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

Clinical Study Spinal Anaesthesia with Hyperbaric Prilocaine in Day-Case Perianal Surgery: Randomised Controlled Trial

Background. The local anaesthetics used in day-case spinal anaesthesia should provide short recovery times. We aimed to compare hyperbaric prilocaine and bupivacaine in terms of sensory block resolution and time to home readiness in day-case spinal anaesthesia. Methods. Fifty patients undergoing perianal surgery were randomized into two groups. The bupivacaine-fentanyl group (Group B) received 7.5 mg, 0.5% hyperbaric bupivacaine + 20 g fentanyl in total 1.9 mL. The prilocaine-fentanyl group (Group P) received 30 mg, 0.5% hyperbaric prilocaine + 20 g fentanyl in the same volume. Results. Time to L1 block and maximum block was shorter in Group P than in Group B (Group P 4.6 ± 1.3 min versus Group B 5.9 ± 01.9 min, = 0.017, and Group P 13.2 ± 7.5 min versus Group B 15.3 ± 6.6 min, = 0.04). The time to L1 regression and S3 regression of the sensorial block was significantly shorter in Group P than in Group B (45.7 ± 21.9 min versus 59.7 ± 20.9 min, = 0.024, and 133.8 ± 41.4 min versus 200.4 ± 64.8 min, < 0.001). The mean time to home readiness was shorter for Group P than for Group B (155 ± 100.2 min versus 207.2 ± 62.7 min ( < 0.001)). Conclusion. Day-case spinal anaesthesia with hyperbaric prilocaine + fentanyl is superior to hyperbaric bupivacaine in terms of earlier sensory block resolution and home readiness and the surgical conditions are comparable for perianal surgery

Temporal Orientation and its Relationships with Organizationally Valued Outcomes: Results from a 14 Country Investigation

In this investigation we were concerned with the cultural covariates of temporal orientation in 14 different national contexts. Data were collected from United States of America (US), Australia, Germany, Poland, Chile, Venezuela, Turkey, United Arab Emirates (UAE), India, Indonesia, Malaysia Japan, South Korea and China. Analyses show that collectivistic cultural orientation tends to be relatively important in the prediction of three facets of temporal orientation (i.e. emphasis on planning and scheduling; sense of time and attitude towards time)

Intuitionistic Fuzzy Multi-Criteria Decision Making Framework Based On Life Cycle Environmental, Economic And Social Impacts: The Case Of U.S. Wind Energy

Intuitionistic Fuzzy Set theory can be used in conjunction with environmentally extended input–output based life cycle assessment (EE-IO-LCA) models to help decision makers to address the inherent vagueness and uncertainties in certain sustainable energy planning problems. In this regard, the EE-IO-LCA model can be combined with an intuitionistic fuzzy set theory for a multi-criteria decision making (MCDM) application with a set of environmental and socio-economic indicators. To achieve this goal, this study proposes the use of the Technique for Order of Preference by Similarity to Ideal Solution method to select the best wind energy alternative for a double layer MCDM problem, which requires expert judgments to simultaneously apply appropriate weighting to each life cycle phase and sustainability indicator to be considered. The novelty of this research is to propose a generic 9-step fuzzy MCDM method to solve sustainable energy decision-making problems using a combination of three different techniques: (1) an intuitionistic fuzzy entropy method to identify the individual importance of phases and criteria; (2) an IFWGA operator to establish a sub-decision matrix with the weights applied to all relevant attributes; and (3) an IFWAA operator to build a super-decision matrix with the weights applied to all of the life-cycle phases considered. This proposed method is then applied as a case study for sustainable energy planning, specifically for the selection of V80 and V90 onshore and offshore wind turbines to be installed in the United States. It is strongly believed that this methodology will provide a vital guidance for LCA practitioners in the future for selecting the best possible energy alternative under an uncertain decision-making scenario

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

Ranking The Sustainability Performance Of Pavements: An Intuitionistic Fuzzy Decision Making Method

In this research, we proposed a fuzzy multi-criteria decision making method which is applied for ranking the life cycle sustainability performance of different pavement alternatives constructed with hot-mix and warm-mix asphalt mixtures. This method consisted of four different techniques such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to select the best pavement alternative, the intuitionistic fuzzy entropy method to identify the importance of phases and criteria, the intuitionistic fuzzy weighted geometric averaging operator to establish a sub-decision making matrix based on weights of attribute, and the intuitionistic fuzzy weighted arithmetic averaging operator to build a super decision matrix depending on weights of different life cycle phases. Based on research findings, a synthetic wax-type warm-mix asphalt additive is selected as the best alternative among the pavement alternatives. In addition, conventional hot-mix asphalt is found to be the second best option compared to other mixtures. © 2013 Elsevier B.V

Application Of The Topsis And Intuitionistic Fuzzy Set Approaches For Ranking The Life Cycle Sustainability Performance Of Alternative Vehicle Technologies

This research involves two novel elements to advance the body of knowledge in existing sustainability assessment frameworks for alternative vehicle technologies. First, we developed an input-output based hybrid life cycle sustainability assessment model using several macro-level social, economic, and environmental indicators, taking into consideration the manufacturing of vehicles and batteries, operation, and end-of-life phases. Second, the results of a hybrid life cycle sustainability assessment for different conventional and alternative vehicles technologies (internal combustion electric vehicles, hybrid electric vehicles, plug-in-hybrid electric vehicles, and battery electric vehicles) are incorporated into the Technique for Order-Preference by Similarity to Ideal Solution and Intuitionistic Fuzzy Sets. Two policy scenarios are considered in this analysis, with Scenario 1 being based on existing electric power infrastructure in the U.S. with no additional infrastructure requirements, while Scenario 2 is an extreme scenario in which the electricity to power electric vehicles is generated exclusively via solar charging stations. The Intuitionistic Fuzzy Multi-Criteria Decision Making and Technique for Order Preference by Similarity to Ideal Solution methods are then utilized to rank the life cycle sustainability performance of alternative passenger vehicles. Furthermore, since expert judgments play an important role in determining the relative performance of alternative vehicle technologies, a sustainability triangle analysis is also presented to show how the weighting applied to each dimension affects the selection of different alternatives. The results indicate that hybrid and plug-in hybrid electric vehicles are the best alternatives for both Scenarios 1 and 2 when all of the indicators are considered. On the other hand, the ranking of vehicles changes significantly when each of the environmental, economic, and social indicators are evaluated individually. This proposed method can be a useful decision making platform for decision-makers to develop more effective policies and guide the offering of incentives to the right domains for sustainable transportation