Search results

Avoided emissions refer to greenhouse gas emission reductions that are a result of using a product or are emission removals due to a decision or an action. Although there is no uniform standard for calculating avoided emissions, market actors have started referring to avoided emissions as “Scope 4” emissions. By default, making a claim about Scope 4 emissions gives an appearance that this Scope of emissions is a natural extension of the existing and accepted Scope-based emissions accounting framework. The purpose of this study is to explore the implications of this assumed legitimacy.

Via a desktop review and interviews, we analyse extant Scope 4 company reporting, associated accounting methodologies and the practical implications of Scope 4 claims.

Upon examination of Scope 4 emissions and their relationship with Scopes 1, 2 and 3 emissions, we highlight a dynamic and interdependent relationship between quantification, commensuration and standardization in emissions accounting. We find that extant Scope 4 assessments do not fit the established framework for Scope-based emissions accounting. In line with literature on the territorializing nature of accounting, we call for caution about Scope 4 claims that are a distraction from the critical work of reducing absolute emissions.

We examine the implications of assumed alignment and borrowed legitimacy of Scope 4 with Scope-based accounting because Scope 4 is not an actual Scope, but a claim to a Scope. This is as an act of accounting territorialization.

The current advancements in technologies and the internet industry provide users with many innovative digital devices for entertainment, communication and trade. However, simultaneous development and the rising sophistication of cybercrimes bring new challenges. Micro businesses use technology like how people use it at home, but face higher cyber risks during riskier transactions, with human error playing a significant role. Moreover, information security researchers have often studied individuals’ adherence to compliance behaviour in response to cyber threats. The study aims to examine the protection motivation theory (PMT)-based model to understand individuals’ tendency to adopt secure behaviours.

The study focuses on Australian micro businesses since they are more susceptible to cyberattacks due to the least security measures in place. Out of 877 questionnaires distributed online to Australian micro business owners through survey panel provider “Dynata,” 502 (N = 502) complete responses were included. Structural equational modelling was used to analyse the relationships among the variables.

The results indicate that all constructs of the protection motivation, except threat susceptibility, successfully predict the user protective behaviours. Also, increased cybersecurity costs negatively impact users’ safe cyber practices.

The study has critical implications for understanding micro business owners’ cyber security behaviours. The study contributes to the current knowledge of cyber security in micro businesses through the lens of PMT.

This paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.

In this paper, a null-space-based task-priority adjustment approach is developed to avoid collisions. The method establishes the relative position of the obstacle and the robot arm by defining the “link space,” and then the priority of the collision avoidance task and the end-effector task is adjusted according to the relative position by introducing the null space task conversion factors.

Numerical simulations demonstrate that the proposed method can realize collision-free maneuvers for redundant manipulators and guarantee the tracking precision of the end-effector task. The experimental results show that the method can avoid dynamic obstacles in redundant manipulator welding tasks.

A new formula for task priority adjustment for collision avoidance of redundant manipulators is proposed, and the original task tracking accuracy is guaranteed under the premise of safety.

This study aims to gain an understanding from parents and teachers about the types of food provision practices and venues, and the food-related policies and rules in primary schools in Australia; and investigate any differences in the presence of policies and rules based on the school location and school type.

Data were collected via two online surveys from August 2019 to March 2020. Descriptive statistics were employed to analyse quantitative responses. Respondents' written responses to food-related policies were categorised into groups.

The two most common food provision services were canteen and lunch order services (mentioned by 72 and 55% of respondents, respectively). Of the 425 respondents whose schools had a canteen (parents and teachers together), 62% reported their school implements a healthy school canteen policy. Significantly more parents compared to teachers, and more respondents from government schools compared to non-government schools stated that their school had implemented such a policy. Approximately half of the respondents (47%) stated their school had implemented other food-related policies and/or rules. These policies or rules belonged to four categories: avoiding certain foods, avoiding food sharing, avoiding food packages and promoting healthy eating.

This study shows the disparities exist in implementing food-related policies among primary schools in Australia. Nutrition promoters and policy planners should consider these results and find the best mechanisms to minimise the gaps in policy implementation.

Higher education institutions (HEIs) play a pivotal role in fostering economic development by cultivating skilled workforce and generating knowledge and innovation. However, HEIs may pose a potential risk to sustainable economic development due to the generation of food waste inside their campus canteens. Therefore, this study aims to examine the influence of attitude, subjective norm (SN), perceived behavior control (PBC), religiosity and pro-social behavior among State Islamic Religious College (SIRC) students on their intention to avoid food waste behavior. This study also focused on the mediating role of the three original theory of planned behavior (TPB) variables and pro-social behavior in the relationship between religiosity and the intention to reduce food waste.

Questionnaires were used to collect data from 443 students at SIRC. The collected data were processed and analyzed using structural equation modelling to test direct, indirect and mediating effects.

The empirical results indicated that the eagerness of students at SIRC to reduce their behavior of leaving food behind can be driven by their negative attitudes or views toward food waste, the practice of religious teachings in their lives, the belief that they can avoid food waste and their concern for the environment. The empirical results reveal that even though religiosity influences SN, it is unable to strengthen the relationship between religiosity and the desire to be anti-food waste.

In addition to contributing to the food waste literature in the context of eating behavior, the results of this study have theoretical and practical implications.

To assess SIRC students’ behavioral intentions to avoid food waste behavior, this study used a contemporary setting to measure attitude, SN, PBC, religiosity and pro-social behavior, so strengthening the TPB’s empirical underpinning.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.