511 resultados para forest industry


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In December 1993, the Commonwealth Assistant Treasurer., Mr George Gear announced an Inquiry into Charitable Organisations in Australia. The inquiry would be undertaken by the Industry Commission, the structure charged by the Commonwealth to oversight its micro-economic reform agenda. The inquiry had been on the Industry Commission's forward workplan since 1992. In July 1993 a draft terms of reference was prepared for comment by the State Premiers...

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On 27 October 1994 the Industry Commission (the Commission) handed down a draft report on its inquiry into charitable organisations. The Commission had spent nearly 12 months investigating community social welfare organisations (CSWOs) including the appropriateness of the present taxation treatment of charitable organisations. The draft report makes recommendations for the taxation of CSWOs including alterations to their exemption from sales tax, fringe benefits tax and other indirect taxes with alterations to the threshold of tax deductible gifts and range of organisations qualifying for public benevolent status. This article examines the current taxation treatment for these organisations and the recommended changes made by the Industry Commission.

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The draft report of the Industry Commission's charitable organisations inquiry introduces a new term for nonprofit organisations delivering human services. The new term is "community social welfare organisation" or "CSWO". The report recommends that tax deductibility of donations be extended such organisations. It then hints at making the definition of CSWO a standard criteria for state taxation exemptions. This paper examines the definition of the new term community social welfare organisation and charts its possible consequences if adopted by the federal government. The promise of tax deductibility status to previously shunned organisations is largely illusory. The Commission's aim of simplification through clarification of the definition is flawed and will not reduce the administration costs for the Australian Tax Office (ATO) or organisations.

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Work in the Australian construction industry is fraught with risk and the potential for serious harm. The industry is consistently placed within the three most hazardous industries to work along with other industries such as mining and transport (National Occupational Health and Safety Commission, 2003). In the 2001 to 2002 period, construction work killed 39 people and injured 13,250 more. Hence, more effort is required to reduce the injury rate and maximise the value of the rehabilitation/back-to-work process.

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Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, Integrated Pest Management (IPM), to determine grain quality and to satisfy importing nation’s biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in storage sampling strategies concentrating on detection of pest insects within a grain bulk, however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in storage sampling systems.

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The prevalence of International New Ventures (INVs) has increased during the past twenty years. Nevertheless, to date there has been no general consensus within the literature on an explanation as to the rapid internationalisation of some firms. Do they follow a similar process to other firms that internationalise based on a more ‘measured’ incremental sequential process of internationalisation. This paper proposes and tests an innovation diffusion model of the internationalisation of small firm INVs and others by drawing on key innovation diffusion models from the literature. The results of this analysis indicate that the synthesised model of export adoption is effective in explaining the internationalisation process of INVs and other firms within the Queensland Food and Beverage Industry. Significantly, the features of the original innovation diffusion models developed in the consumer behaviour literature, which had limited examination within the internationalisation literature, were confirmed. This includes the ability of firms, or specifically decision-makers, to skip stages based on previous experience.

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A core component for the prevention of re-occurring incidents within the rail industry is rail safety investigations. Within the current Australasian rail industry, the nature of incident investigations varies considerably between organisations. As it stands, most of the investigations are conducted by the various State Rail Operators and Regulators, with the more major investigations in Australia being conducted or overseen by the Australian Transport Safety Bureau (ATSB). Because of the varying nature of these investigations, the current training methods for rail incident investigators also vary widely. While there are several commonly accepted training courses available to investigators in Australasia, none appear to offer the breadth of development needed for a comprehensive pathway. Furthermore, it appears that no single training course covers the entire breadth of competencies required by the industry. These courses range in duration between a few days to several years, and some were run in-house while others are run by external consultants or registered training organisations. Through consultations with rail operators and regulators in Australasia, this paper will identify capabilities required for rail incident investigation and explore the current training options available for rail incident investigators.

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Consumers are increasingly exposed to a wider range of wine brands as the industry is becoming vastly competitive. Using data from Australian wine consumers, the authors empirically test a model of antecedents of wine brand loyalty. The findings of this study show that wine knowledge and wine experience influence wine brand loyalty indirectly through wine brand trust and wine brand satisfaction. In addition, it is demonstrated that consumer satisfaction with a wine brand is the strongest driver of wine brand loyalty.

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The objective of this study was to identify key factors differentiating between exporters and non-exporters in the Chilean wine industry. Based on survey data collected from 61 wineries, the findings show that the main barriers for non-exporters are the lack of financial resources, limited quantities of stock for market expansion, management’s lack of knowledge and experience, and the high cost of travelling and participating in trade shows. The results also show that managers have educational levels and international experience exceeding those of other comparable New World wineries. Finally, in developing their main international markets, Chilean wineries did not target psychically close markets as identified in previous wine industry studies

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Each year, organizations in Australian mining industry (asset intensive industry) spend substantial amount of capital (A$86 billion in 2009-10) (Statistics, 2011) in acquiring engineering assets. Engineering assets are put to use in operations to generate value. Different functions (departments) of an organization have different expectations and requirements from each of the engineering asset e.g. return on investment, reliability, efficiency, maintainability, low cost of running the asset, low or nil environmental impact and easy of disposal, potential salvage value etc. Assets are acquired from suppliers or built by service providers and or internally. The process of acquiring assets is supported by procurement function. One of the most costly mistakes that organizations can make is acquiring the inappropriate or non-conforming assets that do not fit the purpose. The root cause of acquiring non confirming assets belongs to incorrect acquisition decision and the process of making decisions. It is very important that an asset acquisition decision is based on inputs and multi-criteria of each function within the organization which has direct or indirect impact on the acquisition, utilization, maintenance and disposal of the asset. Literature review shows that currently there is no comprehensive process framework and tool available to evaluate the inclusiveness and breadth of asset acquisition decisions that are taken in the Mining Organizations. This thesis discusses various such criteria and inputs that need to be considered and evaluated from various functions within the organization while making the asset acquisition decision. Criteria from functions such as finance, production, maintenance, logistics, procurement, asset management, environment health and safety, material management, training and development etc. need to be considered to make an effective and coherent asset acquisition decision. The thesis also discusses a tool that is developed to be used in the multi-criteria and cross functional acquisition decision making. The development of multi-criteria and cross functional inputs based decision framework and tool which utilizes that framework to formulate cross functional and integrated asset acquisition decisions are the contribution of this research.

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Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide.

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Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.