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Intermittent electrical power output from grid-connected solar farms causes intermittent and uncertain requirements for dispatchable power to balance power supply and demand. Accurate forecasting of electrical power output from solar farms can improve managing power generators connected to the grid. To forecast the electrical power output, a time series model is developed for two solar farms in Australia. The forecast model consists of a Fourier series that models seasonality and an autoregressive moving-average component that models the difference between the observed electrical power outputs and the Fourier series. Persistence detection is added to the model to improve forecast performance on clear days. Using minutely data, the model forecasts the electrical power output seven minutes ahead at every five-minute interval to comply with the requirements of the Australian Energy Market Operator (AEMO). Based on a 30-day testing period, the normalized mean absolute error (NMAE) skills of the time series model are 10.9% and 13.2% lower than those of the clear sky index persistence (CSIP) model. However, the normalized root mean squared error (NRMSE) skills of the time series model are approximately 3% and 12% higher than those of CSIP and the model currently used by AEMO, respectively. As the NRMSE skills are more indicative than the NMAE skills in reducing large forecast errors that would reduce electricity grid stability, the results suggest that AEMO can improve the management of the electricity grid with an inexpensive tool by adopting the developed model to forecast electrical power output of solar farms. [ABSTRACT FROM AUTHOR]

Food waste is a global problem. In Australia alone, it is estimated that households throw away AU$5.2 billion worth of food (AU$616 per household) each year. Developed countries have formal waste management systems that provide measures of food waste. However, much remains unknown about informal food waste disposal routes and volumes outside of the formal system. This article provides indicative metrics of informal food waste by identifying, in detail, five of the dominant informal food waste disposal routes used by Australian households: home composting, feeding scraps to pets, sewer disposal, giving to charity, and dumping or incineration. Informal waste generation rates are then calculated from three primary data sources, in addition to data from previous Australian and UK surveys, using a weighted average method in conjunction with a Monte-Carlo simulation. We find that the average Australian household disposes of 2.6 kgs of food waste per week through informal routes (1.7 kgs via household composting, 0.2 kgs via animals, and 0.6 kgs via sewage). This represents 20% of Australian household food waste flows. Our results highlight that informal food waste is a sizable food waste flow from Australian homes, deserving of greater research and government attention. Our examination of the full extent of food waste by disposal mode provides waste managers and policy makers with clear disposal routes to target for behaviour change and positive environmental outcomes. [ABSTRACT FROM PUBLISHER]

People's views of landscape can have significant impact on their land use choices. This in turn can have climate implications. In the literature, there is significant attention paid to the effect of climate on landscape, but as well, there are more examples than one might expect of the effects of landscape change on climate. I present evidence of this from Western Australia and Africa, and some conjectured negative and positive effects in two locations in South Australia. I argue that the land use change affecting the landscape and thus affecting the climate emanates from one's view of landscape. There are also some personal reflections of perceptions of landscape from someone who started life in a climate and landscape far removed from the South Australian environment within which he has chosen to live. [ABSTRACT FROM AUTHOR]

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output. [ABSTRACT FROM AUTHOR]

The conveyance of stormwater has become a major concern for urban planners, considering its harmful effects for receiving water bodies, potentially disturbing their ecosystem. Therefore, it is important to characterize the quality of catchment outflows. This information can assist in planning for appropriate mitigation measures to reduce stormwater runoff discharge from the catchment. To achieve this aim, the article reports the field data from a typical urban catchment in Australia. The pollutant concentration from laboratory testing is then compared against national and international reported values. In addition, a stochastic catchment model was prepared using MUSIC. The study in particular reported on the techniques to model distributed curbside leaky wells with appropriate level of aggregation. The model informed regarding the efficacy of distributed curbside leaky well systems to improve the stormwater quality. The results indicated that catchment generated pollutant load, which is typical of Australian residential catchments. The use of distributed storages only marginally improves the quality of catchment outflows. It is because ability of distributed leaky wells depended on the intercepted runoff volume which is dependent on the hydrological storage volume of each device. Therefore, limited storage volume of current systems resulted in higher contributing area to storage ratio. This manifested in marginal intercepted volume, thereby only minimum reduction in pollutant transport from the catchment to outlet. Considering strong correlation between contributing impervious area and runoff pollutant generation, the study raised the concern that in lieu of following the policy of infill development, there can be potential increase in pollutant concentration in runoff outflows from Australian residential catchments. It is recommended to monitor stormwater quality from more residential catchments in their present conditions. This will assist in informed decision-making regarding adopting mitigations measures before considering developments. [ABSTRACT FROM AUTHOR]

Equity in resource allocation is central to the tenet of social justice in health care. The management of surgical waiting lists is of critical importance to clinicians, patients and regulators. In most hospital environments, the basic process has remained unchanged for decades. Patients are assigned to one of three urgency-related categories. Clinicians consequently administer three competing patient pools. The basis by which patients are selected for treatment may be difficult to define. The specific clinical circumstances of each patient are often unreported and may be unknown to those administering the list. Waiting list bias is also recognised. This may reflect clinician advocacy, pressure to meet category timeframe restrictions or perceived training requirements. In this environment, it is difficult to demonstrate propriety in care. We report the implementation of a pilot program to redesign waiting list management within a South Australian public hospital unit. This allows assemblage of patients into a single list. Overall priority is determined by balancing clinical acuity and waiting time. The determination of acuity takes into account both the primary category and the specific characteristics of each patient that are relevant to their intended procedure. Uniquely, the process is applicable to lists containing patients with dissimilar conditions. This paper reviews the limitations of current approaches in meeting reasonable community expectations. The principles and social justification underpinning this reform are introduced. Finally, the benefits offered by the program are discussed and interim results are reported. What is known about this topic? Current models for the management of hospital waiting lists have remained largely unchanged for several decades. Typically patients are allocated to urgent, semi-urgent and non-urgent categories of care. No methodology exists to systematically integrate these groups, or to account for specific patient factors. In this void, propriety in management is difficult to establish or defend. What does this paper add? A program is reported that unifies all categories of patients into a single prioritised waiting list. The order of patients is dynamic, and transparently reflects waiting time, category assignment and relevant individual patient factors. Uniquely, the program is applicable to lists containing patients with diverse clinical conditions. What are the implications for clinicians? Adoption of new technology is essential if reasonable community expectations in waiting list management are to be met. The current program provides unambiguous, defensible prioritisation of all patients awaiting care. The present reliance on individual managers is reduced, and the unique circumstances of each patient are recognised. We believe this approach affords significant benefit to patients, practitioners and regulators. [ABSTRACT FROM AUTHOR]

Australian households are throwing out more than $5 billion worth of food each year, with over 40% of household food wasted. This non-consumed food constitutes a waste of resources and energy that needlessly contribute to Australia's ecological and carbon footprints. To further complicate matters, the management, transport, and disposal of this non-consumed food is a problem that does not have a comprehensive federally legislated and governed solution. Instead, the collection and processing of municipal food waste is organised by local area councils with support and additional funding from state and federal governments. This has resulted in conflicting waste practices across Australia. This paper investigates the development of waste practices in South Australia, with the objective to understand how this particular state has developed a high level of political commitment to positive environmental action while other Australian states have taken a more conservative position. Opening with a broad discussion upon contemporary food waste policy in Australia, this paper will then delve into a historical discussion on the development of South Australia's municipal waste system in conjunction with an evaluation of the present municipal food waste disposal options that are available within South Australian homes. [ABSTRACT FROM AUTHOR]

Herbivores, particularly introduced ones such as sheep and rabbits, have reduced the levels of regeneration of a number of species of native trees and shrubs in the arid rangelands of southern Australia since European settlement. These reduced levels of regeneration mean that populations of some species are declining while others, less palatable to herbivores, are being maintained, hence gradually altering the composition and/or overall cover of vegetation. In the long term, this could result in loss of palatable species valuable to the pastoral industry for animal production, and/or the dominance of large areas by undesirable "woody weed" species. To investigate the effects of grazing, we develop a model which projects the long-term survival rate of a focus species, My- oporum platycarpurn, under grazing and nongrazing regimes. The technique we are applying is an extension of the Leslie growth model, where the vital rates are linked to random rain- fall events and deterministic grazing. The effect of grazing is incorporated into the model based upon data primarily collected from the TGB Osborn Koonarnore Vegetation Reserve, 400 km north of Adelaide in South Australia within and without the exclosure. The results suggest that only with higher than average rainfall for extended periods will the species of interest, M. platycarpurn, survive under grazing. [ABSTRACT FROM AUTHOR]

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—Termed homoscedastic. However, if the variance changes—Either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—The task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting. [ABSTRACT FROM AUTHOR]

With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires the representation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior. Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting. [ABSTRACT FROM AUTHOR]

• Direct and indirect effects of sectors on net energy consumption are analysed. • Level effect is identified as the major driver for increasing energy consumption. • Manufacturing has the largest energy consumption reduction potential. • How to decrease net energy consumption has been offered to policy-makers. This paper provides a comprehensive analysis of Australian net energy consumption between 2004–05 and 2014–15. Results from environmentally-extended input-output (EEIO) analysis show that the Transport sector has the largest direct effect on net energy consumption in industrial sectors, which decreased by about 35% for net energy consumption per million $AUD in the period. The Export sector has the largest direct net energy consumption while Households consumption results in the largest net energy consumption embodied in different categories of Final demand. The structural decomposition analysis (SDA) decomposes the change of net energy consumption into five drivers, in which net energy intensity mainly reduces Australian net energy consumption by about 8000 Petajoules, while the level effect of Final demand increases it by about 10,000 Petajoules. Analysis of forward and backward linkages highlights the Manufacturing sector as the key industrial sector with the largest energy consumption reduction potential via minor changes in its input and Final demand. This indicates that more attention should be given to the reduction of energy demand from the consumption patterns of Households consumption, the improvement of energy intensity, and the application of cleaner technologies in the Transport and Manufacturing sectors. The Australian Environmental-Economic Accounts is combined with Australian input-output tables to construct the EEIO tables for net energy consumption. The combination of economic and environmental data sets provides a depth of understanding their potential to inform environmental policy decisions. The novelty of the research is the combination of economic and energy data sets, the application of EEIO model, the implementation of the additive SDA method, and the use of forward and backward linkages for the Australian energy system. [ABSTRACT FROM AUTHOR]

In Australia, heatwaves are the deadliest natural hazard and a major driver of peak electricity demand. The disproportionately high peak demand increases electricity prices, causes occasional blackouts and exacerbates energy poverty, all of which limit one’s ability to use air conditioning. Meanwhile, increased energy efficiency of dwellings may decrease their heat stress resistance. This paper challenges whether the current Australian Nationwide Energy Rating Scheme encourages heat stress resistance. Cooling consumption, peak cooling demand and the risk of indoor overheating were assessed for a typical single-storey home in Adelaide and Sydney. Design options between 6 and 8 stars, plus two traditional, energy-inefficient homes were simulated with the AccuRate energy simulation program. This paper demonstrates that the NatHERS does not directly encourage heat stress resistance in new homes and can even deliver buildings with worse heat resistance and increased reliance on air conditioning than traditional, energy-inefficient buildings. This overreliance on air conditioning can present a public health hazard, consequently, the integration of heat stress resistance in the Nationwide House Energy Rating Scheme is recommended. Recommendations for the analysis and assessment of heat stress resistance are provided. [ABSTRACT FROM AUTHOR]

This paper uses input–output analysis to model the environmental impacts of the weekly food consumption of Australia's households sorted by income quintile in 2003. We found that weekly food consumption of the relatively better off households caused greater environmental burden than that of the less well-off household. Meat and bakery products/flour/cereals were the categories that contributed the largest share of environmental impacts in an average household's food consumption footprint: 58% water, 26% energy, 29% CO 2 , and 31% wasted materials. Per dollar spent, fruit and vegetables generated 0.0874 m 3 of water, 0.0055 GJ of energy, 0.37 kg of CO 2 , and 0.0114 kg of wasted materials. This was lower than the per dollar impacts of bakery products (0.8482, 0.0067, 0.63, 0.0204), meat (0.3471, 0.0070, 0.65, 0.0203), dairy (0.0995, 0.0114, 0.98, 0.0192), and edible fats (0.2373, 0.0080, 0.70, 0.0165). Eating out and fast food consumption also had a low per dollar impact (0.1317, 0.0046, 0.38, 0.0141). We surmise that substituting fruit and vegetables at the expense of animal products, processed foods, and fats would decrease environmental impacts. Change at low income levels should be focused upon meat, bakery and dairy consumption, while higher incomes should be focused upon the consequences of eating food outside the home. [ABSTRACT FROM AUTHOR]

Outdoor thermal comfort is influenced by people's climate expectations, perceptions and adaptation capacity. Varied individual response to comfortable or stressful thermal environments results in a deviation between actual outdoor thermal activity choices and those predicted by thermal comfort indices. This paper presents a passive activity observation (PAO) method for estimating contextual limits of outdoor thermal adaptation. The PAO method determines which thermal environment result in statistically meaningful changes may occur in outdoor activity patterns, and it estimates thresholds of outdoor thermal neutrality and limits of thermal adaptation in public space based on activity observation and microclimate field measurement. Applications of the PAO method have been demonstrated in Adelaide, Melbourne and Sydney, where outdoor activities were analysed against outdoor thermal comfort indices between 2013 and 2014. Adjusted apparent temperature (aAT), adaptive predicted mean vote (aPMV), outdoor standard effective temperature (OUT_SET), physiological equivalent temperature (PET) and universal thermal comfort index (UTCI) are calculated from the PAO data. Using the PAO method, the high threshold of outdoor thermal neutrality was observed between 24 °C for optional activities and 34 °C for necessary activities (UTCI scale). Meanwhile, the ultimate limit of thermal adaptation in uncontrolled public spaces is estimated to be between 28 °C for social activities and 48 °C for necessary activities. Normalised results indicate that city-wide high thresholds for outdoor thermal neutrality vary from 25 °C in Melbourne to 26 °C in Sydney and 30 °C in Adelaide. The PAO method is a relatively fast and localised method for measuring limits of outdoor thermal adaptation and effectively informs urban design and policy making in the context of climate change.

Waste generation is linked to consumption both in households (Final demand) and in the supply chain. Gaining understanding into the driving forces that change of waste generation in the supply chain can contribute to solving issues of waste management. The environmentally-extend input-output model is an effective tool with which to investigate the relationship between economic activities and waste generation. In this paper structural decomposition analysis (SDA) is employed to analyse the determinants of changes of waste generation in Australian economy from 2007-2008 to 2013-2014. Empirical results indicate that the major determinant for the increase of waste generation was change in Final demand's overall level of economic activity. Changes in the production mix of Final demand (mix effect) was responsible for a decrease of waste generation in Australian economy during the period. The Manufacturing sector was found to have the highest waste generation intensity. Meaning that each million $AUD output of the Manufacturing sector resulted in the most amount of waste generation. In addition, technological change has contributed the largest waste generation effect for the Construction sector in 2011-2012. These findings suggest that Final demand, technological changes and sectoral changes are identified as the drivers of Australian waste generation historically. To reduce waste generation, policy must be targeted at altering behaviour of consumption and waste generation, and increasing innovation of new ecological technologies for Australian industry., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Compiling, deploying and utilising large-scale databases that integrate environmental and economic data have traditionally been labour- and cost-intensive processes, hindered by the large amount of disparate and misaligned data that must be collected and harmonised. The Australian Industrial Ecology Virtual Laboratory (IELab) is a novel, collaborative approach to compiling large-scale environmentally extended multi-region input-output (MRIO) models. The utility of the IELab product is greatly enhanced by avoiding the need to lock in an MRIO structure at the time the MRIO system is developed. The IELab advances the idea of the "mother-daughter" construction principle, whereby a regionally and sectorally very detailed "mother" table is set up, from which "daughter" tables are derived to suit specific research questions. By introducing a third tier - the "root classification" - IELab users are able to define their own mother-MRIO configuration, at no additional cost in terms of data handling. Customised mother-MRIOs can then be built, which maximise disaggregation in aspects that are useful to a family of research questions. The second innovation in the IELab system is to provide a highly automated collaborative research platform in a cloud-computing environment, greatly expediting workflows and making these computational benefits accessible to all users. Combining these two aspects realises many benefits. The collaborative nature of the IELab development project allows significant savings in resources. Timely deployment is possible by coupling automation procedures with the comprehensive input from multiple teams. User-defined MRIO tables, coupled with high performance computing, mean that MRIO analysis will be useful and accessible for a great many more research applications than would otherwise be possible. By ensuring that a common set of analytical tools such as for hybrid life-cycle assessment is adopted, the IELab will facilitate the harmonisation of fragmented, dispersed and misaligned raw data for the benefit of all interested parties., (Copyright © 2014 Elsevier B.V. All rights reserved.)