables that influence the successful evaluation of C-ERP in the construction industry were evaluated. The major variables identified were validated with data from a survey. The validation procedure quantified associations between variables and perceived benefits from C-ERP stakeholders of construction-related firms. The model described in this paper aims at providing a holistic understanding of the C-ERP dynamics in construction. With this model, researchers and industry practitioners can develop an insight into C-ERP investments in practice.

PurposeThe paper's purpose is to describe and examine the level of utilisation of construction enterprise information systems (CEIS) and to identify critical success factors and benefits from their implementation.Design/methodology/approachA survey was conducted to quantify the current situation of CEIS. The population consisted of stakeholders with reliable working knowledge of their firms' information systems. Statistical tests were conducted to draw conclusions from the data.FindingsA total of 48 per cent of the firms use enterprise resource planning packages, but only 4 per cent of these firms chose to implement the project management modules that are commercially available. Only 16 per cent of the respondents were satisfied with their current level of integration due to CEIS. There was no evidence to suggest a tendency for the integration level due to CEIS to increase together with the business geographical dispersion.Research limitations/implicationsThe rate of response to the survey was low (9 per cent), maybe due to the unavailability of CEIS in all the firms that were contacted. Further, research is needed to study the dynamics between management practices and their effect on CEIS adoption and level of integration.Practical implicationsIntegration efforts and software that are currently in use still do not fully address the concerns of industry practitioners. Associations found between the level of integration and project characteristics can be used to help IT stakeholders decide on the CEIS functions to adopt.Originality/valueThe strategic, operational and organizational benefits found from the implementation of CEIS are based on current data obtained from a targeted survey of construction related firms.

Sustainable transportation and mobility are key components and central to sustainable development. This research aims to reveal the macro-level social, economic, and environmental impacts of alternative vehicle technologies in the U.S. The studied vehicle technologies are conventional gasoline, hybrid, plug-in hybrid with four different all-electric ranges, and full battery electric vehicles (BEV). In total, 19 macro level sustainability indicators are quantified for a scenario in which electric vehicles are charged through the existing U.S. power grid with no additional infrastructure, and an extreme scenario in which electric vehicles are fully charged with solar charging stations. The analysis covers all life cycle phases from the material extraction, processing, manufacturing, and operation phases to the end-of-life phases of vehicles and batteries. Results of this analysis revealed that the manufacturing phase is the most influential phase in terms of socio-economic impacts compared to other life cycle phases, whereas operation phase is the most dominant phase in the terms of environmental impacts and some of the socio-economic impacts such as human health and economic cost of emissions. Electric vehicles have less air pollution cost and human health impacts compared to conventional gasoline vehicles. The economic cost of emissions and human health impact reduction potential can be up to 45% and 35%, respectively, if electric vehicles are charged through solar charging stations. Electric vehicles have potential to generate income for low and medium skilled workers in the U.S. In addition to quantified sustainability indicators, some sustainability metrics were developed to compare relative sustainability performance alternative passenger vehicles. BEV has the lowest greenhouse gas emissions and ecological land footprint per $ of its contribution to the U.S. GDP, and has the lowest ecological footprint per unit of its energy consumption. The only sustainability metrics that does not favor the BEV is the water-energy ratio, where the conventional gasoline vehicle performed best.

The electricity generation/supply and transportation sectors are the two largest contributors to greenhouse gas (GHG) emissions in the U.S., and vehicle-to-grid (V2G) technology is a rapidly emerging solution to reduce these emissions with the adoption of battery-electric (BE) vehicles. Deployments of BE transit and school buses are expected to have larger battery capacities than passenger vehicles, making them more feasible candidates for V2G service. Five electricity generation regions are considered for cash flow analysis of BE and diesel transit and school buses over their entire respective lifetimes with the allowance of V2G services’ net revenue. Besides, the environmental benefits of using the V2G system are studied in place of combustion power generation plants for the regulation services of each study region. Air emission externalities are another crucial issue for bus operations because buses are operated near highly populated areas, so these externalities are also studied in this research with the benefits of a V2G emission reduction potential taken into account. The analysis concluded that BE transit and school buses with V2G application have potential to reduce electricity generation related greenhouse-gas emissions by 1067 and 1420 tons of CO2 equivalence (average), and eliminate $13,000 and $18,300 air pollution externalities (average), respectively.

A multi-criteria optimization model has been developed to propose economically sound and environmentally benign construction waste management strategies for LEED© certified green buildings. First, an input-output based hybrid life cycle assessment model has been developed to quantify the net environmental impacts of different waste management options such as recycling, conventional landfilling and incineration. After quantifying the net environmental burdens associated with these waste management alternatives, a compromised programming model has been utilized to determine the optimum recycling strategy considering environmental and economic impacts, simultaneously.

Wind energy technologies have gained a tremendous interest worldwide to mitigate the environmental impacts related to power generation. In this study, we assess on-shore and off-shore by tracing all of the economy-wide supply chain requirements of wind power plants. The wind turbines consist of two onshore (V80-2.0 MW and V90-3.0 MW) and two offshore (V80-2.0 MW and V90-3.0 MW) turbines, all manufactured by the Vestas Wind Systems A/S.

The aim of this study is to analyze the environmental performance of 15 passenger transportation modes using a combined application of data envelopment analysis (DEA) and hybrid life cycle assessment (LCA). The proposed approach can offer vital guidance for decision makers regarding the relative environmental efficiency of transportation modes, and enhance the interpretation of LCA results.

Sustainability assessment tools are critical in the process of achieving sustainable development. Eco-efficiency has emerged as a practical concept which 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 using data envelopment analysis; a linear programming based mathematical approach. Life cycle assessment and life cycle cost are utilized to derive the eco-efficiency ratios, and data envelopment analysis is used to rank material alternatives. Developed mathematical models are assessed by selecting the most eco-efficiency exterior wall finish for a school building. Through this study, our 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.

THE environmental performance of various algae cofiring scenarios in a 300 MW coal-fired power plant were investigated using an ecologically based hybrid LCA model. Scenarios included cofiring options utilizing 100% Coal, 75% Coal & 25% Algae, 50% Coal & 50% Algae, 25% Coal & 75% Algae, and 100% Algae. These percentage values represent the share of energy production (MJ) by algae and coal resources to generate the same amount of electricity in the power plant.

The United States has the world's largest and busiest network of roads. There is a dire need to quantify sustainability for the highways of each state in the U.S. to ensure they are performing to this ability. The objective of this study is to develop an analytical tool that can be used to evaluate states holistically in terms of highway sustainability. Performance Indicators of states are used to derive the sustainability ratios and data envelopment analysis, a linear programming based mathematical approach, is used to evaluate the states with respect to highway sustainability. This approach does not require the use of a priori subjective weightings to evaluate the sustainability scores of states. Malmquist indices are calculated to identify the change in highway sustainability of these states over a period of time.

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 agentâ€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. Integruotas agentinis įrangos valdymas: koncepciniai sprendimai Santrauka Efektyvus įrangos valdymas yra labai svarbus sÄ—kmingos statybos įmonÄ—s veiklos veiksnys. NeadekvatÅ«s įrangos valdymo sprendimai, netaikant automatizuotų valdymo sistemų, yra subjektyvÅ«s. DÄ—l to dažnai statybos įmonÄ—, taip pat ir visa ekonomika gali patirti didelių nuostolių. Pagrindinis Å¡io straipsnio tikslas yra pristatyti agentinÄ™ įrangos valdymo sistemÄ… integracijai ir automatizacijai didinti ir kartu sprendimų priÄ—mimo klaidoms sumažinti. Naujausi agentinių technologijų tyrimai leidžia taikyti automatizuotÄ… ir integruotÄ… įrangos valdymo sistemÄ…. SiÅ«loma sistema remiasi esama statybos įmonių duomenų baze ir įdiegia belaides statybos įrangos technologijas automatizuotai duomenims integruoti. Raktiniai žodžiai: įrangos valdymas, agentinÄ—s technologijos, statybos pramonÄ—. First Published Online: 14 Oct 2010

In this study, it is tried to provide an estimation of damages imposed to a prototype office building as a result of earthquake occurrence. For this purpose, first a model is developed in BIM and then its properties are exported to a structural analysis software (opensees) in order to evaluate the structure's response under specific loading condition. Next, the outcome of opensees is used to identify the building's Damage State (DS). Then, the associated costs with each damage state are estimated by Performance Assessment Calculation Tool (PACT) provided by Federal Emergency Management Agency (FEMA). Finally, a Life Cycle Environmental Assessment is performed to identify the environmental impacts of the damages imposed to the building as a result of earthquake. For this purpose, embodied energy of the building is calculated and then based on the damage probability, the total embodied energy losses are calculated.