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Transitioning electricity generation sectors to renewable energy is a necessary element of climate change mitigation but a challenging one with years of technical and social lock-in creating considerable inertia and a pace of change insufficient to meet the task. Transition management has emerged as a promising approach to proactively manage and accelerate transitions in sectors like energy and water and has the potential to do so in manner that would help secure energy justice. However, the approach has been criticised for ignoring the political dynamics of transitions, risking procedural and recognition injustice for traditional energy production regions while increasing opposition to a transition. This paper uses a qualitative case study approach to understand how a transition to renewable energy generation could be achieved in a way that secures energy justice for traditional energy production regions like those of Gladstone in the Australian state of Queensland. The paper augments the transition management approach with the “just transitions” concept, which has emerged from labour movements as a means to mitigate negative impacts on workers and communities in traditional energy production regions, to create a “Just Transition Management” framework that is applied as a diagnostic tool to the case study. While results suggest a transition is imminent within Queensland, a lack of: consistent, supportive federal policy; long-term visions, and a clear process leaves it at risk of being captured by the powerful incumbent resources sector utilising an “environment vs. jobs” narrative. Yet, the results also suggest that adopting a just transition management approach has the potential to successfully resist the influence of the resources sector, by providing cheap, secure renewable energy and supporting affected workers into roles in the renewable energy sector using a reflexive, representative network governance approach. Data analysis reveals how the application of just transition principles has already engaged communities and unions that were previously sceptical of renewable energy. Overall, this study suggests that the just transition management framework assists in identifying the political barriers to transitions and energy justice more broadly, while also providing a management approach which creates powerful niche actor-networks to counter the narratives and influence of the incumbent resource sector. Thus it could support successful transitions that achieve the distributional, recognition and procedural justice needed for energy justice globally. [ABSTRACT FROM AUTHOR]

• 72% of participants perceived their crash risk was less than the average same-age, same-sex driver. • 74.2% of participants reported their driving ability was superior to the average same-age, same-sex driver. • 16% perceived their driving ability to be better than a professional race car driver. • Comparative optimism in driving ability was the most salient predictor of speeding. • Negative experiences did not deter speeding behaviours. Introduction: Preliminary research has indicated that numerous drivers perceive their risk of traffic crash to be less than other drivers, while perceiving their driving ability to be better. This phenomenon is referred to as 'comparative optimism' (CO) and may prove to inhibit the safe adoption of driving behaviors and/or dilute perceptions of negative outcomes. The objective of this study was to investigate comparative judgments regarding crash risk and driving ability, and how these judgments relate to self-reported speeding. Method: There were 760 Queensland motorists comprised of 51.6% males and 48.2% females, aged 16–85 (M = 39.60). Participants completed either a paper or online version of a survey. Judgments of crash risk and driving ability were compared to two referents: the average same-age, same-sex driver, and the average same-age, same-sex V8 supercar champion. Results: Drivers displayed greater optimism when comparing their crash risk and driving ability to the average same-age, same- sex driver (respectively, 72%, 72.4%), than when comparing to a V8 supercar champion (respectively, 60%, 32.9%). When comparing judgements of crash risk and driving ability to a similar driver, it appears that participants in the present study are just about as optimistic about their risk of crash (i.e. 72%) as they are optimistic about their driving ability (i.e. 74.2%). [ABSTRACT FROM AUTHOR]

• A sizeable proportion (25%) acknowledged drink driving. • Motorists were regularly exposed to RBT operations. • Motorists observed RBT operations more often than were tested. • Motorists calculated that they needed to be tested more often. • Higher exposure to RBT operations reduced the risk of re-offending. Introduction: Random Breath Testing (RBT) remains a primary method to both deter and apprehend drink drivers, yet a large proportion of road fatalities continue to be caused by the offense. Outstanding questions remain regarding how much exposure to RBT operations is needed to influence deterrence-based perceptions and subsequent offending. Method: Given this, licensed motorists (N = 961) in Queensland were recruited to complete a questionnaire either in the community (N = 741) or on the side of the road after just being breath tested (N = 243). Survey items measured different types of exposure to RBT operations (e.g., "seen" vs. "being tested") and subsequent perceptions of apprehension as well as self-reported drink driving behaviors. Results: The key findings that emerged were: motorists were regularly exposed to RBT operations (both viewing and being tested), such exposure was not significantly correlated with perceptions of apprehension certainty, and a sizable proportion reported engaging in drink driving behaviors (e.g., approx. 25%), although roadside participants naturally reported a lower percentage of offending behaviors. Importantly, it was revealed that current "observations" of RBT was sufficient, but not actual levels of active testing (which needed to be doubled). Nevertheless, higher levels of exposure to RBT operations was found to be predictive of a lack of intention to drink and drive again in the future. Conclusions: This paper suggests that mere exposure to enforcement may not create the intended rule compliance, and that the frequency of exposure is also essential for the roadside. [ABSTRACT FROM AUTHOR]

Adaptation is now firmly embedded in the societal discourse regarding the management of climate risk. In this discourse, adaptation planning and implementation at the local level are seen as particularly important for developing robust responses to climate change. However, it is not clear whether the mantra that adaptation is local holds true given the multi-level nature of climate risk governance. Using a multi-method approach, this paper examines the extent to which adaptation should be framed as a local issue and, specifically, the role of local government in adaptation relative to other actors. In so doing, the paper first explores the extent to which the local framing of adaptation is embedded in the international adaptation literature. This is followed by a specific case study from Southeast Queensland, Australia, which focuses on the critical examination of the processes of responsibility shifting and taking among actors involved in coastal adaptation planning. Results indicate the assumption that adaptation is local remains widely held in adaptation science, although counter arguments can be readily identified. Interviews with adaptation actors revealed unclear divisions of responsibility for climate change adaptation as a significant constraint on actors’ willingness to implement adaptation. Furthermore, attributing responsibility for adaptation to local actors might not necessarily be a robust strategy, due to the existence of particularly strong constraints and value conflicts at local levels of governance. Greater appreciation by researchers and practitioners for the interactions between local actors and those at higher levels of governance in shaping response capacity may contribute to more equitable and effective allocations of responsibilities for adaptation action. [ABSTRACT FROM AUTHOR]

• We present a multi-disciplinary collaborative approach for developing a regional scale climate adaptation plan with the health and wellbeing sector. • This includes an innovative engagement and policy mapping strategy. • We demonstrate the application of this approach for developing climate change adaptation plan for health and wellbeing sector in Queensland, Australia. • We find that direct engagement between various key stakeholders is a critical element of translating scientific evidence of climate change impacts on human health into a regional adaptation policy for the health and wellbeing sector. The science of climate change and its impacts on health makes it clear that human health and wellbeing will be increasingly negatively impacted as a result of climate change. The health and wellbeing sector must respond to these growing pressures in order to continue to provide safe, quality care. Adaptation and mitigation policies need to be developed at different scales, including at a regional government level. Numerous challenges exist; for example, the necessity for collaboration between multiple agencies across scales, the tailoring of policies to the health issues specific to regions, and constraints on existing regional and local resources and adaptive capacities, to name a few. This paper presents a multi-disciplinary collaborative approach used to develop a regional scale climate adaptation plan with the health sector. Starting from a scientific understanding of climate change impact on the health sector in Queensland, Australia, the approach used an innovative engagement strategy to a) better understand awareness of relevant stakeholders about current and future climate change impact on the health of the population and on service provision, b) identify on-ground barriers to effective adaptation faced by the sector stakeholders, c) identify opportunities and benefits which would arise from adaptation, and d) identify what conditions or support stakeholders required to overcome those barriers, take advantage of opportunities, and achieve benefits from adaptation. Analysis of these findings guided the development of specific policy directions for the sector. We found direct engagement between various key stakeholders such as health service providers (e.g. hospitals), critical infrastructure providers, academics, local government authorities, and sub-sectors such as aged care and early childhood care facilities, was a critical element of translating scientific evidence of climate change impacts on human health into a regional adaptation policy for the health and wellbeing sector. The resulting policy, grounded in the reality and experience of health and wellbeing sector stakeholders, reflects their insights and concerns, and served to develop a level of sectoral 'ownership' (not 'top-down' imposition) which will be important for its successful ongoing development and implementation. [ABSTRACT FROM AUTHOR]

• A PV variability tolerant multifunctional control strategy for BESS is proposed. • Four control functions of BESS with possible user-defined extensions were formulated. • A use-case to control PV plant's power ramp rate is demonstrated using real data. • Validation is performed in real-time software-in-loop platform using real-field data. • Evaluation of proposed strategy shows the potential for real-world deployment. This paper proposes a multifunctional control strategy for battery energy storage systems (BESSs) in solar photovoltaic (PV) plants to avoid the unacceptable PV-power ramp-rate caused by PV variability. In addition to the PV variability tolerant ramp-rate control, the proposed multifunctional BESS control strategy is capable of maintaining a user-specified charging profile to store excess PV power while providing backup power support and charge maintenance, by accommodating diverse control functions time to time. Four control functions, namely, Rest (RST), Surplus Energy Storage (STR), Back-up Power Support (SPT) and Charge Maintenance (CMT) functions, are proposed in this article. Besides, extendibility of the proposed multifunctional strategy is presented by adding two action strategies – one with the STR function and another with the SPT function. Validation of the proposed control strategy is conducted by using real historical data from a utility-scale solar PV plant located in Queensland, Australia. Numerical analyses show that the strategy can successfully keep the PV ramp-rate within specified limit along with the user specified charge profile. Further, exploiting the real-field data, the proposed strategy is demonstrated through a real-time co-simulation study conducted in a MATLAB interfaced RSCAD-RTDS platform that can be used as Digital Twin (DT) for the PV plant. [ABSTRACT FROM AUTHOR]

This paper presents the functionality of the newly designed hybrid evolutionary algorithm (HEA) applied for synthesizing predictive rules from complex ecological data by providing the options for: (a) modelling single or multiple rules and (b) optimizing model parameters by Hill Climbing (HC) or Differential Evolution (DE). The effectiveness of the improved HEA is tested by predictive modelling of chlorophyll-a and the tropical cyanobacteria Cylindrospermopsis monitored in the Wivenhoe Reservoir in Queensland (Australia) from 1998 to 2009. The paper validates results of the alternative optimization algorithms and model structures, and provides insights into ecological relationships captured by the models by means of sensitivity analyses. [Copyright &y& Elsevier]

• Presents a typology of emergent risk that highlights the iterative and inter-related nature of climate change risk, and propensity for impacts, such as legal action and reduced debt access, to emerge subsequent to, yet far removed from, initial physical impacts. • Highlights current practice and gaps in local government risk management concerning emergent risk. • Highlights the potential and need for local government to consider, the application of risk transfer mechanisms such as catastrophe bonds in the face of historical and conceivable climate change-perpetuated reduced access to, and increased cost of, traditional mechanisms such as insurance. • Provides insight into barriers and enablers to local government agencies gaining an appreciation of the potential application of alternative risk transfer mechanisms. Insurance represents an integral part of local government risk management strategy. As climate change progresses, increased loss and risk related to extreme weather events such as tropical cyclones, could motivate insurers to withdraw from certain markets. In some regions, such a withdrawal represents an emergent risk that, when coupled with increasing populations and other climate change impacts, could leave local governments and ratepayers particularly vulnerable. This paper investigates such a scenario and its ramifications in the context of a region particularly vulnerable to climate change, through an exploration of the degree that consideration of emergent risks, such as loss of insurance, and the potential application of insurance alternatives influence Queensland local government risk management. The study finds little appreciation amongst government officials of emergent risk implicit in extreme weather events such as cyclones, little understanding of the nuances of risk transfer mechanisms beyond familiar traditional insurance and disaster funding mechanisms, and by default, a lack of appreciation of the relationship between the two. A lack of resource and leadership with respect to emergent risk, and an absence of dialogue between insurance brokers and local government concerning climate change risk arise as the main reasons for this result. This research is significant because it challenges current local government risk management practice through an exploration of the risks inherent in the process itself. This has potential social, economic and ecological ramifications in drawing attention to aspects of possible "uninsurability" and prospects of alleviation thereto. Further research is recommended to consider the insurance industry's part in this study's findings in order to inform current industry practice and thinking and further enlighten the causes of local government disengagement in this critical area. [ABSTRACT FROM AUTHOR]

Abstract: This is the first paper to report a detailed comparison between ground based measurements of cloud enhanced ultraviolet (UV) radiation at sites located in both hemispheres. The measurements were undertaken at a Northern Hemisphere site (at the campus of the University of Girona in Spain) and a Southern Hemisphere site (campus of the University of Southern Queensland, Toowoomba, Australia). These sites were chosen as they have both simultaneous sky camera and solar radiation (including UV) measurement campaigns operating for at least 5 years over the same time period. Although hemispheric comparisons of UV radiation are not new, this paper focuses on a study of enhancements, including a comparison of the frequency of occurrence and associated cloud types, at the locations with differing climatology. It is found that although there are commonalities between the sites, such as the correlation of the frequency of enhancements with larger solar zenith angle (SZA) both at Girona and Toowoomba, there are differences too, e.g. the frequency of occurrence of the enhancements, and the type of clouds that produce most enhancements (cirrus at Girona, cumulus at Toowoomba). It is suggested that this is due to the differences in latitude coupled with a large amount of Cirriform cloud, especially corresponding to large SZA, at Girona. Possible explanations for this major difference were examined, with the most likely hypothesis relating to multiple scattering due to Cirriform cloud and the subsequent effect on UV radiation on a horizontal surface at large SZA. [Copyright &y& Elsevier]

Abstract: Problem: The efficacy of drink driving (driving under the influence, DUI) countermeasures to deter motorists from driving over the legal limit is extremely important when considering the personal and economic impact the offending behavior has on the community. This paper reports on an examination of 780 Queensland motorists'' perceptions of legal and non-legal sanctions and their deterrent impact on self-reported offending behavior. Method: The data were collected via a telephone survey of motorists recruited from a random sample of all listed telephone numbers in the state, adjusted according to district population figures. Results: The results indicated that there were a range of legal and non-legal factors that were significantly associated with self-reported DUI including: the perceived risk of apprehension and license loss (legal factors); and concerns relating to the possibility of being involved in a crash and hurting another person (non-legal factors). However, additional multivariate analyses indicated that while both legal and non-legal factors significantly predicted self-reported DUI, higher alcohol consumption levels and more favorable attitudes to the behavior also appear to increase the likelihood of DUI. Discussion: The paper will outline the direct implications of the research project such as the development and promotion of countermeasures that both effectively deter motorists and address pro-offending attitudes. Impact on industry: The findings also highlight that DUI remains a relatively common behaviour among some motoring groups and that there is a need to extend current levels of enforcement as well as adopt innovative strategies to enhance the impact of these operations on the offending behaviour. [Copyright &y& Elsevier]

Abstract: ZeroGen Pty Ltd (ZeroGen) recently announced its plan to develop the world’s first commercial-scale 450MW clean coal power plant by 2017 in Queensland, Australia. Since the Queensland Government first announced the project in early 2006, stakeholder engagement activities have been a key priority for ZeroGen. The company understands that stakeholder perceptions, particularly of the safety of carbon dioxide (CO2) capture and storage (CCS) processes, represent a key challenge to the commercialisation of clean coal technologies. As a result, ZeroGen has developed and implemented an advanced consultation framework to actively engage a wide range of stakeholders to address this challenge. This paper details the consultation framework and outlines the findings of research conducted to assess the perceptions of community leaders where the project’s CCS activities are occurring. The paper confirms that the consultation processes deployed by ZeroGen to date have been successful in overcoming a key challenge to the commercialisation of clean coal technologies involving CCS. [Copyright &y& Elsevier]

The buildings sector contributes 20% of global greenhouse gas emissions, and building energy use is anticipated to double by 2050. Building performance research has typically focused on the operational phase, with life-cycle impacts increasingly studied, but often in isolation. This paper presents a novel framework integrating building energy modelling with life-cycle analysis and life-cycle costing. This is applied to four façade glazing materials in a 15-story case study office building in Queensland, Australia – a sub-tropical and commercial example rare in literature but important for future building development. Results demonstrate that double-glazed low-emissivity material (Type 4) was the most energy and financially efficient despite higher embodied energy and carbon than alternatives. It yielded operational energy and greenhouse gas emission savings of 13% against the baseline, and 2% lifetime financial savings. Clear double-glazing (Type 3) had the poorest overall performance, attributed to its low U-value trapping heat within the building and increasing net energy and emissions. This was new evidence that clear double-glazing should be carefully considered in sub-tropical climates. The integrated approach identified a material with improved overall performance. If only embodied impacts (production, transport, disposal) were assessed, GT1 would appear the best option as it takes less energy and carbon to manufacture and transport yet has very poor operational performance. This paper extends existing knowledge on façade materials and will aid future work on sustainable, low-emission building design in other climates, economies, designs and industries. • Integrated modelling of building façade operational energy, carbon, and cost. • Double-glazed low emissivity option reduced energy 13% in operational phase. • Rare commercial study in sub-tropical climate shows glazing selection influence. • One glazing type performed poorly in winter due to overnight 'heat trapping. • Framework will aid analysis for other climates, economies, designs and industries. [ABSTRACT FROM AUTHOR]

Residential photovoltaic systems with battery energy storage could help accelerate the transition to a more efficient, secure and reliable electricity supply. Understanding and managing the risks associated with this complex, consumer-led transformation will be critical to minimize disruption and achieve effective integration. In this paper, we use a case study of Queensland Australia and causal loop modelling, underpinned by qualitative data sourced from extensive interviews with expert stakeholders, to consider how residential battery energy storage deployed at-scale could erode the dominance of the traditional centralized electricity supply model. With feedback loops highlighting the primacy of the consumer in coming years, our modelling suggests that a failure by incumbent utilities and policy makers to respond to consumer needs, or subvert access to battery technology, will ultimately reinforce consumer drivers for self-sufficiency and grid independence. Our analysis highlights that energy policy should address consumers’ non-financial motivations for grid independence while concurrently incentivizing the strategic management of home battery energy storage to achieve beneficial whole-of-system outcomes. The development of price-based signals that assign fair-value along the supply chain could help achieve both of these objectives. Our analysis highlights the importance of taking a long-term, systems-based view of residential battery deployment to avoid negative outcomes along the supply chain and to help achieve the most efficient integration. [ABSTRACT FROM AUTHOR]

This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for daily global solar radiation estimation in the solar-rich Sunshine State of Queensland, Australia. To design a robust hybrid modelling mechanism, the Interim-ERA European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis data are employed to train and cross-validate the estimation model that is formulated by an evolutionary-type algorithm: the Coral Reefs Optimization (CRO) integrated with an Extreme Learning Machine (ELM) model. The hybrid CRO-(ELM) algorithm is applied in two stages: first for the feature selection process guided by an ELM algorithm (a class of fast training neural network tool) as the model’s fitness function to screen an optimal set of predictor variables and second, for the estimation of the solar radiation using the optimally screened variables by the final hybrid CRO-(ELM)-ELM system. To benchmark the performance of the hybrid CRO-ELM algorithm for this estimation problem we apply an alternative, yet a similar feature screening approach: the Grouping Genetic Algorithm encoded into the ELM-based model (GGA-(ELM) used as the predictor mechanism). After the feature selection process is performed by the CRO algorithm that extracts the data patterns for accurate estimation the alternative objective algorithm is applied (in this case the ELM again) to formulate the hybrid CRO-(ELM)-ELM modelling system. To provide a rigorous evaluation of the CRO-(ELM)-ELM hybrid system, alternative estimation approaches are considered: the Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR) and the Support Vector Regression (SVR). The hybrid CRO-(ELM)-ELM system is tested in a real problem where the results are evaluated by means of several statistical score metrics and diagnostic plots of the observed and the estimated daily global solar radiation in the testing phase. We show that the hybrid CRO-(ELM)-ELM model is able to yield promising results; thus improving those attained by the 7 alternative models (i.e., hybrid CRO-(ELM)-MARS, CRO-(ELM)-MLR and CRO-(ELM)-SVR and the GGA equivalent models). The study ascertains that the CRO-based hybrid system where a large pool of predictor data are carefully screened through a wrapper-based modelling system and the ELM model is applied as a objective estimation tool can be adopted as a qualified stratagem in solar radiation estimation problems. The proposed hybrid CRO-(ELM)-ELM system exhibits clear advantages over the alternative machine learning approaches tested and also over the other machine learning algorithms used without the feature selection tool; thus advocating its scientific utility in renewable energy applications. [ABSTRACT FROM AUTHOR]

Water efficiency measures and alternative supply sources alleviate peak water demand on urban water supply networks. Consequently, they also provide benefits to water service providers, in terms of augmentation deferrals and reduced sized infrastructure. However, while these benefits are acknowledged in the literature, they have not been thoroughly investigated and quantified. This paper empirically demonstrates how the installation of different potable water saving measures would affect the design of urban water supply networks. Peak day water demand profiles were developed for the baseline scenario, which represented the typical building code mandated for new dwellings constructed in Queensland, Australia, and for households fitted with water saving measures. The core novel feature of this study relates to the use of an innovative bottom-up approach to the development of demand profiles based on smart meters enabling comprehensive water end use datasets (i.e. demand in shower, tap, etc.) to be obtained. Hydraulic model runs were conducted for various water savings scenarios across different planning horizons to determine the scheduling of augmentations in a water supply study area. The results of the model runs showed deferred and eliminated augmentations, as well as reductions in infrastructure sizing for the water savings scenarios compared to the baseline scenario. Financial analysis (i.e. NPV) on trunk main augmentation requirements over 50 year asset life cycles indicated that savings of between $1,574,289 (11.4%) and $7,030,796 (51%) could be achieved by incorporating water efficiency and potable source substitution measures in new infill developments in the study region. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) or particle pollution include the tiny particles of dust and fly ash particles are expelled from coal-burning power plants. Coal combustion is an extremely prevalent source of air pollution, and resulting PM has substantial impacts on human health, especially in industrial zones. This paper aims to design a novel hybrid deep learning framework based on long short-term memory (LSTM) integrated with a two-stage data filtering technique to accurately predict the air quality indices (i.e., PM 2.5 and PM 10) in a chosen study region 'Miles Airport, Queensland,' that meets the needs of the coal seam gas industry in Australia. The data used to construct the novel hybrid two-stage data filtering technique based on LSTM comprising of six meteorological parameters (i.e., wind direction, wind speed, air temperature, relative humidity, solar radiation, and rainfall) and two environmental factors (i.e., ozone and total suspended particulate). In the first stage, two robust feature selection methods, namely, extreme gradient boosting (XGBoost) and the classification and regression tree (CART) approach, were adopted to explore the most significant predictors. Then in the second stage, the best subset regression (BSR) technique is utilized to determine the best subsets input combinations i.e., C1, C2, and C3 based on several particular metrics. The three BSR-based input combinations were employed in the LSTM model to estimate the PM 2.5 and PM 10. Furthermore, to validate the main hybrid framework, two advanced machine learning (ML) methods (i.e., LightGBM and ridge kernel regression (KRR)) and two traditional ML methods (i.e., Adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLP)) were hybridized with the multi-level data filtering strategy by examining the optimal input combinations. Several statistical metrics, graphical tools, and diagnostic analyses evaluated the hybrid models. The outcomes of the PM 2.5 simulation based on 2375 data samples showed that the LSTM-C3 containing all the selected predictors yielded the most promising accuracy, followed by the LightGBM-C3 and MLP-C3 models. On the other hand, the simulation of PM 10 distribution demonstrated that the LSTM-C3 was superior to other models, followed by the KRR-C3 and LightGBM-C3 models. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays an irreplaceable role in the efficient management of electric systems. Particularly in the electricity market and industry, accurate forecasting could provide effective future plans and economic operations for operators of utilities and power systems. Hence, a more precise and stable load-forecasting model is essentially needed in the field of electricity load forecasting. To avoid the limitations of individual models, a new combined model is proposed. In this model, except for the multiple seasonal patterns used to reduce interferences from the original data, a new optimization algorithm is presented and applied to optimize the weight coefficients of the combined model based on non-positive constraint combination theory. To estimate the forecasting ability of the proposed combined model, half-hourly electricity power data from New South Wales, the State of Victoria and the State of Queensland in Australia were used in this paper as a case study. In the numerical experiments, compared with other six single models, the average mean absolute percent errors (MAPEs) of the combined model were reduced by 0.7138%, 1.0281%, 4.8394%, 0.9239%, 9.6316% and 7.3367%, respectively. [ABSTRACT FROM AUTHOR]

Advancing knowledge on how climate change impacts on water resources is considered a priority in the academic world. This paper studies the strategies adopted by urban water professionals in the tropical region of Queensland, Australia to minimise the impacts of climate change on the regional urban water supply network. In the recent past, the dry tropical urban region of Queensland has faced a climate-induced water crisis (2014–2018). This article investigates the urban water distribution authorities' planning and operational practices as part of climate change adaptation in the study region. Water professionals from the local urban water authorities were interviewed using a comprehensive range of questionnaires. Although the impacts of climate change on different water supply sources have been investigated in many parts of Queensland, the adaptation measures taken by the local authorities appear to be random and unplanned. Holistic implementation of climate change adaptation is rare and at its infancy stage due to the lack of quantifiable implementation frameworks or an audit process. The result shows resource constraints and lack of government support are the two major barriers to climate change adaptation in the water sector. Some water practitioners believe that the local urban water authorities have done a reasonable job on climate change adaptations in bits and pieces considering a wide range of local challenges such as lack of support from stakeholders, limited resources, and technology. Some other participants believe that too much attention on climate change will cause a noticeable problem in economic activities unless concerns from all stakeholders receive equal attention and are addressed. This is an important point and deserves proper acknowledgement to overcome relevant political and community barriers to climate change adaptation. The study concludes with a critical discussion on various strands of climate change adaptation to address the long-term regional urban water supply security challenges. [ABSTRACT FROM AUTHOR]

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to ≈ 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR , including the Willmott's, Nash-Sutcliffe's coefficient and Legates & McCabe's Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications. [Display omitted] • This paper presents a hybrid CSVR algorithm for global solar radiation prediction. • The proposed approach combines Convolutional Neural Network with a Support Vector Machine. • An Atom Search-based feature selection is applied to improve the performance of the system. • Experiments are carried out in data at six solar energy farms in Queensland, Australia. • The results show the efficacy of deep learning CSVR for solar energy prediction problems. [ABSTRACT FROM AUTHOR]

Intelligence technology develops quickly to predict and respond to the actions of electric power users to maintain a reliable and secure electricity infrastructure. This paper proposed a new on-line training network called distributed hyper-spherical ARTMAP (dHS-ARTMAP) to forecast the electricity load. The new model constructs a more compact network structure and largely decreases the proliferation problem that Fuzzy ARTMAP models usually encounter. Experiments of short-term electricity load forecasting are made with the data from Queensland, Australia. Results are compared with other methods. The effectiveness of the dHS-ARTMAP network proves itself a promising alternative to put into practical use. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents impacts of wind energy integration to the Queensland network. [•] Existing and prospective wind farms has been taken into consideration. [•] Load growth and generation displacement has been carried out to accommodate wind power. [•] Aggregated DFIG model has been considered to model wind farms. [•] Interarea mode has been investigated. [Copyright &y& Elsevier]

Abstract: This paper describes sequential assimilation of data into a three-dimensional coastal ocean model using fast and cheap statistical surrogates of the model (emulators). The model simulates resuspension and deposition of fine sediments in a macro-tidal environment of the Fitzroy Estuary and Keppel Bay, North-East Australian coast. The assimilation algorithm was applied first to synthetic observations produced by a twin model run, and then with real data obtained from satellite observation. The latter are derived from remote sensing algorithms customised to the study region. The main objective of simulations was to test the data assimilation scheme using synthetic observations and identify potential issues and challenges when assimilating real data sets. The assimilation algorithm proved capable of substantially reducing a prior uncertainty of the model for both the scenario with the synthetic observations and the scenario with the satellite data. Significant remaining error in western Keppel Bay after assimilating satellite data is diagnostic of an underlying error in the system conceptualisation – in other words, it indicates that the primary source of error is not in the parameter values specified, but in the model structure, in the interpretation of satellite data or in the other input data. The results of our study show the utility of the developed technique for the data assimilation into the three-dimensional sediment transport model of the Fitzroy estuary and Keppel Bay. More research is required to understand the capacity of this technique to generalise to other models and regions. [Copyright &y& Elsevier]

Abstract: This paper aims to show a new solar design tool that can be used to optimize the building''s shape and solar access in existing urban areas. The presented methodology was used for the analysis of the Surfers Paradise in Queensland, where in the past the urban and buildings development changed completely the morphology of the city. Since in the medium term the Zero Energy building will be the standard for new constructions, urban development should follow the Integrated Sustainable Design approach and the principles of solar design in order to exploit solar radiation using PV modules and Solar thermal collectors in the building envelopes. The tool is based on Matlab algorithm and on RADIANCE-DAYSIM, a dynamic simulation tool, and can help urban designers and architects since the first design phase. [Copyright &y& Elsevier]

Abstract: Purpose: Information and communications technology solutions have been introduced into the residential aged care system in order to improve the effectiveness and efficiency of aged care, however to date, the actual benefits have not been systematically analysed. The aim of this study was to identify the benefits of electronic health records (EHR) in residential aged care services and to examine how the benefits have been achieved. Method: A qualitative interview study was conducted in nine residential aged care facilities (RACFs) belonging to three organisations in the Australian Capital Territory (ACT), New South Wales (NSW) and Queensland, Australia. A longitudinal investigation after the implementation of the aged care EHR systems was conducted at two data points: January 2009 to December 2009 and December 2010 to February 2011. Semi-structured interviews were conducted with 110 care staff members selected through theoretical sampling, representing all levels of care staff who worked in those facilities. Results: Three categories of benefits were perceived by the care staff members according to who gain the benefits: the benefits to individual care staff members, to residents and to the RACFs. The benefits to individual care staff members include an improvement of documentation efficiency, information and knowledge growth as well as empowering the staff; the benefits to residents are an improvement in the quality of individual residents’ health records, the higher quality of care and smoother communication between the residents and aged care staff; the RACFs gain an increased ability to manage information and acquire funding, an increase in their ability to control the care quality and improvements in the working environment and educational benefits. Three factors leading to these benefits were examined: the nature of the aged care EHR systems in comparison with paper-based records; the way the systems were used by the staff and one benefit that could lead to another. Conclusions: In this study, EHR systems were perceived to have substantial benefits for care staff, residents and the aged care organisations introducing the systems. The benefits were derived from the nature of the aged care EHR systems, staff members’ continuous use of the systems, and one benefit led to the other. [Copyright &y& Elsevier]

Abstract: Scale formation is a serious problem in the mineral processing industry. To better understand the options available for mitigating this problem, a novel scale–velocity model is proposed in this paper for slurry systems commonly found in mineral processing plants. The new qualitative scale growth model predicts that at very low fluid velocities the scale growth rate is enhanced by an increase in fluid velocity due to the mass transfer-controlled scale growth. At higher fluid velocities, the scale growth rate decreases with increasing fluid velocity due to the increased flow erosion effect, for slurry systems. This suggests the potential of particulate erosion as a scale suppression mechanism. The model predicts the existence of an optimal slurry flow velocity, where scaling rate and equipment erosion rate are both zero. The optimal fluid velocity value is proposed to be used as the main parameter to improve engineering design of mineral processes in terms of scale suppression. A novel agitator design, swirl flow technology (SFT), developed and patented by CSIRO and Queensland Alumina Ltd (QAL) in Australia was introduced as an agitator design that better meets requirements for scale suppression than a widely used conventional draft-tube agitator system. SFT agitation has been installed in gibbsite precipitators at QAL''s alumina plant for a decade. As shown by CFD simulations and laboratory measurements, swirled flow agitation generates more uniform velocity distribution and higher velocity values at the wall than conventional agitators for the same power input; this reduces the maximum growth rate of scaling in the tank leading to significantly prolonged precipitator''s service life. Based on the full-scale operational experience at QAL, it can be suggested that SFT agitation roughly halves the scale growth rate as compared to that measured in the conventional draft tube agitator systems. [Copyright &y& Elsevier]

Abstract: This paper proposes a practical job grouping approach, which aims to enhance the time related performance metrics of container transfers in the Patrick AutoStrad container terminal, located in Brisbane, Australia. It first formulates a mathematical model of the automated container transfers in a relatively complex environment. Apart from the consideration on collision avoidance of a fleet of large vehicles in a confined area, it also deals with many other difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. The proposed job grouping approach aims to improve the makespan of the schedule for yard jobs, while reducing straddle carrier waiting time by grouping jobs using a guiding function. The performance of the current sequential job allocation method and the proposed job grouping approach are evaluated and compared statistically using a pooled t-test for 30 randomly generated yard configurations. The experimental results show that the job grouping approach can effectively improve the schedule makespan and reduce the total straddle carrier waiting time. [Copyright &y& Elsevier]

Abstract: This paper presents an integrated modeling method for multi-criteria land-use suitability assessment (LSA) using classification rule discovery (CRD) by ant colony optimisation (ACO) in ArcGIS. This new attempt applies artificial intelligent algorithms to intelligentise LSA by discovering suitability classification rules. The methodology is implemented as a tool called ACO–LSA. The tool can generate rules which are straightforward and comprehensible for users with high classification accuracy and simple rule list in solving CRD problems. A case study in the Macintyre Brook Catchment of southern Queensland in Australia is proposed to demonstrate the feasibility of this new modeling technique. The results have addressed the major advantages of this novel approach. [Copyright &y& Elsevier]

Abstract: This paper describes the methodology used to derive the economics of CO2 storage in coal with enhanced coalbed methane recovery. A significant difference between CO2 storage in coal seams and storage in saline aquifers is that the incrementally recovered natural gas constitutes an additional revenue stream. In the case of CO2 storage in coal it is necessary to distinguish between CBM (primary coalbed methane recovery), CO2-ECBM (enhanced coalbed methane recovery caused by and including the injection of CO2), and CO2 storage with incremental CBM recovery (Storage-ICBM). The methodology is demonstrated by means of a case study that evaluates the economics of carbon capture and storage with incremental CBM recovery (CCS-ICBM) at the Spring Gully CBM prospect in the Bowen Basin, QLD, Australia–Australia’s most productive CBM basin. Economic analyses of the Spring Gully development show that the specific cost of CO2-ECBM is A$37/t CO2 avoided (equivalent to US$31/t at an assumed long term exchange rate of A$1.00 = US$0.85) including capture costs. However, this incorporates the benefits of the underlying CBM project and therefore underestimates the cost of CCS-ICBM. The incremental cost of CCS-ICBM is A$92/t (US$78/t) excluding these benefits. The results demonstrate the importance of using an appropriate methodology to determine and present storage economics. If no distinction between CO2-ECBM and Storage-ICBM is made, the real costs of storage will for most cases be significantly underestimated and could potentially lead to poor investment decisions and the selection of economically unviable storage sites. [Copyright &y& Elsevier]

Abstract: This paper examines the fate of perfluorinated sulfonates (PFSAs) and carboxylic acids (PFCAs) in two water reclamation plants in Australia. Both facilities take treated water directly from WWTPs and treat it further to produce high quality recycled water. The first plant utilizes adsorption and filtration methods alongside ozonation, whilst the second uses membrane processes and advanced oxidation to produce purified recycled water. At both facilities perfluorooctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS), perfluorohexanoic acid (PFHxA) and perfluorooctanoic acid (PFOA) were the most frequently detected PFCs. Concentrations of PFOS and PFOA in influent (WWTP effluent) ranged up to 3.7 and 16ngL−1 respectively, and were reduced to 0.7 and 12ngL−1 in the finished water of the ozonation plant. Throughout this facility, concentrations of most of the detected perfluoroalkyl compounds (PFCs) remained relatively unchanged with each successive treatment step. PFOS was an exception to this, with some removal following coagulation and dissolved air flotation/sand filtration (DAFF). At the second plant, influent concentrations of PFOS and PFOA ranged up to 39 and 29ngL−1. All PFCs present were removed from the finished water by reverse osmosis (RO) to concentrations below detection and reporting limits (0.4–1.5ngL−1). At both plants the observed concentrations were in the low parts per trillion range, well below provisional health based drinking water guidelines suggested for PFOS and PFOA. [ABSTRACT FROM AUTHOR]

This paper reports the distribution of Polycyclic Aromatic Hydrocarbons (PAHs) in wash-off in urban stormwater in Gold Coast, Australia. Runoff samples collected from residential, industrial and commercial sites were separated into a dissolved fraction (<0.45 μm), and three particulate fractions (0.45–75 μm, 75–150 μm and >150 μm). Patterns in the distribution of PAHs in the fractions were investigated using Principal Component Analysis. Regardless of the land use and particle size fraction characteristics, the presence of organic carbon plays a dominant role in the distribution of PAHs. The PAHs concentrations were also found to decrease with rainfall duration. Generally, the 1- and 2-year average recurrence interval rainfall events were associated with the majority of the PAHs and the wash-off was a source limiting process. In the context of stormwater quality mitigation, targeting the initial part of the rainfall event is the most effective treatment strategy. The implications of the study results for urban stormwater quality management are also discussed. [Copyright &y& Elsevier]

Abstract: Discrete underground drainage conduits in quartz sandstones are far less common than in limestones. This paper provides field evidence from the quartzose Precipice Sandstone in the Carnarvon Range of south-central Queensland, Australia, for tubular underground drainage networks similar in many ways to limestone conduits. Diameters range from less than 1 or 2 cm to over 1.5 m, most display a near-circular to oval cross-section that seems to suggest phreatic or epiphreatic development, and the internal surfaces of many are case-hardened by secondary silica deposits. A number of the region''s perennial springs appear to be fed by such tubes. The dominant vertical jointing of the quartz sandstone and relatively high permeability of the sandstone are important controls on tube formation. Solutional weathering of the sandstone is widespread, and is followed by the removal of loosened sand grains by flowing underground water, the process of ‘arenisation’. Tube development would appear to have been happening for a very long time, and may still be occurring. A model for tube network formation is proposed. These findings highlight our potentially poor understanding of groundwater flow within some quartz sandstones, and may have important groundwater management implications. [Copyright &y& Elsevier]

Abstract: This paper details a methodology for the design of a model predictive controller for a continuous granulation plant. The work is based on a non-linear one-dimensional population balance model (1D-PBM), which was parameterized using experimental step test data generated at a continuous granulation pilot plant installed at the University of Queensland, Australia. The main objective was to operate the granulator under optimal conditions while off-specification material was fed back into the granulator to increase the economy of the process. The final algorithm design combines elements of model predictive control (MPC) with gain scheduling to cancel non-linearities in the recycle flow. A model directly identified from the step test data was the basis for testing a model predictive controller. Simulations show that the efficiency and robustness of this granulation process can be improved by applying the proposed control strategy. Ongoing work focuses on the implementation of the proposed control strategy on a full scale industrial plant. [Copyright &y& Elsevier]

Abstract: This paper describes the development and validation of the Australian Land Erodibility Model (AUSLEM), designed to predict land susceptibility to wind erosion in western Queensland, Australia. The model operates at a 5×5km spatial resolution on a daily time-step with inputs of grass and tree cover, soil moisture, soil texture and surficial stone cover. The system was implemented to predict land erodibility, i.e. susceptibility to wind erosion, for the period 1980–1990. Model performance was evaluated using cross-correlation analyses to compare trajectories of mean annual land erodibility at selected locations with trends in wind speed and observational records of dust events and a Dust Storm Index (DSI). The validation was conducted at four spatial length scales from 25 to 150km using windows to represent potential dust source areas centered on and positioned around eight meteorological stations within the study area. The predicted land erodibility had strong correlations with dust-event frequencies at half of the stations. Poor correlations at the other stations were linked to the inability of the model to account for temporal changes in soil erodibility, and comparing trends in the land erodibility of regions with dust events whose source areas lie outside the regions of interest. The model agreement with dust-event frequency trends was found to vary across spatial scales and was highly dependent on land type characteristics around the stations and on the types of dust events used for validation. [Copyright &y& Elsevier]

Current ambient air quality standards are mass-based and restricted to PM2.5 and PM10 fractions. The major contribution to both PM2.5 and PM10 fractions is from particles belonging to the coarse mode and generated by mechanical processes. These standards are thus unable to effectively control particle concentrations from combustion sources, such as motor vehicles and power plants, which tend to emit very small particles that are almost entirely respirable and in the submicron range, and dominate the nucleation and accumulation modes, which contribute much less to particle mass concentration. The aim of this work was to examine whether PM1 and PM10 would be a more effective combination of mass standards than PM2.5 (dominant in the nucleation and accumulation modes) and PM10 (dominant in the coarse mode) in controlling combustion-related ambient particles, as well as those originating from mechanical processes. First, a large body of data on particle size distributions in a range of environments in South East Queensland, Australia, was analysed, with an aim of identifying the relation between modality in the distributions and sources of particles belonging to different modes. The analyses included a matrix of the following elements: particle volume and number distributions, type of environment and locations of the modes in the range of PM1, PM2.5 and PM10 fractions. Second, with the same aim, 600 published modal location values relating to number, surface area, volume and mass size distributions for a range of environments worldwide were analysed. The analysis identified a clear and distinct separation between the location of the modes for a substantial number of environments worldwide and particle metrics, which suggests that modality in particle size distributions may be a parameter that has potential to be used in the development of PM1 air quality guidelines and standards. Based on these analyses, implications for choosing different mass standards for airborne particulate matter are discussed in the paper. [Copyright &y& Elsevier]

Abstract: This paper details the distribution of Cu, Pb and Zn in aquatic systems draining Mount Isa Ag-Cu-Pb-Zn Mine in arid northern Queensland, Australia. Sediment-metal concentrations in the <2mm grain-size fraction adjacent to and downstream of the mine significantly exceed background concentrations (Cu, 159; Pb, 36; Zn, 86ppm) as well as Australian government sediment quality low trigger guidelines (Cu, 65; Pb, 50; Zn, 200ppm). Overbank sediments are more contaminated than channel sediments with mean values of Cu, 480; Pb, 540; Zn, 750ppm. Mean concentrations in cut riverbank samples from the <2mm fraction were Cu, 195; Pb, 724; Zn, 807ppm. Corresponding <180μm samples returned concentrations of Cu, 321; Pb, 995; Zn, 1110ppm. Delivery of contaminants during wet seasons from Mount Isa Mine and historically contaminated riverbanks remains an ongoing issue. The ease of dust entrainment in arid zones means that sediments enriched in toxic concentrations of metals may be widely dispersed and ultimately ingested and absorbed by biota. [Copyright &y& Elsevier]

Abstract: Jakeman et al. (Jakeman, A.J., Letcher, R.A., Norton, J.P., 2006. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21, 602–614) outline ten steps in the pursuit of good practice in model development and application to increase the credibility and impact of results from environmental models. This paper shows how the ten steps of model development are relevant to numerical groundwater modelling, using a model of a data-rich coastal groundwater system near Bowen in Queensland, Australia as an example. The model is Geographic Information System-based and estimates the dynamical water balance using Darcy''s Law. The method, which is generally applicable to data-rich aquifers, proved cost and time effective and provided important insights to the groundwater dynamics of the area. [Copyright &y& Elsevier]

Abstract: In catchments adjacent to the Great Barrier Reef World Heritage Area in Queensland, Australia, there is a growing concern that sediments and nutrients being exported from the land are having a detrimental effect on coral reef communities. There is a need to determine the processes and rates of erosion from the major land use types, so that management intervention can be initiated to reduce sediment yields where required. This paper presents a sediment budget for Weany Creek, a 13.5 km2 grazed semi-arid sub-catchment of the Burdekin River catchment, Australia. A range of field methods was used to measure erosion from hillslopes, gullies and stream banks, as well as identify the amount of sediment being deposited and remobilised on the bed of gullies and the stream network. The data suggests that at least during drought conditions, the primary erosion source in this catchment is gully erosion. However, the largest source of sediment in the budget is actually associated with the remobilisation of in-channel sediment stores. Overall, the sediment budget is comprised of ∼81% coarse material and 19% fine sediment and an agreement between the fine sediment yield estimated in the sediment budget and the yield measured at the catchment outlet is within 10%. The total sediment yield estimated for this catchment is ∼4205 t yr−1 and is much lower than expected for a catchment of this size. This may reflect the drought conditions during the measurement period; however, there is also the possibility that the primary erosion sources have been exhausted, and the rates of sediment loss may be much lower now than they may have been in the past. Nonetheless, the results show that stored sediment, which may have been deposited in the channel many decades ago, is an important contributor to end of catchment sediment yields and warrants further investigation. [Copyright &y& Elsevier]

Abstract: In this paper, a two-dimensional control volume finite-element computational model is developed for simulating saltwater intrusion in a heterogeneous coastal alluvial aquifer system at Gooburrum located near Bundaberg in Queensland, Australia. The model consists of a coupled system of two non-linear partial differential equations. The first equation describes the flow of a variable-density fluid, and the second equation describes the transport of dissolved salt via a form of the Fokker–Planck equation. The outcomes of the work demonstrate that transport simulation techniques provide excellent tools for hydraulic investigations even when complex transition zones are involved. [Copyright &y& Elsevier]

Plutonium (Pu) interactions in the environment are highly complex. Site-specific variables play an integral role in determining the chemical and physical form of Pu, and its migration, bioavailability, and immobility. This paper aims to identify the key variables that can be used to highlight regions of radioecological sensitivity and guide remediation strategies in Australia. Plutonium is present in the Australian environment as a result of global fallout and the British nuclear testing program of 1952–1958 in central and west Australia (Maralinga and Montebello islands). We report the first systematic measurements of 239+240Pu and 238Pu activity concentrations in distal (≥1000 km from test sites) catchment outlet sediments from Queensland, Australia. The average 239+240Pu activity concentration was 0.29 mBq.g −1 (n = 73 samples) with a maximum of 4.88 mBq.g −1.238Pu/239+240Pu isotope ratios identified a large range (0.02–0.29 (RSD: 74%)) which is congruent with the heterogeneous nuclear material used for the British nuclear testing programme at Maralinga and Montebello Islands. The use of a modified PCA relying on non-linear distance correlation (dCorr) provided broader insight into the impact of environmental variables on the transport and migration of Pu in this soil system. Primary key environmental indicators of Pu presence were determined to be actinide/lanthanide/heavier transition metals, elevation, electrical conductivity (EC), CaO, SiO 2 , SO 3 , landform, geomorphology, land use, and climate explaining 81.7% of the variance of the system. Overall this highlighted that trace level Pu accumulations are associated with the coarse, refractive components of Australian soils, and are more likely regulated by the climate of the region and overall soil type. • 73 outlet catchment sediments analysed for 239+240Pu trace-level activity concentration. • Combined with a large national geochemical dataset to investigate the transport of Pu as a contaminant in the Australian soil surface. • Statistical analysis reveals linear and non-linear interrelationships between Pu activity and environmental variables. • Highlighted an association of Pu with the coarse, refractive components of the soils (heavy minerals). • Trace level Pu accumulations are more likely regulated by the climate of the region and overall soil type. [ABSTRACT FROM AUTHOR]

Domestic food waste (DFW) is an important policy issue because it represents inefficiencies in supply chains and consumption behaviour, creates pressures on urban landfills, and causes downstream environmental impacts such as greenhouse gas (GHG) emissions and odour from landfills. However, the evaluation of proposals to minimise or treat food waste, such as through separate disposal streams, is difficult because those impacts are not registered through normal market price signals. This paper presents non-market valuations for a hypothetical kerbside DFW collection service from a survey of households in two local government areas in the Central Queensland region, Australia. Choice modelling (CM) and contingent valuation methods (CVM) were employed to elicit potential participation and estimate willingness to pay (WTP) of the community for a domestic food waste collection service. Latent class analysis results for the sub-groups supporting an improvement option revealed that cost, odour and fortnightly collection frequency limit both WTP for and participation in the potential kerbside collection program. While there was support because it would extend the lifespans of the local landfills, potential reduction of methane emissions was not a strong driver. No difference in support or willingness to pay could be identified between the two local government populations. While the CVM results showed a WTP of $30.42 and a 58% participation rate for the proposed option, the latent class models showed that potential participation and willingness to pay varied across negative, indifference and positive sub-samples. This study provides a useful example of how a new policy option to improve waste management at a local area level might be evaluated, where the willingness to pay estimates can be factored into a benefit cost analysis and the estimates of likely support or opposition can be inputted into a political economy framework. • The study assessed household preferences to reduce domestic food waste. • Stated preference experiments were used to estimate WTP for reduction options. • Behavioural and attitudinal factors were important in explaining choices. • Two-stage contingent valuation models estimated both participation and WTP. • Latent class analysis models were used to classify responses to key attributes. [ABSTRACT FROM AUTHOR]

• Development of an integrated GIS tool for optimal locating of biomass energy plants. • Calculation of optimum locations, sizes and corresponding biomass delivery cost. • Evaluation of sugarcane and forest waste for bioelectricity generation. • The delivery cost of sugarcane waste alone ranged from AUD 142 to 198 per dry tonne. • By combining two biomass sources, plant sizes could increase between 3% and 31% The highly dispersed nature of biomass sources and limited tools with which to exploit geographically explicit data to minimise delivery costs are major barriers to the initiation of biomass energy projects. This paper presents a geographical information system based approach in combining suitability analysis, spatial biomass assessment and optimality analysis to locate plants for bioelectricity generation with sustainable availability of single or multiple biomass. First, fuzzy multi-criteria analysis was employed to identify the suitability of plants' locations, using local economic, environmental, and social sustainability criteria. As well, the availability of multiple types of biomass was assessed. Biomass availability information and land use data were combined to generate high-resolution spatial biomass information with user-defined resolution. Next, a location-allocation model was used to identify optimal locations for biomass energy plants by considering both road networks and spatially distributed biomass availability. Additionally, different scenarios were analysed to optimise plant sizes, biomass delivery costs and total number of plants for the region under both single biomass and multi-biomass approaches. The developed model was demonstrated for a Queensland case study using sugarcane waste alone and combined sugarcane waste and forest waste. Results using sugarcane waste in Queensland identified optimally located plants with sizes from 57 MW to 185 MW and average transportation distances of 27 km to 64 km. By combining sugarcane waste with forest waste, the plant sizes increased, depending on spatial biomass availability. This methodology can be extended to evaluate optimum configuration and optimum combination of different biomass types at a plant level. [ABSTRACT FROM AUTHOR]

Australian and Queensland Government's Reef 2050 Water Quality Improvement Plan has set targets for improving the water quality entering the Great Barrier Reef lagoon. Given the large public investment and the deficit of data linking on-farm land management to changes in environmental outcomes, there is a need for a robust and efficient methods of quantifying links between land management and water quality. This paper explores a pragmatic approach to making this link using available data. We demonstrate that a simple parameterisation process is suitable for estimating hydrology and water quality across a wide range of land uses and management practices in agricultural landscapes. However, a manually calibrated model may still require the analysis of parameters to reduce error variances and evaluate uncertainties. Confidence in estimating hydrology and water quality in descending order is: runoff, sediment, nitrogen, phosphorous, and pesticide losses, reflecting the availability of data and inherent error propagation. • A pragmatic modelling by Howleaky is suitable to link management to water quality. • A manually calibrated model may still require a reduction in error variances. • Data availability and inherent error propagation determined confidence in modelling. [ABSTRACT FROM AUTHOR]

• A mathematical programming approach for the multi-action problem is proposed (MAMP). • A new MIP model that includes fragmentation penalty in actions (MAMP-E) is presented. • The models are applied to the Mitchell River basin in Queensland, Australia. • MIP models get shorter execution times and higher quality solutions. • The MAMP-E model obtains higher ecological benefits than the MAMP model. Planning for management actions that address threats to biodiversity is important for securing its long term persistence. However, systematic conservation planning (SCP) has traditionally overlooked this aspect and just focused on identifying priority areas without any recommendation on actions needed. This paper develops a mixed integer mathematical programming (MIP) approach for the multi-action management planning problem (MAMP), where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for connectivity. An extended version of the MAMP model (MAMP-E) is also proposed that adds an expression for minimizing fragmentation between different actions. To evaluate the efficiency of the two models, they were applied to a case study corresponding to a large area of the Mitchell River in Northern Australia, where 45 species of freshwater fish are exposed to the presence of four threats. The evaluation compares our exact MIP approach with the conservation planning software Marxan and the heuristic approach developed in Cattarino et al. (2015). The results obtained show that our MIP models have three advantages over their heuristic counterparts: shorter execution times, higher solutions quality, and a solution quality guarantee. Hence, the proposed MIP methodology provides a more effective framework for addressing the multi-action conservation problem. [ABSTRACT FROM AUTHOR]

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people's daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning. • Develop a novel power load forecasting system for grid safety dispatch. • Extract important information effectively from the original load time series. • Creatively propose two hybrid optimization methods to optimize forecasting models. • Set up a comprehensive evaluation system to verify the effectiveness of the models. [ABSTRACT FROM AUTHOR]