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In recent years, the escalating risk of natural disasters caused by Near-Earth Objects (NEOs) has garnered heightened scrutiny, particularly in the aftermath of the 2013 Chelyabinsk event. This has prompted increased interest from governmental and supranational entities, leading to the formulation of various measures and strategies aimed at mitigating the potential threat posed by NEOs. This paper delves into the analysis of the 2011 AG5 asteroid within the context of small celestial bodies (e.g., asteroids, comets, or meteoroids) exhibiting resonant orbits with Earth's heliocentric revolution. Initial observations in 2011 raised alarms regarding the asteroid's orbital parameters, indicating a significant risk of Earth impact during its resonant encounter in 2040. Subsequent observations, however, mitigated these concerns. Here, we manipulate the orbital elements of the 2011 AG5 asteroid to simulate its behavior as a virtual impactor (a virtual asteroid whose orbit could impact Earth). This modification facilitates the assessment of impact mitigation resulting from a deflection maneuver utilizing a kinetic impactor. The deflection maneuver, characterized as an impulsive change in the asteroid's momentum, is executed during a resonant encounter occurring approximately two decades before the potential impact date. The paper systematically evaluates the dependence of the deflection maneuver's efficacy on critical parameters, including the position along the orbit, epoch, and momentum enhancement factor. [ABSTRACT FROM AUTHOR]

Considering the increasing demand for more sustainable products across many industries, eco-labels are a useful tool for communicating the sustainability-related performance of a product to the eco-conscious consumer. However, the abundance of different eco-labels and a lack of harmonization concerning their assessment methods can hamper their effectiveness. To address these shortcomings, this paper considers the methods employed by eco-labels in the textile and clothing industry to assess the sustainability-based performance of products. Using a sample of 10 eco-labels from the Ecolabel Index, a new framework for classifying eco-labels based on their assessment methods is developed. The framework includes two categories of label assignments ((i) binary and (ii) different levels of performance) and six types of assessment methods. These types are characterized according to the decision support features employed by the labels, such as lists of mandatory criteria, minimum (average) scores, percentage scores, and the weighting of sub-categories. The proposed framework shows the benefits of cascading decision science notions in the eco-labeling domain. It provides a harmonized vocabulary of components (i.e., a roadmap) to perform a consistent and traceable advancement of eco-labels. Consequently, it can be expanded at present to allow for the classification of other eco-labels in the textile and clothing industry and beyond. [ABSTRACT FROM AUTHOR]

The interest in studying energy systems' resilience is increasing due to a rising awareness of the importance of having a secure energy supply. This growing trend is a result of a series of recent disruptions, among others also affecting electricity systems. Therefore, it is of crucial importance for policymakers to determine whether their country has a resilient electricity supply. Starting from a set of 12 indicators, this paper uses data envelopment analysis (DEA) to comprehensively evaluate the electricity supply resilience of 140 countries worldwide. Two DEA models are applied: (1) the original ratio-based Charnes, Cooper, and Rhodes (CCR) model and (2) a novel hybrid framework for robust efficiency analysis incorporating linear programming and Monte Carlo simulations. Results show that the CCR model deems 31 countries as efficient and hence lacks the capability to differentiate them. Furthermore, the CCR model considers only the best weight vectors for each country, which are not necessarily representative of the overall performance of the countries. The robustness analysis explores these limitations and identifies South Korea, Singapore and Canada as the most resilient countries. Finally, country analyses are conducted, where Singapore's and Japan's performances and improvement potentials are discussed. [ABSTRACT FROM AUTHOR]

The impacts of energy accidents are of primary interest for risk and resilience analysts, decision makers, and the general public. They can cause human health and environmental impacts, economic and societal losses, which justifies the interest in developing models to mitigate these adverse outcomes. We present a classification model for sorting energy accidents in the natural gas sector into hazard classes, according to their potential fatalities. The model is built on decision rules, which are knowledge blocks in the form of "if (condition), then (classification to hazard class x)". They were extracted by the rough sets method using natural gas accident data from 1970–2016 of the Energy-related Severe Accident Database (ENSAD) of the Paul Scherrer Institut (PSI), the most authoritative information source for accidents in the energy sector. This was the first attempt to explore the relationships between the descriptors of energy accidents and the consequence (fatalities). The model was applied to a set of hypothetical accidents to show how the decision-making process could be supported when there is an interest in knowing which class (i.e., low, medium, high) of fatalities an energy accident could cause. The successful use of this approach in the natural gas sector proves that it can be also adapted for other energy chains, such as oil and coal. [ABSTRACT FROM AUTHOR]