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A Quantitative Model Supporting Socially Responsible Public Investment Decisions for Sustainable Tourism

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  • Aurora Skrame

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende (CS), Italy)

  • Claudio Ciancio

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende (CS), Italy)

  • Vincenzo Corvello

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende (CS), Italy)

  • Roberto Musmanno

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende (CS), Italy)

Abstract

The purpose of this article is to develop a quantitative model that supports policy makers in the tourism sector in making socially responsible investment decisions. In particular, this paper proposes a methodological approach to assess the impact of strategic decisions at the policy level, in the field of tourism, from an economic, environmental and social point of view. The Calabria region, in Italy, has been chosen as a real-world case study. Based on historical data, the study identifies the main levers that influence tourism-related dynamics in Calabria. A quantitative forecasting model to support future investment decisions for sustainable tourism has then been developed. This problem is modeled through a multi-criteria optimization framework. To initialize such a framework, a non-linear autoregressive network with exogenous inputs (NARX) has been used. The proposed model is a flexible instrument to evaluate public investment policies in the field of tourism from the point of view of sustainability and social responsibility.

Suggested Citation

  • Aurora Skrame & Claudio Ciancio & Vincenzo Corvello & Roberto Musmanno, 2020. "A Quantitative Model Supporting Socially Responsible Public Investment Decisions for Sustainable Tourism," IJFS, MDPI, vol. 8(2), pages 1-9, June.
  • Handle: RePEc:gam:jijfss:v:8:y:2020:i:2:p:33-:d:365882
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    References listed on IDEAS

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