The analysis and prediction of the quantity and composition of household refuse

This thesis describes the development of a simple and accurate method for estimating the quantity and composition of household waste arisings. The method is based on the fundamental tenet that waste arisings can be predicted from information on the demographic and socio-economic characteristics of households, thus reducing the need for the direct measurement of waste arisings to that necessary for the calibration of a prediction model. The aim of the research is twofold: firstly to investigate the generation of waste arisings at the household level, and secondly to devise a method for supplying information on waste arisings to meet the needs of waste collection and disposal authorities, policy makers at both national and European level and the manufacturers of plant and equipment for waste sorting and treatment. The research was carried out in three phases: theoretical, empirical and analytical. In the theoretical phase specific testable hypotheses were formulated concerning the process of waste generation at the household level. The empirical phase of the research involved an initial questionnaire survey of 1277 households to obtain data on their socio-economic characteristics, and the subsequent sorting of waste arisings from each of the households surveyed. The analytical phase was divided between (a) the testing of the research hypotheses by matching each household's waste against its demographic/socioeconomic characteristics (b) the development of statistical models capable of predicting the waste arisings from an individual household and (c) the development of a practical method for obtaining area-based estimates of waste arisings using readily available data from the national census. The latter method was found to represent a substantial improvement over conventional methods of waste estimation in terms of both accuracy and spatial flexibility. The research therefore represents a substantial contribution both to scientific knowledge of the process of household waste generation, and to the practical management of waste arisings.

Economic efficiency when prices are not fixed:disentangling quantity and price efficiency

This paper proposes an approach to compute cost efficiency in contexts where units can adjust input quantities and to some degree prices so that through their joint determination they can minimise the aggregate cost of the outputs they secure. The model developed is based on the data envelopment analysis (DEA) framework and can accommodate situations where the degree of influence over prices ranges from minimal to considerable. When units cannot influence prices at all the model proposed reduces to the standard cost efficiency DEA model for the case where prices are taken as exogenous. In addition to the cost efficiency model, we introduce an additive decomposition of potential cost savings into a quantity and a price component, based on Bennet indicators. © 2014 Elsevier Ltd.

Edge centrality via the Holevo quantity

In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be computed in terms of the Holevo quantity, a well known quantum information theoretical measure. While computing the Von Neumann entropy and hence the Holevo quantity requires computing the spectrum of the graph Laplacian, we show how to obtain a simplified measure through a quadratic approximation of the Shannon entropy. This in turns shows that the proposed centrality measure is strongly correlated with the negative degree centrality on the line graph. We evaluate our centrality measure through an extensive set of experiments on real-world as well as synthetic networks, and we compare it against commonly used alternative measures.