Comprehensive embodied energy analysis framework

The assessment of the direct and indirect requirements for energy is known as embodied energy analysis. For buildings, the direct energy includes that used primarily on site, while the indirect energy includes primarily the energy required for the manufacture of building materials. This thesis is concerned with the completeness and reliability of embodied energy analysis methods. Previous methods tend to address either one of these issues, but not both at the same time. Industry-based methods are incomplete. National statistical methods, while comprehensive, are a ‘black box’ and are subject to errors. A new hybrid embodied energy analysis method is derived to optimise the benefits of previous methods while minimising their flaws. In industry-based studies, known as ‘process analyses’, the energy embodied in a product is traced laboriously upstream by examining the inputs to each preceding process towards raw materials. Process analyses can be significantly incomplete, due to increasing complexity. The other major embodied energy analysis method, ‘input-output analysis’, comprises the use of national statistics. While the input-output framework is comprehensive, many inherent assumptions make the results unreliable. Hybrid analysis methods involve the combination of the two major embodied energy analysis methods discussed above, either based on process analysis or input-output analysis. The intention in both hybrid analysis methods is to reduce errors associated with the two major methods on which they are based. However, the problems inherent to each of the original methods tend to remain, to some degree, in the associated hybrid versions. Process-based hybrid analyses tend to be incomplete, due to the exclusions associated with the process analysis framework. However, input-output-based hybrid analyses tend to be unreliable because the substitution of process analysis data into the input-output framework causes unwanted indirect effects. A key deficiency in previous input-output-based hybrid analysis methods is that the input-output model is a ‘black box’, since important flows of goods and services with respect to the embodied energy of a sector cannot be readily identified. A new input-output-based hybrid analysis method was therefore developed, requiring the decomposition of the input-output model into mutually exclusive components (ie, ‘direct energy paths’). A direct energy path represents a discrete energy requirement, possibly occurring one or more transactions upstream from the process under consideration. For example, the energy required directly to manufacture the steel used in the construction of a building would represent a direct energy path of one non-energy transaction in length. A direct energy path comprises a ‘product quantity’ (for example, the total tonnes of cement used) and a ‘direct energy intensity’ (for example, the energy required directly for cement manufacture, per tonne). The input-output model was decomposed into direct energy paths for the ‘residential building construction’ sector. It was shown that 592 direct energy paths were required to describe 90% of the overall total energy intensity for ‘residential building construction’. By extracting direct energy paths using yet smaller threshold values, they were shown to be mutually exclusive. Consequently, the modification of direct energy paths using process analysis data does not cause unwanted indirect effects. A non-standard individual residential building was then selected to demonstrate the benefits of the new input-output-based hybrid analysis method in cases where the products of a sector may not be similar. Particular direct energy paths were modified with case specific process analysis data. Product quantities and direct energy intensities were derived and used to modify some of the direct energy paths. The intention of this demonstration was to determine whether 90% of the total embodied energy calculated for the building could comprise the process analysis data normally collected for the building. However, it was found that only 51% of the total comprised normally collected process analysis. The integration of process analysis data with 90% of the direct energy paths by value was unsuccessful because: • typically only one of the direct energy path components was modified using process analysis data (ie, either the product quantity or the direct energy intensity); • of the complexity of the paths derived for ‘residential building construction’; and • of the lack of reliable and consistent process analysis data from industry, for both product quantities and direct energy intensities. While the input-output model used was the best available for Australia, many errors were likely to be carried through to the direct energy paths for ‘residential building construction’. Consequently, both the value and relative importance of the direct energy paths for ‘residential building construction’ were generally found to be a poor model for the demonstration building. This was expected. Nevertheless, in the absence of better data from industry, the input-output data is likely to remain the most appropriate for completing the framework of embodied energy analyses of many types of products—even in non-standard cases. ‘Residential building construction’ was one of the 22 most complex Australian economic sectors (ie, comprising those requiring between 592 and 3215 direct energy paths to describe 90% of their total energy intensities). Consequently, for the other 87 non-energy sectors of the Australian economy, the input-output-based hybrid analysis method is likely to produce more reliable results than those calculated for the demonstration building using the direct energy paths for ‘residential building construction’. For more complex sectors than ‘residential building construction’, the new input-output-based hybrid analysis method derived here allows available process analysis data to be integrated with the input-output data in a comprehensive framework. The proportion of the result comprising the more reliable process analysis data can be calculated and used as a measure of the reliability of the result for that product or part of the product being analysed (for example, a building material or component). To ensure that future applications of the new input-output-based hybrid analysis method produce reliable results, new sources of process analysis data are required, including for such processes as services (for example, ‘banking’) and processes involving the transformation of basic materials into complex products (for example, steel and copper into an electric motor). However, even considering the limitations of the demonstration described above, the new input-output-based hybrid analysis method developed achieved the aim of the thesis: to develop a new embodied energy analysis method that allows reliable process analysis data to be integrated into the comprehensive, yet unreliable, input-output framework. Plain language summary Embodied energy analysis comprises the assessment of the direct and indirect energy requirements associated with a process. For example, the construction of a building requires the manufacture of steel structural members, and thus indirectly requires the energy used directly and indirectly in their manufacture. Embodied energy is an important measure of ecological sustainability because energy is used in virtually every human activity and many of these activities are interrelated. This thesis is concerned with the relationship between the completeness of embodied energy analysis methods and their reliability. However, previous industry-based methods, while reliable, are incomplete. Previous national statistical methods, while comprehensive, are a ‘black box’ subject to errors. A new method is derived, involving the decomposition of the comprehensive national statistical model into components that can be modified discretely using the more reliable industry data, and is demonstrated for an individual building. The demonstration failed to integrate enough industry data into the national statistical model, due to the unexpected complexity of the national statistical data and the lack of available industry data regarding energy and non-energy product requirements. These unique findings highlight the flaws in previous methods. Reliable process analysis and input-output data are required, particularly for those processes that were unable to be examined in the demonstration of the new embodied energy analysis method. This includes the energy requirements of services sectors, such as banking, and processes involving the transformation of basic materials into complex products, such as refrigerators. The application of the new method to less complex products, such as individual building materials or components, is likely to be more successful than to the residential building demonstration.

An association rule analysis framework for complex physiological and genetic data

Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set u i (e.g. u 1 numerical data streams, u 2 images, u 3 videos, u 4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases. © 2012 Springer-Verlag.

Multi-level semantic analysis for sports video

There has been a huge increase in the utilization of video as one of the most preferred type of media due to its content richness for many significant applications including sports. To sustain an ongoing rapid growth of sports video, there is an emerging demand for a sophisticated content-based indexing system. Users recall video contents in a high-level abstraction while video is generally stored as an arbitrary sequence of audio-visual tracks. To bridge this gap, this paper will demonstrate the use of domain knowledge and characteristics to design the extraction of high-level concepts directly from audio-visual features. In particular, we propose a multi-level semantic analysis framework to optimize the sharing of domain characteristics.

The welfare economics of measuring sustainability: a new approach based on social choice theory and systems analysis

This paper presents a new measure of sustainability within a welfare economics framework. Gross domestic product (GDP) can be used as an indicator of sustainability if the GDP estimates are undertaken within a cost-benefit analysis framework based on social choice perspectives. Sustainability is dependent on a healthy and functioning socio-economic and environmental (SEE) system. Economic development can damage the SEE system through resource degradation, over-harvesting and pollution. This paper addresses the tensions between economic development and sustainability by undertaking a number of SEE-based adjustments to GDP based on social choice perspectives in order to measure sustainability. These adjustments include the environmental and social costs caused by economic development such as water pollution, the depletion of non-renewable resources, and deforestation. Thailand is used as a case study for a 25 year period (1975-1999). The results show a divergence in terms of GDP per capita and the SEE-adjusted GDP per capita figure. The paper concludes that, with increasing environmental and social costs of economic development, pursuing such extreme high growth objectives without due environmental and social considerations can threaten present social welfare and future sustainability. Copyright © 2005 John Wiley & Sons, Ltd and ERP Environment.

Beyond the usual suspects: complexity of fragility and analytical framework

Development in so-called ‘fragile states’ has become a key priority for the international community over the past few years, but international actors have not yet adequately incorporated sufficiently nuanced understandings of fragility into policies or practices. The increasing proportion of the world’s poor living in fragile contexts, the depth of human need in these contexts, and the potential regional spillover implications of this fragility, all make this an urgent concern. This chapter examines this growing need and discusses the origins and methodological approach in this volume, before setting up the rest of the book with definitions and an analysis framework. The chapter concludes with a summary of the book chapters and contributions.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

The relationship between economic development and social welfare: A new adjusted GDP measure of welfare

Existing literature focuses on the issue of preparation of social welfare measurements on the basis of an unadjusted Gross Domestic Product (GDP). This paper extends this method to incorporate cost-benefit analysis of economic growth in a growing economy in calculating the adjusted GDP, termed as the cost-benefit (CB)-adjusted GDP. This approach is empirically applied to Thailand. There are stark differences between GDP per capita and CB adjusted GDP per capita rates for this period.This paper concludes that GDP can be used as an indicator of social welfare if the GDP estimates are undertaken within a cost-benefit analysis framework.

Estimating intergenerational utility distribution preferences

Resource management decisions influence not only the output of the economy but also the distribution of utility between groups within the community. The theory of Benefit Cost Analysis provides a means of incorporating this distributional change through the application of distributional or welfare weights. This paper reports the results of research designed to estimate distributional weights suitable for inclusion in a Benefit Cost Analysis framework. The findings of a choice modelling experiment estimating community preferences with respect to intergenerational utility distribution are presented to illustrate this innovative application of a stated preference technique.

Estimating intergenerational distribution preferences

Resource management decisions influence not only the output of the economy but also the distribution of utility between groups within the community. The theory of cost benefit analysis provides a means of incorporating distributional changes into the decision making calculus through the application of distributional or welfare weights. However, this practice has not been widely adopted in part due to difficulties in the estimation of distributional weights. This paper addresses this problem by using the stated preference method of choice modelling to estimate distributional weights suitable for inclusion in a cost benefit analysis framework. The findings of a choice modelling experiment designed to estimate community preferences with respect to intergenerational utility distribution illustrate the potential of this method in addressing distributional issues.

A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources

Joint analysis of multiple data sources is becoming increasingly popular in transfer learning, multi-task learning and cross-domain data mining. One promising approach to model the data jointly is through learning the shared and individual factor subspaces. However, performance of this approach depends on the subspace dimensionalities and the level of sharing needs to be specified a priori. To this end, we propose a nonparametric joint factor analysis framework for modeling multiple related data sources. Our model utilizes the hierarchical beta process as a nonparametric prior to automatically infer the number of shared and individual factors. For posterior inference, we provide a Gibbs sampling scheme using auxiliary variables. The effectiveness of the proposed framework is validated through its application on two real world problems - transfer learning in text and image retrieval.

Conceptualising how SMEs incorporate green content in their websites

This paper presents a framework on how Small and Medium Enterprises (SMEs) can proactively incorporate content relating to their ecological responsibility (or green) activities in their websites. SME studies offer limited guidance on, and conceptualisation of, how organisations can incorporate different types of content into their website designs. This paper addresses this problem by presenting the results of an exploratory, qualitative content analysis of Australian SME websites where emergent themes are interpreted using framing and legitimacy theories. It describes three dimensions (location, presentation, and specificity) which comprise the framework, under which the themes are grouped. The paper outlines how scholars can use the framework to develop models and carry out evaluations regarding how SMEs embed green content, and potentially other specific content types, in their websites. It also summarises how the framework can assist SMEs (or website developers serving them) make informed decisions regarding framing their websites as green, or de-emphasising this content, by paying attention to its location (e.g. homepage, navigation bars) and presentation (e.g. how paragraphs, images, etc are used) within webpages. The legitimacy or credibility of the green content can be enhanced using different types of specificity (e.g. statistics, detail of processes and actions, and third-party substantiation).

Measurement invariance across chronic conditions: a systematic review and an empirical investigation of the Health Education Impact Questionnaire (heiQ™).

To examine whether lack of measurement invariance (MI) influences mean comparisons among different disease groups, this paper provides (1) a systematic review of MI in generic constructs across chronic conditions and (2) an empirical analysis of MI in the Health Education Impact Questionnaire (heiQ™).

Implementing novel models of posttreatment care for cancer survivors: Enablers, challenges and recommendations

AIM: The American Society of Clinical Oncology and US Institute of Medicine emphasize the need to trial novel models of posttreatment care, and disseminate findings. In 2011, the Victorian State Government (Australia) established the Victorian Cancer Survivorship Program (VCSP), funding six 2-year demonstration projects, targeting end of initial cancer treatment. Projects considered various models, enrolling people of differing cancer types, age and residential areas. We sought to determine common enablers of success, as well as challenges/barriers. METHODS: Throughout the duration of the projects, a formal "community of practice" met regularly to share experiences. Projects provided regular formal progress reports. An analysis framework was developed to synthesize key themes and identify critical enablers and challenges. Two external reviewers examined final project reports. Discussion with project teams clarified content. RESULTS: Survivors reported interventions to be acceptable, appropriate and effective. Strong clinical leadership was identified as a critical success factor. Workforce education was recognized as important. Partnerships with consumers, primary care and community organizations; risk stratified pathways with rapid re-access to specialist care; and early preparation for survivorship, self-management and shared care models supported positive project outcomes. Tailoring care to individual needs and predicted risks was supported. Challenges included: lack of valid assessment and prediction tools; limited evidence to support novel care models; workforce redesign; and effective engagement with community-based care and issues around survivorship terminology. CONCLUSION: The VCSP project outcomes have added to growing evidence around posttreatment care. Future projects should consider the identified enablers and challenges when designing and implementing survivorship care.

Assessment of simulated clinical skills and distance students: can we do it better?

Australian universities have traditionally been able to supplement clinical education, for undergraduate nursing courses, delivered on placement with weekly clinical teaching in the simulated environment. The Objective Structured Clinical Assessment (OSCA) tool has been used in this simulated environment to assess clinical skills. Recently, however, online delivery of undergraduate nursing courses has become more common. The move from an internal mode of teaching to an online external mode is seen worldwide and poses challenges to staff and students as well as changing the teaching and learning culture of institutions (Philip and Wozniak, 2009). This cultural shift and the resulting diminishing timeframe for students to acquire and practice simulated clinical skills imply that it may become necessary to rethink assessment forms such as the OSCA assessment. This study examines whether or not the OSCA tool developed by Bujack et al. (1991a) is the best tool to be used in this new context, where online teaching is supplemented by very short, annual, intensive periods of study. Skills acquisition theories dictate that time is required to produce an ideal skills acquisition environment (Quinn, 2000) but the time constraints placed on students in such intensive periods of study could influence skills acquisition. This cross-sectional qualitative study used semi-structured interviews and focus groups to collect data. 65% of the nursing faculty participated in the study. The teaching of the Bachelor of Nursing (BN) occurred on two campuses and staff from both areas participated. This group of nurse academics was employed across the range of academic levels (from lecturer to professor) at the University. Data analysis followed a generic thematic analysis framework. Findings in this study show that there are a variety of attitudes and underpinning beliefs amongst staff in relation to the OSCAs. Doubts were raised in regard to the suitability of the use of the OSCA tool in this setting. It also became apparent during this study that the OSCA tool possibly serves purposes other than an assessment tool.

"If you can have one glass of wine now and then, why are you denying that to a woman with no evidence": Knowledge and practices of health professionals concerning alcohol consumption during pregnancy.

BACKGROUND: Alcohol consumption during pregnancy has the potential to cause significant harm to the foetus and the current Australian guidelines state that it is safest not to drink alcohol while pregnant. However, conflicting messages often appear in the media and it is unclear if the message to avoid alcohol is being effectively conveyed to pregnant women. AIMS: This research aims to explore the advice that health professionals provide to pregnant women about alcohol consumption; the knowledge of health professionals regarding the effects of alcohol consumption; and their consistency with following the Australian guidelines. METHODS: Ten semi-structured face to face interviews were conducted with health professionals who regularly provide antenatal care. These include midwives, obstetricians, and shared care general practitioners. A six-stage thematic analysis framework was used to analyse the interview data in a systematic way to ensure rigour and transparency. The analysis involved coding data extracts, followed by identifying the major themes. FINDINGS: Health professionals displayed adequate knowledge that alcohol can cause physical and mental difficulties that are lifelong; however, knowledge of the term FASD and the broad spectrum of difficulties associated with alcohol consumption during pregnancy was limited. Although health professionals were willing to discuss alcohol with pregnant women, many did not make this a routine part of practice, and several concerning judgements were noted. CONCLUSION: Communication between health professionals and pregnant women needs to be improved to ensure that accurate information about alcohol use in pregnancy is being provided. Further, it is important to ensure that the national guidelines are being supported by health professionals.