Localization in Sardinia and Its Obstacles: A Reply to Hospers’ “Localization in Europe’s Periphery: Tourism Development in Sardinia”

In this reply to Hospers' “Localization in Europe's Periphery: Tourism Development in Sardinia” by Gert-Jan Hospers (2003), we argue that the author's advocacy of localized economic policies as a viable means to the economic development of Sardinia does not take into account current institutional assets that prevent Sardinia from pursuing localized interests effectively. We first discuss the historical background of these institutional assets, highlighting that a top-down approach to decision-making has characterized relations between Sardinia and the central state for most of the modern era. We then discuss the institutional and economic impediments to Sardinian attempts to pursue localized policies in light of recent institutional conflicts between region and central state. Our conclusion is that the localization of economic strategies necessitates entwined localization of decision-making powers in order to be effective.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Sustainable Rural Tourism: Lessons for Rural Development

Rural areas are recognised for their complex, multi-functional capacities with a range of different interest groups claiming their rights to, and use of, different rural spaces. The current rural development paradigm that is evident across the globe is epitomised by the European LEADER approach. Using evidence from the proposed National Park in Northern Ireland, we ask the question: what is the potential of sustainable rural tourism to contribute to rural development? Within our analysis we consider the scope for adaptive tourism to overcome some of the ongoing challenges that have been identified within the LEADER approach. Four themes are revealed from this analysis: institutional (in)capacity; legitimacy of local groups; navigating between stakeholder interests; and sustainable tourism in practice. These issues, discussed in turn, have clear implications for the new rural development programme.

Incorporating Higher Moments into Value at Risk Forecasting

Value-at-risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a simple approach to forecasting of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting the standard normal distribution with the first four moments, which are allowed to vary over time. In an extensive empirical study, we compare the GCE approach to other models of VaR forecasting and conclude that it provides accurate and robust estimates of the realized VaR. In spite of its simplicity, on our dataset GCE outperforms other estimates that are generated by both constant and time-varying higher-moments models.