Descrizione del progetto
Un’ancora di salvezza per gli alberghi costieri vulnerabili dell’Italia
Le spiagge italiane sono la linfa vitale del suo settore alberghiero, ma i rischi posti dai cambiamenti climatici a tal riguardo vengono ampiamente ignorati. Sorprendentemente, infatti, la valutazione degli impatti economici e ambientali esercitati dall’innalzamento costiero del livello del mare sul settore alberghiero è stata sinora scarsa o inesistente. Sostenuto dal programma di azioni Marie Skłodowska-Curie, il progetto SEA-LIMITHS utilizzerà l’intelligenza artificiale e l’apprendimento automatico per studiare le conseguenze economiche generate dall’innalzamento del livello del mare sulle spiagge sabbiose del Veneto e dell’Emilia-Romagna. Grazie all’abbinamento dei dati generati dagli utenti con mappe di copertura e uso del suolo, SEA-LIMITHS valuterà le perdite economiche per l’industria dell’ospitalità dal 2050 al 2100 in base a diversi scenari climatici se non verranno implementate misure di adattamento, mettendo a confronto queste previsioni con i costi associati all’adozione di soluzioni di adattamento alternative.
Obiettivo
There has been no evaluation of the economic and environmental impacts of coastal sea-level rise on the hotel industry utilising Artificial Intelligence (A.I.) to analyse the whole transmission channel. The SEA-LIMITHS project will contribute to the expanding but still limited body of information on climate change and tourism losses. Despite the importance of sand beaches to the hotel sector in the vast majority of Italian beaches, tragically few policymakers incorporate quantifiable risk assessments of sand beach loss due to climate change. This study investigates the impacts and economic consequences of sea level rise on sandy beaches and beach hospitality losses owing to beach erosion and flooding in two economically important Italian coastal areas, Veneto and Emilia-Romagna. Combining sandy beaches with a proposed rich dataset based on machine learning and artificial intelligence, user-generated data is matched to the land cover/land use map to separate the hospitality sectors (accommodation, food and drink, sports and leisure) in order to assess economic losses in the hospitality industry from 2050 to 2100 using representative concentration pathways (RCP 4.5 and RCP 8.5). Taking into consideration no adaptation measures, the predicted cumulative damage costs per hospitality industry are analysed and contrasted to the investment cost of adopting alternative adaption solutions. Using an Italy-specific computable general equilibrium model, the estimated direct costs will be used in a macroeconomic assessment as input to determine how changes in hospitality activities effect regional value added. The fact that this proposal pertains to several disciplines of study is what gives it its creative interdisciplinarity. The combination of economic and environmental research, together with the developing and increasing methods of machine learning, will produce an outstanding piece of work and significantly advance the author's scientific career.
Campo scientifico
- natural sciencesearth and related environmental sciencesphysical geographycoastal geography
- natural sciencesearth and related environmental sciencessoil sciencesland-based treatment
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Meccanismo di finanziamento
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinatore
30123 Venezia
Italia