Academic literature on the topic 'Steel Prices Australia'

In 2014, the Australian Government implemented the Emissions Reduction Fund to offer incentives for businesses to reduce greenhouse gas (GHG) emissions by following approved methods. Beef cattle businesses in northern Australia can participate by applying the ‘reducing GHG emissions by feeding nitrates to beef cattle’ methodology and the ‘beef cattle herd management’ methods. The nitrate (NO3) method requires that each baseline area must demonstrate a history of urea use. Projects earn Australian carbon credit units (ACCU) for reducing enteric methane emissions by substituting NO3 for urea at the same amount of fed nitrogen. NO3 must be fed in the form of a lick block because most operations do not have labour or equipment to manage daily supplementation. NO3 concentrations, after a 2-week adaptation period, must not exceed 50 g NO3/adult animal equivalent per day or 7 g NO3/kg dry matter intake per day to reduce the risk of NO3 toxicity. There is also a ‘beef cattle herd management’ method, approved in 2015, that covers activities that improve the herd emission intensity (emissions per unit of product sold) through change in the diet or management. The present study was conducted to compare the required ACCU or supplement prices for a 2% return on capital when feeding a low or high supplement concentration to breeding stock of either (1) urea, (2) three different forms of NO3 or (3) cottonseed meal (CSM), at N concentrations equivalent to 25 or 50 g urea/animal equivalent, to fasten steer entry to a feedlot (backgrounding), in a typical breeder herd on the coastal speargrass land types in central Queensland. Monte Carlo simulations were run using the software @risk, with probability functions used for (1) urea, NO3 and CSM prices, (2) GHG mitigation, (3) livestock prices and (4) carbon price. Increasing the weight of steers at a set turnoff month by feeding CSM was found to be the most cost-effective option, with or without including the offset income. The required ACCU prices for a 2% return on capital were an order of magnitude higher than were indicative carbon prices in 2015 for the three forms of NO3. The likely costs of participating in ERF projects would reduce the return on capital for all mitigation options.

Objective The aims of the present study were to illustrate and discuss the effects of the non-maintenance of equivalent prices when the comparators of pharmaceuticals listed on the Pharmaceutical Benefits Schedule (PBS) on a cost-minimisation basis come off-patent and are subject to statutory price reductions, as well as further potential price reductions because of the effects of price disclosure. Methods Service use, benefits paid, and price data were analysed for a selected sample of pharmaceuticals recommended for listing on a cost-minimisation basis between 2008 and 2011, and their comparators, to estimate the cost savings to the PBS of maintaining equivalent prices. Results Potential cost savings for 12 pharmaceuticals, including alternative compounds and combination products across nine therapeutic groups, ranged from A$570 000 to A$40 million to April 2015. Potential savings increased significantly following recent amendments to the price disclosure process. Conclusions Potential savings from maintaining equivalent prices for all pharmaceuticals listed on the PBS on a cost-minimisation basis could be over A$500 million per year. Actions to reduce these costs can be taken within existing policy frameworks, but legislative and political barriers may need to be addressed to minimise these costs, which are incurred by the taxpayer for no additional benefit. What is known about the topic? Pharmaceuticals listed on the PBS must provide value for money. Many pharmaceuticals achieve this by demonstrating equal effectiveness to an already listed pharmaceutical and requesting the same price as this comparator; that is, listing on a cost-minimisation basis. When the comparator moves off-patent, the price of the still-patented pharmaceutical is protected, whereas the off-patent drug is subject to price disclosure and often steep price reductions. What does this paper add? This paper adds to recent evidence on the costs to government of paying different prices for two or more pharmaceuticals that are equally effective. Between 2008 and 2011, the direct comparators for 68 pharmaceuticals listed on a cost-minimisation basis have moved onto the price disclosure list. Across 12 of these listings, the potential cost savings in the 10 months to April 2015 were A$73 million. What are the implications for practitioners? The PBS costs the Australian government over A$9 billion per year. Annual savings over A$500 million per year could be achieved by maintaining cost-minimisation across equally effective pharmaceuticals. This would improve the efficiency of the PBS at no risk to patients. Legislation is required to remove the existing F1 and F2 categorisation of listed pharmaceuticals, but the proposed changes would remove the need for therapeutic group premiums and simplify the pricing of PBS items.

A bio-economic modelling framework (GRASP-ENTERPRISE) was used to assess the implications of retaining woody regrowth for carbon sequestration on a case study beef grazing property in northern Australia. Five carbon farming scenarios, ranging from 0% to 100% of the property regrowth retained for carbon sequestration, were simulated over a 20-year period (1993–2012). Dedicating regrowth on the property for carbon sequestration reduced pasture (up to 40%) and herd productivity (up to 20%), and resulted in financial losses (up to 24% reduction in total gross margin). A net carbon income (income after grazing management expenses are removed) of $2–4 per t CO2-e was required to offset economic losses of retaining regrowth on a moderately productive (~8 ha adult equivalent–1) property where income was from the sale of weaners. A higher opportunity cost ($ t–1 CO2-e) of retaining woody regrowth is likely for feeder steer or finishing operations, with improved cattle prices, and where the substantial transaction and reporting costs are included. Although uncertainty remains around the price received for carbon farming activities, this study demonstrated that a conservatively stocked breeding operation can achieve positive production, environmental and economic outcomes, including net carbon stock. This study was based on a beef enterprise in central Queensland’s grazing lands, however, the approach and learnings are expected to be applicable across northern Australia where regrowth is present.

AbstractSeasonal climate forecasts (SCFs) provide opportunities for pastoralists to align production decisions to climatic conditions, as SCFs offer economic value by increasing certainty about future climatic states at decision-making time. Insufficient evidence about the economic value of SCFs was identified as a major factor limiting adoption of SCFs in Australia and abroad. This study examines the value of SCFs to beef production system management in northern Australia by adopting a theoretical probabilistic climate forecast system. Stocking rate decisions in October, before the onset of the wet season, were identified by industry as a key climate sensitive decision. The analysis considered SCF value across economic drivers (steer price in October) and environmental drivers (October pasture availability). A range in forecast value was found ($0–$14 per head) dependent on pasture availability, beef price, and SCF skill. Skillful forecasts of future climate conditions offered little value with medium or high pasture availability, as in these circumstances pastures were rarely overutilized. In contrast, low pasture availability provided conditions for alternative optimal stocking rates and for SCFs to be valuable. Optimal stocking rates under low pasture availability varied the most with climate state (i.e., wet or dry), indicating that producers have more to gain from a skillful SCF at these times. Although the level of pasture availability in October was the major determinant of stocking rate decisions, beef price settings were also found to be important. This analysis provides insights into the potential value of SCFs to extensive beef enterprises and can be used by pastoralists to evaluate the cost benefit of using a SCF in annual management.

Chinese truffles, close relatives of the French black truffle, arrived on world markets in the middle of the 1990s. Available in quantity at low prices, they were eagerly snapped up by traders facing a steep decline in European production. Despite having less intense flavour, they have captured a large part of world trade in truffles, and their harvest has brought significant economic benefits to rural communities in Sichuan and Yunnan provinces of southern China. In parallel, the development of artificial truffle flavouring has led to an explosion in ersatz ““truffle”” products, and the practice in many restaurants of boosting the flavour of inferior truffles with artificial truffle oils. However, recent expansion of truffle growing in North America and the arrival of southern hemisphere producers from New Zealand and Australia is extending both the quantity of truffle available and the season.

The level of bruising and its distribution on the carcass was assessed for 35 085 cattle at an abattoir in southern Queensland. Using the Australian Carcase Bruise Scoring System, the average bruise score was 8.4 points (equivalent to 1.1 kg bruise trim) per carcass, with 61%of bruising on the hindquarters. Forty-two carcasses (0.12%) were condemned because of bruising. There were significant ( P< 0.01) differences between slaughter-lots due to horn status (mixed horn > hornless group), class of animal (mixed sex and cow > steer > bull groups), breed (Zebu crossbred > British breed groups), mode of travel (road plus rail > road > road plus walk), and individual saleyard. There were no differences due to method of sale (direct to abattoir v. via saleyard), road transport operators, road distance travelled (<50 to > 500 km), or due to unloading cattle from trucks to dip for ticks. The results indicate that animal factors, such as horn status and class of animal, may be more important sources of bruising than transportation (at least by major transport operators) and method of sale. Future efforts to reduce the level of bruising depend on all meat processors paying lower prices for bruised carcasses and publicly proclaiming these differential prices.

The present study determined the impact of maternal genetics for estimated breeding values for rib fat (High-Fat, Low-Fat) or residual feed intake (RFI; High-RFI, Low-RFI) on the carcass compliance of Angus steer progeny when reared pre-weaning under High or Low-Nutrition and post-weaning under various finishing system (grazing versus short-term feedlot). The dams were joined to sires of similar genetic background (close to average estimated breeding values) and sires were rotated among all dam genotypes, with herds located at either Struan Research Centre, near Naracoorte in the south-east of South Australia, or Vasse Research Station, in the south-west of Western Australia. The breeding herd was part of the Beef CRC maternal productivity project and cows were managed under either High or Low-Nutrition, achieved by adjustments to stocking rate in rotational grazing systems and supplementary feeding, so as to maintain ~20% difference in cow liveweight. The steer progeny were weaned at ~7 months of age, with individuals from both pre-weaning nutritional treatments being treated the same from then on at each site. Steers from Struan Research Centre in South Australia born in 2008 and 2009 were sold and grown out on pasture on a local commercial property. Steer calves born in 2010 at Vasse remained on the station where they were backgrounded on hay, followed by a short period (111 days) total mixed ration containing 40% grain. In the first year, steers from Struan (n = 58) were slaughtered together at ~2 years of age, and in the second year (n = 85), consigned to six slaughter groups as their ultrasound-scanned subcutaneous P8 (rump) fat reached 7 mm and their liveweight exceeded 550 kg. Steers from Vasse (n = 101) were slaughtered at ~12 months of age, all on the same day. High-Fat-line dams produced steers with carcasses with greater P8 fat than did Low-Fat-line dams at both sites. At Struan, when the 2008-born steers were slaughtered together, more steers from Low-Fat dams failed to meet minimum fat specifications, than steers from High-Fat dams (28% vs 9% respectively). The steers born in 2009 at Struan all met processor fat specifications but steers from the Low-Fat dams took longer to reach the fat threshold, and so had greater carcass weight, but attracted more price penalties because of increased dentition. All steers from Vasse met minimum requirements for fat, with none penalised for dentition. Vasse steers from High- or Low-RFI dams performed in a manner similar to that from High- and Low-Fat dams, respectively, in that the High-RFI group produced fatter carcasses than did the Low-RFI group. Steers reared under low pre-weaning nutrition weighed less at weaning than did those on High-Nutrition, but had higher weight gains after weaning, although insufficient to result in the same carcass weight. The results showed that commercial cattle producers need to be aware of the balance and trade-off among fat breeding value, effect of pre-weaning nutrition and post-weaning growth required to ensure their cattle meet market specifications and to avoid price penalties.

The Clean Energy Act (CEA) and its related legislation received royal assent on 18 November 2011, ushering in a new era for the Australian industry, and for those who deal with it. Building on the 2007 National Greenhouse and Energy Reporting Scheme (NGERS), which mandates the measurement and reporting of greenhouse gas emissions and electricity production and consumption, the CEA imposes direct obligations on: individual industrial operations (facilities) that emit more than 25,000 tonnes of carbon dioxide, or its other equivalent greenhouse gases, from particular sources, in a year; suppliers of natural gas (at the point of last supply before the gas is burnt or otherwise used), for the emissions that will be generated when the gas is burnt; and, operators of land-fill facilities, such as local councils. While the primary emissions targeted by the scheme are produced by burning fossil fuels, they also include emissions such as the methane released when coal is mined. The obligations include the option of surrendering carbon units for each tonne of emissions, however, if this optional step is not performed, the mandatory payment of a tax, which far exceeds the cost of a unit, is enforced. The Australian Government will sell carbon units at a fixed price for the first three years, starting at $23, after which units will be auctioned for between $15 and the expected international unit price, plus $20. The supply of domestic units will be unlimited for the three fixed price years, but will be subject to a reducing cap in following years, consistent with the Government policy of reducing Australia’s emissions. The Government has created a monopoly for the supply of units for the first three years by prohibiting the use of overseas-sourced carbon units, and by only allowing 5% of the unit surrender requirements to be comprised of Australian generated carbon credits. Thereafter, for the first five of the flexible-charge years, only half the units can be sourced from overseas, with any apparent saving likely to be offset by the various taxes and charges applicable to the use of those units. Certain fuels will also be separately taxed. Entities, however, which acquire, manufacture or import fuels and would otherwise be entitled to a fuel tax credit, may be able to assume direct liability thus enabling them to acquire or manufacture fuel, free of the carbon tax component. Where the imposts will cause competitive disadvantage to industries that compete with entities from other countries that do not have similar imposts, some assistance is provided in the form of allocated units provided at no charge. Assistance is also available to coal-fired electricity generators, producers of liquefied natural gas, operators of gassy coal mines, and the steel industry (not discussed in this paper). This paper also explains, in detail, how liability is created, how to determine which entities are liable, the means of assigning liability to other entities, and the assistance available to various industries to help deal with the financial impact of the scheme on their operations. It also outlines the key concepts that underpin the scheme.

With a moderate- to low-oil-price environment being the new normal, improving process efficiency, thereby leading to hydrocarbon recovery at reduced costs, is becoming the need of the hour. The oil and gas industry generates vast amounts of data that, if properly leveraged, can generate insights that lead to recovering hydrocarbons with reduced costs, better safety records, lower costs associated with equipment downtime, and reduced environmental footprint. Data analytics and machine-learning techniques offer tremendous potential in leveraging the data. An analysis of papers in OnePetro from 2014 to 2020 illustrates the steep increase in the number of machine-learning-related papers year after year. The analysis also reveals reservoir characterization, formation evaluation, and drilling as domains that have seen the highest number of papers on the application of machine-learning techniques. Reservoir characterization in particular is a field that has seen an explosion of papers on machine learning, with the use of convolutional neural networks for fault detection, seismic imaging and inversion, and the use of classical machine-learning algorithms such as random forests for lithofacies classification. Formation evaluation is another area that has gained a lot of traction with applications such as the use of classical machine-learning techniques such as support vector regression to predict rock mechanical properties and the use of deep-learning techniques such as long short-term memory to predict synthetic logs in unconventional reservoirs. Drilling is another domain where a tremendous amount of work has been done with papers on optimizing drilling parameters using techniques such as genetic algorithms, using automated machine-learning frameworks for bit dull grade prediction, and application of natural language processing for stuck-pipe prevention and reduction of nonproductive time. As the application of machine learning toward solving various problems in the upstream oil and gas industry proliferates, explainable artificial intelligence or machine-learning interpretability becomes critical for data scientists and business decision-makers alike. Data scientists need the ability to explain machine-learning models to executives and stakeholders to verify hypotheses and build trust in the models. One of the three highlighted papers used Shapley additive explanations, which is a game-theory-based approach to explain machine-learning outputs, to provide a layer of interpretability to their machine-learning model for identification of identification of geomechanical facies along horizontal wells. A cautionary note: While there is significant promise in applying these techniques, there remain many challenges in capitalizing on the data—lack of common data models in the industry, data silos, data stored in on-premises resources, slow migration of data to the cloud, legacy databases and systems, lack of digitization of older/legacy reports, well logs, and lack of standardization in data-collection methodologies across different facilities and geomarkets, to name a few. I would like to invite readers to review the selection of papers to get an idea of various applications in the upstream oil and gas space where machine-learning methods have been leveraged. The highlighted papers cover the topics of fatigue dam-age of marine risers and well performance optimization and identification of frackable, brittle, and producible rock along horizontal wells using drilling data. Recommended additional reading at OnePetro: www.onepetro.org. SPE 201597 - Improved Robustness in Long-Term Pressure-Data Analysis Using Wavelets and Deep Learning by Dante Orta Alemán, Stanford University, et al. SPE 202379 - A Network Data Analytics Approach to Assessing Reservoir Uncertainty and Identification of Characteristic Reservoir Models by Eugene Tan, the University of Western Australia, et al. OTC 30936 - Data-Driven Performance Optimization in Section Milling by Shantanu Neema, Chevron, et al.