Five Baltic Ports Togehter: Forecasts, Trends and Recommendations

The ports of Stockholm, Tallinn, Helsinki, Naantali and Turku play key roles in making the Central Baltic region accessible. Effective, competitive, eco-friendly and safe port procedures and solutions for the transportation of goods are of major importance for trade in the Baltic Sea region. This report presents the most essential results and recommendations of the PENTA project, which focused on how ports could better comprehend and face current and future challenges facing carriage of goods by sea. Each of the four work packages (WPs) of the PENTA project analysed the changes from a different perspective. WP2 focused on traffic flows between the PENTA ports. Its main emphasis was on the ports, shipowners, and logistics companies that are the key parties in freight transport and on the changes affecting the economy of those ports. In WP3 noise as an environmental challenge for ports was investigated and the analysis also shed light on the relationship between the port and the city. In WP4 procedures related to safety, security and administrative procedures were researched. The main emphasis was on identifying the requirements for the harmonisation of those procedures. Collaboration is highlighted throughout this report. In order to prepare for the future, it was found that ports need to respond to growing competition, increasing costs and shifts in customer demand by strengthening their existing partnerships with other actors in the maritime cluster. Cargo and passenger transport are the main sources of income for most ports. Cargo traffic between the PENTA ports is expected to grow steadily in the future and the outlook for passenger traffic is positive. However, to prepare for the future, ports should not only secure the core activities which generate revenue but also seek alternative ways to make profit. In order to gain more transit traffic, it is suggested that ports conduct a more thorough study of the future requirements for doing business with Russia. The investigation of noise at ports revealed two specific dilemmas that ports cannot solve alone. Firstly, the noise made by vessels and, secondly, the relationship between the port and the surrounding city. Vessels are the most important single noise source in the PENTA ports and also one of the hardest noise sources to handle. Nevertheless, port authorities in Finland and Sweden are held responsible for all noise in the port area, including noise produced by vessels, which is noise the port authority can only influence indirectly. Building housing by waterfront areas close to ports may also initiate disagreements because inhabitants may want quiet areas, whereas port activities always produce some noise from their traffic. The qualitative aspects of the noise question, cooperating with the stakeholders and the communicating of issues related to noise are just as important. We propose that ports should follow the logic of continuous improvement in their noise management. The administrative barriers discussed in this report are mainly caused by differences in international and national legislation, variations in the customs procedures of each country, the incompatibility of the IT systems used in maritime transport, noncompliance with regulations regarding dangerous goods, and difficulties in applying Schengen regulations to vessels from non-EU countries. Improving the situation is out of the hands of the ports to do alone and requires joint action on a variety of levels, including the EU, national authorities and across administrative borders.

Electricity price forecasting accounting for renewable energies: optimal combined forecasts

Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al(2007 and 2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models(according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combi nation of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to Decem ber 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.

Long range forecasts of the numbers of Helicoverpa punctigera and H-armigera (Lepidoptera : Noctuidae) in Australia using the Southern Oscillation Index and the Sea Surface Temperature

The use of long-term forecasts of pest pressure is central to better pest management. We relate the Southern Oscillation Index (SOI) and the Sea Surface Temperature (SST) to long-term light-trap catches of the two key moth pests of Australian agriculture, Helicoverpa punctigera (Wallengren) and H. armigera (Hubner), at Narrabri, New South Wales over 11 years, and for H. punctigera only at Turretfield, South Australia over 22 years. At Narrabri, the size of the first spring generation of both species was significantly correlated with the SOI in certain months, sometimes up to 15 months before the date of trapping. Differences in the SOI and SST between significant months were used to build composite variables in multiple regressions which gave fitted values of the trap catches to less than 25% of the observed values. The regressions suggested that useful forecasts of both species could be made 6-15 months ahead. The influence of the two weather variables on trap catches of H. punctigera at Turretfield were not as strong as at Narrabri, probably because the SOI was not as strongly related to rainfall in southern Australia as it is in eastern Australia. The best fits were again given by multiple regressions with SOI plus SST variables, to within 40% of the observed values. The reliability of both variables as predictors of moth numbers may be limited by the lack of stability in the SOI-rainfall correlation over the historical record. As no other data set is available to test the regressions, they can only be tested by future use. The use of long-term forecasts in pest management is discussed, and preliminary analyses of other long sets of insect numbers suggest that the Southern Oscillation Index may be a useful predictor of insect numbers in other parts of the world.

Risk attitude, planting conditions and the value of seasonal forecasts to a dryland wheat grower

The value of a seasonal forecasting system based on phases of the Southern Oscillation was estimated for a representative dryland wheat grower in the vicinity of Goondiwindi. In particular the effects on this estimate of risk attitude and planting conditions were examined. A recursive stochastic programming approach was used to identify the grower's utility-maximising action set in the event of each of the climate patterns over the period 1894-1991 recurring In the imminent season. The approach was repeated with and without use of the forecasts. The choices examined were, at planting, nitrogen application rate and cultivar and, later in the season, choices of proceeding with or abandoning each wheat activity, The value of the forecasting system was estimated as the maximum amount the grower could afford to pay for its use without expected utility being lowered relative to its non use.

Tracks of Change - the changing value commitments of non-profit organisations

If nonprofit organisations are moving towards more market oriented ways of operating, is this changing the traditional meanings and value of commitments associated with their activities? This article discusses the findings of a research project conducted by the University of Queensland into the impact that changes in government policies are having on the community services sector, in particular disability services. The values and belief systems traditionally associated with the sector were found to be fundamentally unaltered.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.

Capacity utilisation and profitability: A decomposition of short-run profit efficiency

The principal aim of this paper is to measure the amount by which the profit of a multi-input, multi-output firm deviates from maximum short-run profit, and then to decompose this profit gap into components that are of practical use to managers. In particular, our interest is in the measurement of the contribution of unused capacity, along with measures of technical inefficiency, and allocative inefficiency, in this profit gap. We survey existing definitions of capacity and, after discussing their shortcomings, we propose a new ray economic capacity measure that involves short-run profit maximisation, with the output mix held constant. We go on to describe how the gap between observed profit and maximum profit can be calculated and decomposed using linear programming methods. The paper concludes with an empirical illustration, involving data on 28 international airline companies. The empirical results indicate that these airline companies achieve profit levels which are on average US$815m below potential levels, and that 70% of the gap may be attributed to unused capacity. (C) 2002 Elsevier Science B.V. All rights reserved.

Patterns of governance: The curious case of non-profit community services in Australia

Community has taken on a new significance in Australian social policy discourse. Seemingly sound and morally justifiable, in the context Of neo-liberalism the language of community positions non-profit delivery of services as superior to state-provided services. As a consequence, non-profit community services are being centrally positioned to mediate the relationship between the state and citizen subjects. In the first part of this paper we trace some of the key historical developments in Australia's welfare state and patterns of governance that are propelling the non-profit sector firm the margins to the centre. The second section examines the relationship between Australia's shifting political landscape and the emerging welfare regime. One key feature of this new regime is the attempt to relocate citizenship away from the domain of the state and into that of civil society. The article concludes by sketching out some research themes, focusing, for example, on the impact of devolution of governance in terms of client rights and public accountability.

Voluntary disclosure of forward-looking earnings forecasts in Australia

This study examines the voluntary disclosure of future earnings information in annual reports for Australian listed companies. We find that most Australian companies in our sample do not provide quantitative earnings, forecasts in their annual reports, although more than half of the sample do disclose forward-looking information relating to earnings, without specifically disclosing point estimates for the future. These companies mostly supply qualitative information with a positive bias, while the remainder of the sample discloses no forward-looking information relating to earnings. Our findings also suggest that larger companies with less volatile earnings tend to provide more future earnings information than smaller companies with relatively volatile earnings.

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.