|
on Tourism Economics |
| By: | Yesim Aliefendioglu; Gizem Var; Harun Tanrivermis |
| Abstract: | Creating employment opportunities and increasing the national income, the tourism sector contributes to increased prosperity. In the Tenth Development Plan of Turkey covering the 2014-2018 period, it has been emphasized that sustainable tourism activities will be enhanced within the scope of an environmentally sensitive and responsible tourism understanding and the sociocultural and environmental degradation associated with tourism will be reduced. In the implementation of tourism activities for local and regional economic development, the main objective is minimizing the impact on the environment and socio-cultural structures.In this study, sustainable tourism opportunities in settlements, of which development is based on tourism, are elaborated and the Göreme Municipality, whose tangible and intangible cultural assets are at risk of being lost due to the indirect effects of uncontrolled tourism, are chosen as the study area. Settlements such as Göreme that owe their economic development success to culture tourism have to protect their existing natural, cultural, and social features in order to ensure the continuation of this development. While conservation of the gained identities of settlements is a necessity for urban and regional development, it is also a requirement to feel the sense of belonging to the city or the region. The Göreme Municipality is a settlement with an identity thanks to the existence of a cultural landscape and in such settlements, although the main objective of tourism is to enhance the quality of life economic viability, the sustainability of tangible and intangible assets that should be protected should not be thrown into the background. However, the study results suggest that with the transformation of the old residential buildings of the region built using traditional materials to hotels due to demand pressures, the local population becomes alienated from the town and the culture around them.The sustainability of the intangible cultural heritage has not been achieved; the real estate in the region changed hands with the appeal of hotel investments to investors out of the region; and the low level of utilization of the local people from increased revenues has led to a failure in ensuring regional development and a fall in the quality of life of residents in the area. It is understood that in case the current trends continue, tourism will have a damaging structure rather than providing economic contribution for the local com |
| Keywords: | Governance; local and regional development; real estate ownership; Sustainable and cultural tourism; Transformation |
| JEL: | R3 |
| Date: | 2017–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2017_386&r=tur |
| By: | Maria Martini Barzolai (Dept. of Management, Università Ca' Foscari Venice); Anna Moretti (Dept. of Management, Università Ca' Foscari Venice) |
| Abstract: | Development and competitiveness of tourism destinations have been often associated by previous literature to the effectiveness of destination governance. However, a thorough understanding of relevant dimensions explaining destination governance success, both from theoretical and analytical perspectives, is still lacking. With this paper the authors contribute to advancing the theory on destination governance, particularly focusing on the issue of governance success from a dynamic perspective. The theoretical and analytical framework proposed is based on the most recent developments of network governance studies and proposes an investigation of micro-dynamics to understand governance evolutionary paths towards successful or unsuccessful outcomes. The theoretical and analytical framework is applied to the case of an emerging network of stakeholders of an Alpine destination, the Comelico area. The analysis of the case, developed through an action research approach, allowed to identify both static (commitment and competences) and dynamic (framing and mobilizing) relevant dimensions in explaining successful or unsuccessful outcomes for emerging tourism destinations. |
| Keywords: | Destination governance, governance success, networks, emerging destination, commitment, competences |
| JEL: | L14 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:vnm:wpdman:152&r=tur |
| By: | Oscar Claveria (AQR-IREA AQR-IREA, University of Barcelona (UB). Tel. +34-934021825; Fax. +34-934021821. Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Diagonal 690, 08034 Barcelona, Spain); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC)); Salvador Torra (Riskcenter-IREA, Department of Econometrics and Statistics, University of Barcelona (UB)) |
| Abstract: | In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon. |
| Keywords: | STL decomposition, non-parametric regression, time series features, forecast accuracy, machine learning, tourism demand, regional analysis. JEL classification:C45, C51, C53, C63, E27, L83. |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:201805&r=tur |