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

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

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

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

A shift to green cybersecurity sustainability development: Using triple bottom-line sustainability assessment in Qatar transportation sector

Green cybersecurity is the emerging trend in the new era and this green cybersecurity technology minimizes the negative effects of IT operations and implements a green sustainable environment. Therefore, the study conceptually draws the concept of green cybersecurity by applying the theory of reasoned action (TRA) assumptions that logically support green information technology acceptance. Using a convenient sampling, the data were collected from Qatar transport industries, particularly the IT experts and managers, to get responses on the implementation of green cybersecurity and sustainability of 5 transport companies in Doha, Qatar. Using Smart PLS-SEM, the study employed the SEM technique to test the proposed hypotheses. The results reported that green cybersecurity’s control/position, integrity, and authenticity significantly and positively influenced TBL sustainability, but confidentiality, availability, and utility do not. The implementation of industry 4.0 makes them accessible and more effective to ensure TBL sustainable development in the transport industries in Qatar. Applying green cybersecurity in this setting will improve services in transportation sector. A green cybersecurity platform will make it a point to systematically search for and promote innovations made possible by smart green technologies to avoid carbon-emission vehicles. Through the efficient and cutting-edge green, cybersecurity will be Qatar’s transportation sector’s primary responsibility to contribute to Qatar’s sustainable development. In order to accomplish this goal, the regulator must create and implement it. In addition, it emphasizes the importance of adopting green cybersecurity to confront the difficulties facing city transportation all over Qatar as a foundational component of achieving long-term sustainable development

Integrated modelling for sustainability assessment and decision making of alternative fuel buses

In this paper, a hybrid life cycle sustainability assessment (LCSA) model integrating multi region input–output analysis with novel multi-criteria decision-making techniques is proposed to assess three different fuel alternatives: compressed natural gas (CNG), electric buses (EBs), and diesel buses (DBs). A global hybrid LCSA model first quantified the environmental, economic, and social impacts of alternative fuel buses. The results were investigated in terms of multiple combinations of manufacturing and end-of-life scenarios by encompassing impacts embedded in the global supply chains taking Qatar as a case applied to the proposed model. The Interval-Valued Neutrosophic Fuzzy (IVNF)-Analytic Hierarchy Process with the Combined Compromise Solution (CoCoSo) approach is used to rank the alternative fuel buses based on their corresponding sustainability performance. The proposed model will help in quantitatively capturing the macrolevel life cycle socioeconomic and environmental impacts along with optimally selecting alternatives to support sustainable urban transport policy towards a net-zero transportation system globally

How sustainable is liquefied natural gas supply chain? An integrated life cycle sustainability assessment model

Integrating sustainability into the distribution network process is a significant problem for any industry hoping to prosper or survive in today's fast-paced environment. Since gas is one of the world's most important fuel sources, sustainability is more important for the gas industry. While such environmental and economic effects have been extensively researched in the literature, there is little emphasis on the full social sustainability of natural gas production and supply chains in terms of the triple bottom line. This research aims to perform the first hybrid life cycle sustainability assessment (LCSA) of liquefied natural gas and evaluate its performance from the natural gas extraction stage to LNG regasification after delivery through maritime transport carriers. LCSA is used for estimating the social, economic, and environmental impacts of processes, and our life cycle model included the multi-region input–output analysis, Aspen HYSYS, and LNG maritime transport operations sustainability assessment tools. The results spot the light on the most contributors of CO2-eq emission. It is found that LNG loading (export terminal) is the source that generated the highest carbon footprint, followed by the MDEA sweetening unit with the contribution of 40% and 24%, respectively. Socially, around 73% of human health impact comes from SRU and TGTU units which are the most contributors to the particulate matter emission. Based on the interpretation of life cycle results, the environmental indicators show better performance in the pre-separation unit and LNG receiving terminal representing a sustainability factor equal to 1. In terms of social and economic impacts, the natural gas extraction stage presents the best performance among all other stages, with a sustainability factor equal to 1. Based on this study's findings, an integrated framework model is proposed. Various suggestions for sustainability strategies and policies that consider business sustainability and geopolitics risk are presented

How Can Collaborative Circular Economy Practices in Modular Construction Help Fédération Internationale de Football Association World Cup Qatar 2022 to Achieve Its Quest for Sustainable Development and Ecological Systems?

Embarking on the World Cup journey with circular collaborative strategies can positively impact the environment and socioeconomic outcomes to prosper development at the center of sustainability. World Cup mega-events are set with overriding priorities in cutting down environmental footprints to accelerate sustainable development across the Fédération Internationale de Football Association movement to leave an enduring legacy post-event in global sports. This paper conducts the first of its kind comprehensive critical analysis on ecological quality in life cycle impact assessment for 2022 Fédération Internationale de Football Association World Cup modular container stadiums in Qatar. A “cradle-to-cradle” life cycle assessment, including the material and resource production, construction, operation, and end-of-life (EOL) phase, is analyzed in this study, taking the case of Ras Abu Aboud stadium. Ecoinvent v3.7.1 life cycle inventory database was used to quantify the ecosystem damage-related impacts. Two scenarios were considered for the operation phase: scenario 1 (single year of operation) and scenario 2 (30 years of operation). A sensitivity analysis was used to understand the extent of impact per category indicator subject to material quantity variations. The results showed that the planned circularity contributed to savings in the EOL phase of more than 4.26 × 107 species.year compared with 1.7 species.year across the overall life-cycle impacts. Several perspective-based circular and sharing economy scenarios were assessed to reveal the benefits of circular collaborative economy applications in leveraging possible ecological burdens before, during, and post-mega events in sustainable construction. This research acts as a backbone for future single-sport mega-events to attempt to transition to a carbon-neutral, fully sustainable event with an everlasting legacy

Understanding the energy consumption and greenhouse gas emissions and the implication for achieving climate change mitigation targets

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Quantifying the daily economic impact of extreme space weather due to failure in electricity transmission infrastructure

Extreme space weather due to coronal mass ejections has the potential to cause considerable disruption to the global economy by damaging the transformers required to operate electricity transmission infrastructure. However, expert opinion is split between the potential outcome being one of a temporary regional blackout and of a more prolonged event. The temporary blackout scenario proposed by some is expected to last the length of the disturbance, with normal operations resuming after a couple of days. On the other hand, others have predicted widespread equipment damage with blackout scenarios lasting months. In this paper we explore the potential costs associated with failure in the electricity transmission infrastructure in the U.S. due to extreme space weather, focusing on daily economic loss. This provides insight into the direct and indirect economic consequences of how an extreme space weather event may affect domestic production, as well as other nations, via supply chain linkages. By exploring the sensitivity of the blackout zone, we show that on average the direct economic cost incurred from disruption to electricity represents only 49% of the total potential macroeconomic cost. Therefore, if indirect supply chain costs are not considered when undertaking cost-benefit analysis of space weather forecasting and mitigation investment, the total potential macroeconomic cost is not correctly represented. The paper contributes to our understanding of the economic impact of space weather, as well as making a number of key methodological contributions relevant for future work. Further economic impact assessment of this threat must consider multiday, multiregional event