Academic literature on the topic 'Technological forecasting Australia'

Abstract Forecasting supply and demand is fundamental to the sustainability of the water system. Demand for urban water seems on an ever-upward trajectory, with use increasing twice as quickly as population throughout the 20th century. However, data from Ballarat, a city in south-eastern Australia, show that despite this conventionally held wisdom, total water usage actually peaked over 30 years ago. While the 1997–2009 ‘Millennium Drought’ had some effect, the decline commenced many years before. Initially, this was due to a reduction in external domestic water use, which correlates well with an increase in water price. However, the effect was found to not be purely economic as the price was not volumetric-based. Internal water use seems more affected by technological advances and regulatory controls. Interestingly, there was no relationship found between rainfall and water demand. The role of price, water-reduction education programmes, water-efficient technology and regulation supports previous research that a multifaceted approach is required when developing demand-reduction policies and strategies. This finding emphasises the importance of understanding the component of consumptive behaviour being targeted, and ensuring that policies being implemented are appropriate for the desired behavioural change.

The transfer of market power in electric generation from utilities to end-users spurred by the diffusion of distributed energy resources necessitates a new system of settlement in the electricity business that can better manage generation assets at the grid-edge. A new concept in facilitating distributed generation is peer-to-peer energy trading, where households exchange excess power with neighbors at a price they set themselves. However, little is known about the effects of peer-to-peer energy trading on the sociotechnical dynamics of electric power systems. Further, given the novelty of the concept, there are knowledge gaps regarding the impact of alternative electricity market structures and individual decision strategies on neighborhood exchanges and market outcomes. This study develops an empirical agent-based modeling (ABM) framework to simulate peer-to-peer electricity trades in a decentralized residential energy market. The framework is applied for a case study in Perth, Western Australia, where a blockchain-enabled energy trading platform was trialed among 18 households, which acted as prosumers or consumers. The ABM is applied for a set of alternative electricity market structures. Results assess the impact of solar generation forecasting approaches, battery energy storage, and ratio of prosumers to consumers on the dynamics of peer-to-peer energy trading systems. Designing an efficient, equitable, and sustainable future energy system hinges on the recognition of trade-offs on and across, social, technological, economic, and environmental levels. Results demonstrate that the ABM can be applied to manage emerging uncertainties by facilitating the testing and development of management strategies.

Abstract The international scientific community has long recognized the need for coordinated drought monitoring and response, but many factors have prevented progress in the development of a Global Drought Early Warning System (GDEWS): some of which involve administrative issues (coordinated international action and policy) while others involve scientific, technological, and logistical issues. The creation of the National Integrated Drought Information System (NIDIS) Portal within the United States provided an opportunity to take the first steps toward building the informational foundation for a GDEWS: that is, a Global Drought Information System (GDIS). At a series of workshops sponsored by the World Meteorological Organization (WMO) and Group on Earth Observations (GEO) held in Asheville, North Carolina, in April 2010, it was recommended that a modular approach be taken in the creation of a GDIS and that the NIDIS Portal serve as the foundation for the GDIS structure. Once a NIDIS-based Global Drought Monitor (GDM) Portal (GDMP) established an international drought clearinghouse, the various components of a GDIS (drought monitoring, forecasting, impacts, history, research, and education) and later a GDEWS (drought relief, recovery, and planning) could be constructed atop it. The NIDIS Portal is a web-based information system created to address drought services and early warning in the United States, including drought monitoring, forecasting, impacts, mitigation, research, and education. This portal utilizes Open Geospatial Consortium (OGC) web mapping services (WMS) to incorporate continental drought monitors into the GDMP. As of early 2012, the GDM has incorporated continental drought information for North America (North American Drought Monitor), Europe (European Drought Observatory), and Africa (African Drought Monitor developed by Princeton University); interest has been expressed by groups representing Australia and South America; and coordination with appropriate parties in Asia is also expected. Because of the range of climates across the world and the diverse nature of drought and the sectors it impacts, the construction and functioning of each continental drought monitor needs to be appropriate for the continent in question. The GDMP includes a suite of global drought indicators identified by experts and adopted by the WMO as the necessary measures to examine drought from a meteorological standpoint; these global drought indicators provide a base to assist the global integration and interpretation of the continental drought monitors. The GDMP has been included in recent updates to the GEO Work Plan and has benefited from substantial coordination with WMO on both their Global Framework for Climate Services and the National Drought Policy efforts. The GDMP is recognized as having the potential to be a major contributor to both of these activities.

The article analyzes the support for the US market, where the government has allocated almost $ 22.2 billion for the development of dairy cattle. direct and indirect subsidies to the country's dairy sector (35.02 c/l), which is equivalent to 73% of farmers' milk sales, showed relatively high domestic support, export subsidies, conservation programs, risk management programs, disaster relief programs, loan programs, crop insurance, livestock support. Surveys to support the Indian market, which ranks second in the world in raw milk production (9.5%), have shown that almost 80% of small-scale farmers are small-scale farmers. Milk collection is carried out by 130 thousand dairy cooperatives. NABARD (National Bank for Agriculture and Rural Development) under DEDS, provides for subsidies of up to 25% of costs. China is investing heavily in the construction of large dairy farms and livestock complexes with up to 100,000 cows. The Australian market produces 9.3 million tonnes of milk, of which 36% is exported and is the world's fourth exporter of dairy products (6% of the world market). Australia's dairy cattle are characterized by a small amount of direct government support. During 2015-2016, agriculture received financial and commercial assistance over $ 147 million. US in the form of payments to farms. It has been established that price forecasting plays an important role in regulating the milk market in Australia, on the basis of which the profile Ministry, taking into account world prices, generates milk price indices. Analysis of milk production in Switzerland has shown that it remains highly subsidized. In 2013, state support for milk producers amounted to CHF 1.8 billion, incl. direct subsidies are estimated at 1.5 billion Swiss francs, which is 61 thousand Swiss francs per dairy farm, or 0.41 Swiss francs per 1 liter of milk. The state support system for dairy cattle in Canada has been found to include the following instruments: import tariffs that restrict dairy imports; minimum guaranteed prices for raw milk that are set at the maximum amount of milk sold to the dairies within the quota; a system of direct payments to farmers for milk production within the quota. The amount of direct payments per 1 liter of milk is set annually by the government. In order to support Canadian producers in technological modernization aimed at improving the efficiency of milk production, a dairy farm investment program (DFIP) is implemented with state support of $ 250 million. USA According to the Organization for Economic Co-operation and Development (OECD) in Iceland, Japan, Norway and Switzerland, the level of support for dairy producers exceeds on average 70% of the gross income of farmers, in Canada, the EU, Hungary, Korea and the USA the amount of support is 40-55%. An analysis of the support for the development of dairy cattle in the EU countries showed that the following instruments are allocated for these purposes: production restrictions (milk production quotas); government interventions and storage; Establishment of product sales regulations / regulations; the dairy package (including regulating contractual relations in the dairy sector); foreign trade (import regulations, export subsidies); government subsidies. It is found that the main factor that increases the profitability of dairy production in developed countries is the improvement of quality and differentiation of the range. Major factors contributing to the successful development of dairy cattle are increased government support and economical use of resources. Also used are a set of financial incentives, including reducing the tax burden. Key words: Livestock, milk market, domestic support, development programs, cooperation, financial incentives, subsidies, import tariffs, quotas.

This paper presents some results of rainfall forecast in the monsoon onset period in South Vietnam, with the use of ensemble Kalman filter to assimilate observation data into the initial field of the model. The study of rainfall forecasts are experimented at the time of Southern monsoon outbreaks for 3 years (2005, 2008 and 2009), corresponding to 18 cases. In each case, there are five trials, including satellite wind data assimilation, upper-air sounding data assimilation, mixed data (satellite wind+upper-air sounding data) assimilation and two controlled trials (one single predictive test and one multi-physical ensemble prediction), which is equivalent to 85 forecasts for one trial. Based on the statistical evaluation of 36 samples (18 meteorological stations and 18 trials), the results show that Kalman filter assimilates satellite wind data to forecast well rainfall at 48 hours and 72 hours ranges. With 24 hour forecasting period, upper-air sounding data assimilation and mixed data assimilation experiments predicted better rainfall than non-assimilation tests. The results of the assessment based on the phase prediction indicators also show that the ensemble Kalman filter assimilating satellite wind data and mixed data sets improve the rain forecasting capability of the model at 48 hours and 72 hour ranges, while the upper-air sounding data assimilation test produces satisfactory results at the 72 hour forecast range, and the multi-physical ensemble test predicted good rainfall at 24 hour and 48 hour forecasts. The results of this research initially lead to a new research approach, Kalman Filter Application that assimilates the existing observation data into input data of the model that can improve the quality of rainfall forecast in Southern Vietnam and overall country in general.References Bui Minh Tuan, Nguyen Minh Truong, 2013. 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Purpose This paper aims to: first, it studies expert opinions about the future of clean, decentralized energy technology in Australia; second, develop an interpretive and participatory foresighting methodology for a forthcoming study. Design/methodology/approach This paper reports a forecasting study about the future of clean energy. Driven mostly by economics and changing carbon policies, the energy sector is currently moving from fossil fuels to a variety of cleaner technologies. Energy experts have several incommensurate interpretations of how this change will happen. This paper describes the first phase of an ongoing study that foresight clean energy futures in Australia. By building on a participatory method in a scientific expert community, it describes the path from technological presumptions into four parallel yet interconnected scenarios. The paper also explores the social drivers behind these scenarios. Findings First, energy experts in Australia classify futures into four main scenarios: abundant, where energy will be mostly produced by solar cells; traded, where the future of energy lies in virtual power plants and microgrids; circular, which targets Australia’s NetZero goals through biomaterials, carbon capture and new powerful; secure, which secures the country’s energy supply through coal and nuclear energy. Second, they locate policy as the most important form of wildcards. The policy is multilayered from local to US politics and falls outside the scope of forecasting. Research limitations/implications The most important limitations of the study are: first, its reliance on scientific and technological experts, which guarantees its scientific validity but may underrepresent the social drivers of energy; second, this study is a methodological pilot of a larger study that will target industrial, commercial and local drivers; third, its focus on Australia, where politics, the size of the country and climate shape the uptake of clean energy in specific ways, most notably in the case of rapid uptake of solar energy. Practical implications The main practical implications of the paper are its broad focus on clean energy futures and its participatory foresighting approach, which can be repeated in other studies. Social implications The main social implication of the study is that it clearly shows that a technological perspective is necessary but not sufficient in understanding the future of clean energy. The paper also shows that local drivers importantly mold the future and should be taken into account in future studies and policy. Originality/value This paper makes two contributions. First, it organizes several technologies into four scenarios that clarify Australia’s clean energy futures better than a piecemeal study would do. Second, it developed and piloted an interpretive participatory methodology for studying futures by building on references from design research. This methodology will be used in subsequent studies.

Efforts to surveil social media platforms at scale using big data techniques have recently manifested in government-funded research to predict protests following the election of President Trump. This work is part of a computer science research field focused on online “civil unrest prediction” dedicated to forecasting protests across the globe (e.g. Indonesia, Brazil and Australia). Researchers draw upon established data science techniques such as event detection/prediction, but also specific approaches for surveilling social movements are conceived. Besides furthering the academic knowledge-base on civil unrest and protests, the works in this field envision to support a variety of stakeholders with different interests such as governments, the military, law enforcement, human rights organizations and industries such as insurance and supply chain management. I analyze the recent history of civil unrest prediction on social media platforms through examining discourses, implicated actors and technological affordances as encountered in publications and other public online artifacts. In this paper I discuss different risk frames employed by researchers, concerning politics of the technology and argue for a needed public debate on the role of online protest surveillance in democratic societies.

Introduction: the purpose of this research is to analyze the relationships and growth trends of Industry 4.0 within the global universe of current literature on innovation, limited to the subject area of business, administration, and accounting by means of a bibliometric analysis. Method: Scopus is used as a database for the analysis of 513 documents for the period from 1998 to 2021. VOS viewer was used for processing Scopus database and elaboration of figures representing the relationships between samples. Results: the number of documents is exponentially growing, starting with a trend in 2017 and increasing ever since. The most dynamic source is Technological Forecasting And Social Change. The author with most documents is Voight, K. L. with 10, and the most distinguished institution is the University of Johannesburg with 14 documents. The country with more documents is Italy, with 69. According to the database 69 % of the total documents are articles and 23,4 % are conference papers. The number of documents in the area of Business, Management and Accounting, with 513 documents, exceeds by far any other area, representing 42 % of all documents. The funding sponsor with more documents is the National Natural Science Foundations of China with 13 documents. Some of the most updated organizations are Department of Mechanical & Industrial Engineering, The center for digital labor markets. The country with more documents is United Kingdom with 50 documents and 584 citations, followed by United States with 1110 citations. United States has mainly bonds with China, Australia, United Kingdom, Germany and Italy. United Kingdom has mainly bonds with France, Italy, Germany and United States. China’s main relations are Australia, United States, Pakistan, Taiwan, Turkey, Brazil and South Korea. Italy’s main bonds are France, Spain, Brazil, Russian Federation, United Kingdom, Austria and Portugal. It could be determined that countries are mostly grouped in 6 clusters. Conclusion: Although there are not many publications yet, it can be inferred that the term industry 4.0 within the available literature of innovation will keep increasing exponentially over time reinforcing relations between authors, institutions and countries. Based on the current state of literature, the subject area of business, management, and accounting will keep on being the one that encompasses most of the available documents.

Key Performance Indicators in performance management system was attracted by researchers and practitioners. In order to effective implemented KPIs in SMEs, managers must deeply understand about the KPIs, role of KPIs, implemented KPIs. Based on the quantitative method by doing survey with 162 SEMs, author indicated the current situation of the perception of SMEs manager about the fundamental of KPI, roles of KPIs and difficulty implemented KPIs in performance management system. Based on the consistent theory about KPIs , author proposed some solution for manager to enhance their knowledge of KPIs. Keywords SMEs, KPIs , Performance appraisal References Ahmad, M. M., & Dhafr, N. (2002). Establishing and improving manufacturing performance measures. Robotics and Computer-Integrated Manufacturing, 18(3), 171-176. Aylin Ates, Patrizia Garengo, Paola Cocca, Umit Bititci (2013), The development of SME managerial practice for effective performance management, Journal of Small Business and Enterprise Development, 20(1), 28-54Ayoup, H., Omar, N. H., & Rahman, I. K. A. (2012). Implementation of Balance Scorecard (BSC) in a Malaysian GLC: Perceptions of Middle Managers. Asia-Pacific Management Accounting Journal, 7(2), 99-126.Barney, J. (1991), ‘Firm resources and sustained competitive advantage’ Journal of Management, 17(1), 99-120Cagliano, R., Blackmon, K. and Voss, C. (2001).Small firms under microscope: international differences in production/operations management practices and performance. Integrated Manufacturing Systems, 12, 469– 482.Corbett, L. M. (1998). Benchmarking manufacturing performance in Australia and New Zealand. Benchmarking for Quality Management & Technology, 5(4), 271-282. De Waal, A. A. (2003). Behavioral factors important for the successful implementation and use of performance management systems. Management Decision, 41(8), 688-697.Garengo, P., Biazzo, S., & Bititci, U. S. (2005). Performance measurement systems in SMEs: A review for a research agenda. International journal of management reviews, 7(1), 25-47.Epstein, M. J., & Roy, M.-J. (2001). Sustainability in action: Identifying and measuring the key performance drivers. Long range planning, 34(5), 585-604. Evans, N. (2005). Assessing the Balanced Scorecard as a Management Tool for Hotels, International Journal of Contemporary Hospitality, Management Accounting, 17, 5 : 376-390.Jakelski, D., & Lebrasseur, R. (1997). Implementing continuous improvement in the North American mining industry. Technological Forecasting and Social Change, 55(2), 165-177. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5): Prentice hall Upper Saddle River, NJ.Lado, A. A., & Wilson, M. C. (1994), ‘Human resource systems and sustained competitive advantage: A competency-based perspective’, Academy of management review, 19(4), 699-727.Mai Thanh Lan (2016), Xây dựng các năng lực cho nhà quản trị cấp trung nhằm nâng cao năng lực cạnh tranh của doanh nghiệp, Tạp chí Kinh tế & phát triển, 225, 90-99.Lê Quân (2015), Lãnh đạo doanh nghiệp Việt Nam: Hội đồng quản trị; Ban điều hành; Khung năng lực; Thẻ điểm cân bằng; Chỉ số hoàn thành, NXB Đại học Quốc Gia Hà Nội, Hà Nội.Martins, R.A. and Salerno, M.S. (1999). Use of new performance measurement system, some empirical findings. In Managing Operations Networks – VI International EurOMA Conference, Venice, Italy, 7–8 JuneParmenter, D. (2010). Key performance indicators (KPI): developing, implementing, and using winning KPIs: John Wiley & Sons.Rodriguez, R. R., Saiz, J. J. A., & Bas, A. O. (2009). Quantitative relationships between key performance indicators for supporting decision-making processes. Computers in Industry, 60(2), 104-113. Shahin, A., & Mahbod, M. A. (2007). Prioritization of key performance indicators: An integration of analytical hierarchy process and goal setting. International Journal of Productivity and Performance Management, 56(3), 226-240.Nguyễn Đình Thọ (2012). Phương pháp nghiên cứu khoa học trong kinh doanh. Thiết kế và thực hiện. Hà Nội: Nhà xuất bản Lao động xã hộiTsai, Y.-C., & Cheng, Y.-T. (2012). Analyzing key performance indicators (KPIs) for E-commerce and Internet marketing of elderly products: A review. Archives of gerontology and geriatrics, 55(1), 126-132. Yeung, J. F., Chan, A. P., & Chan, D. W. (2009). A computerized model for measuring and benchmarking the partnering performance of construction projects. Automation in Construction, 18(8), 1099-1113.

Accurate and timely forecasting of forest fuel moisture is critical for decision making in the context of bushfire risk and prescribed burning. The moisture content in forest fuels is a driver of ignition probability and contributes to the success of fuel hazard reduction burns. Forecasting capacity is extremely limited because traditional modelling approaches have not kept pace with rapid technological developments of field sensors, weather forecasting and data-driven modelling approaches. This research aims to develop and test a 7-day-ahead forecasting system for forest fuel dryness that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models. The integrated system was established across a diverse range of 30 sites in south-eastern Australia. Fuel moisture was measured hourly using 10-hour automated fuel sticks. A subset of long-term sites (5 years of data) was used to evaluate the relative performance of a selection of machine learning (Light Gradient Boosting Machine (LightGBM) and Recurrent Neural Network (RNN) based Long-Short Term Memory (LSTM)), statistical (VARMAX) and process-based models. The best performing models were evaluated at all 30 sites where data availability was more limited, demonstrating the models' performance in a real-world scenario on operational sites prone to data limitations. The models were driven by daily 7-day continent-scale gridded weather forecasts, in-situ fuel moisture observation and site variables. The model performance was evaluated based on the capacity to successfully predict minimum daily fuel dryness within the burnable range for fuel reduction (11 – 16%) and bushfire risk (