The Winnipeg principles, WTO and sustainable development: Proposed policies for reconciling trade and the environment

There is, a widespread belief that the WTO has made virtually no concessions to environmentalists about their concerns arising from free trade and the process of globalization. There are concerns that these processes may undermine prospects for sustainable development. Following, the United Nations Conference on Environment and Development held in Rio de Janeiro in 1992, the International Institute for Sustainable Development was established to advocate policies to support sustainable development within Canada and globally. In 1994, it proposed the Winnipeg Principles as. a means for reconciling international trade and development so as to: achieve sustainable development (ISD, 1994a,b). These seven principles are outlined in this article and assessed:. Although the International Institute for Sustainable Development had hoped: through these principles to influence the work programme of the Environment and Trade Committee of WTO, it seems to have little effect. Probably if these principles had been seriously considered by WTO, the serious social conflicts which emerged globally at the beginning of this century would have been avoided, and we would be in a better position to understand the complex links between trade, environment and sustainable development and adopt relevant policies. Copyright (C) 2001 John Wiley & Sons, Ltd and ERP Environment.

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.

An advanced method for eliminating impacts from frequency deviations in power system signal processing

Frequency deviation is a common problem for power system signal processing. Many power system measurements are carried out in a fixed sampling rate assuming the system operates in its nominal frequency (50 or 60 Hz). However, the actual frequency may deviate from the normal value from time to time due to various reasons such as disturbances and subsequent system transients. Measurement of signals based on a fixed sampling rate may introduce errors under such situations. In order to achieve high precision signal measurement appropriate algorithms need to be employed to reduce the impact from frequency deviation in the power system data acquisition process. This paper proposes an advanced algorithm to enhance Fourier transform for power system signal processing. The algorithm is able to effectively correct frequency deviation under fixed sampling rate. Accurate measurement of power system signals is essential for the secure and reliable operation of power systems. The algorithm is readily applicable to such occasions where signal processing is affected by frequency deviation. Both mathematical proof and numerical simulation are given in this paper to illustrate robustness and effectiveness of the proposed algorithm. Crown Copyright (C) 2003 Published by Elsevier Science B.V. All rights reserved.

Inflation expectations, interest rates and arbitrary income transfers

Unexpected inflation, disinflation or deflation cause arbitrary income transfers between an economy's borrowers and lenders. This redistribution results from distorted real interest rates that are too high when price level changes are over-predicted and too low when they are under-predicted. This article shows that in Australia's case, inflation expectations were mostly biased upwards throughout the 1990s, according to the Melbourne Institute of Applied Economic and Social Research series and to a new derived series based on bond yields, implying that real interest rates were too high over this time. In turn, this caused substantial arbitrary income transfers from debtors to creditors, estimated to have averaged up to 3 per cent of gross domestic product over the period.

Cadence, war and security

An increased incidence of attack has been identified as a major characteristic of the new threat posed by terrorist groups such as al Qaeda. This article considers what such a change means for Western national security systems by examining bow different parts of the system change over time. It becomes evident that Western national security systems are structured on an assumption of comparatively slow state-based threats. In contrast, terrorist franchises operate at a faster pace, are more 'lightweight' and can adapt within the operational and capability cycles of Western governments. Neither network-centric warfare nor an improved assessment of the threat, called for by some, offers a panacea in this regard. Rather, it is clear that not only do Western governments need to adjust their operational and capability cycles, but that they also need a greater diversity of responses to increase overall national security resilience and offer more tools for policy-makers.

How do practitioners use conceptual modeling in practice?

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether practitioners still embraced conceptual modeling seriously. In addition, what are the most popular techniques and tools used for conceptual modeling? What are the major purposes for which conceptual modeling is used? The study found that the top six most frequently used modeling techniques and methods were ER diagramming, data flow diagramming, systems flowcharting, workflow modeling, UML, and structured charts. Modeling technique use was found to decrease significantly from smaller to medium-sized organizations, but then to increase significantly in larger organizations (proxying for large, complex projects). Technique use was also found to significantly follow an inverted U-shaped curve, contrary to some prior explanations. Additionally, an important contribution of this study was the identification of the factors that uniquely influence the decision of analysts to continue to use modeling, viz., communication (using diagrams) to/from stakeholders, internal knowledge (lack of) of techniques, user expectations management, understanding models' integration into the business, and tool/software deficiencies. The highest ranked purposes for which modeling was undertaken were database design and management, business process documentation, business process improvement, and software development. (c) 2005 Elsevier B.V. All rights reserved.