Effectiveness of policy against illegal disposal of waste

Our objective is to analyze the effectiveness, against the illegal disposal of waste, of a licensing system that has been introduced in a waste management policy. We theoretically find enforcement leverage in the licensing system, and then examine the theoretical result empirically. The results suggest that extending liability to disposers, which forms the basis of the enforcement leverage, deters illegal disposal more effectively than increasing penalties for illegal disposal. We also obtain evidence of transboundary movement of illegal disposal, and find how the court determines penalties for illegal disposal.

On the effectiveness of a license scheme for E-waste recycling : the challenge of China and India

It is well known that China and India have been recycling centers of WEEE, especially printed circuit boards, and that serious environmental pollution in these countries has been generated by improper recycling methods. After the governments of China and India banned improper recycling by the informal sector, improper recycling activities spread to other places. Then, these governments changed their policies to one of promoting proper recycling by introducing a scheme, under which E-waste recycling requires a license issued by the government. In this paper, the effectiveness of that license scheme is examined by means of an economic model. It can be shown that the license scheme can work effectively only if disposers of E-waste have a responsibility to sell E-waste to license holders. Our results run counter to the idea that international E-waste trade should be banned and provide an alternative solution to the problem.

Antisocial behaviour orders : Unanticipated directions in social network site development

Abstract: Social network technologies, as we know them today have become a popular feature of everyday life for many people. As their name suggests, their underlying premise is to enable people to connect with each other for a variety of purposes. These purposes however, are generally thought of in a positive fashion. Based on a multi-method study of two online environments, Habbo Hotel and Second Life, which incorporate social networking functionality, we she light on forms of what can be conceptualized as antisocial behaviours and the rationales for these. Such behaviours included: scamming, racist/homophobic attacks, sim attacks, avatar attacks, non-conformance to contextual norms, counterfeiting and unneighbourly behaviour. The rationales for sub behaviours included: profit, fun, status building, network disruption, accidental acts and prejudice. Through our analysis we are able to comment upon the difficulties of defining antisocial behaviour in such environments, particularly when such environments are subject to interpretation vis their use and expected norms. We also point to the problems we face in conducting our public and private lives given the role ICTs are playing in the convergence of these two spaces and also the convergence of ICTs themselves.

An observational analysis of the impact of board dynamics and directors’ participation on perceived board effectiveness

This study addresses calls for closer examination of board dynamics by offering an inside view of director interactions. Video-observations of three board meetings at each of two Australian corporations matched with director interviews and secondary data reveal distinct patterns of director interactions, their sources of variation and impact on perceived board effectiveness. Our data reveal that director interactions are multi-dimensional and dynamic: while group interactions across agenda items are similar, with a few directors leading the discussion, the contributing directors change across items. Moreover, directors’ inclusiveness and evenness of participation are associated with higher perceptions of board effectiveness. Last, we find that director interactions change with the nature of the items, board climate and board meeting arrangements. The study contributes to the literature by moving beyond the individual-level analysis of directors’ skills or independence, and offering a detailed view of how the joint group and individual dimensions of board dynamics affect board functioning.

Pipeline renewal decision support : a case study using LinEAR in a water pipeline network

This paper presents a case study for the application of a Linear Engineering Asset Renewal decision support software tool (LinEAR) at a water distribution network in Australia. This case study examines how the LinEAR can assist water utilities to minimise their total pipeline management cost, to make a long-term budget based on mathematically predicted expenditure, and to present calculated evidence for supporting their expenditure requirements. The outcomes from the study on pipeline renewal decision support demonstrate that LinEAR can help water utilities to improve the decision process and save renewal costs over a long-term by providing an optimum renewal schedules. This software can help organisation to accumulate technical knowledge and prediction future impact of the decision using what-if analysis.

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

The feasibility, delivery and cost effectiveness of drink driving interventions : a qualitative analysis of professional stakeholders

Many intervention programs have been designed to decrease the rate of drink driving by altering the behavioural characteristics that may lead a person to drink and drive. However, most programs target high risk and repeat offenders. There is very little research on the feasibility and effectiveness of first offender programs. This project is part of a larger program of research that focuses on first time offenders, in order to reduce the rate of subsequent drink driving which may result in a repeat offence. A number of professional stakeholders were approached and interviewed with a view to capturing and reflecting current drink driving related concerns while developing an intervention in the context of Australian drink driving related legislation. The qualitative interviews involved open ended questioning which led to the themes discussed in the analysis. Included in the interviews were senior representatives from the Magistrates Court, Queensland Transport, Probation & Parole, Queensland Corrective Services, Royal Automobile Club Queensland (RACQ), Intraface Consulting (drug & alcohol EAP), Brisbane Police Prosecution Corps, Queensland Police Service and private practice psychology. Issues such as delivery of interventions, feasibility and cost-effectiveness were discussed, as were potential content and design. It was generally agreed that a tailored online intervention imposed as a sentencing option would be the most effective for first time offenders in terms of cost, ease of delivery and feasibility. The development of an online intervention program for first offenders is widely supported by professional stakeholders.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.