Inicio  /  Climate  /  Vol: 10 Par: 10 (2022)  /  Artículo
ARTÍCULO
TITULO

The Value-Add of Tailored Seasonal Forecast Information for Industry Decision Making

Clare Mary Goodess    
Alberto Troccoli    
Nicholas Vasilakos    
Stephen Dorling    
Edward Steele    
Jessica D. Amies    
Hannah Brown    
Katie Chowienczyk    
Emma Dyer    
Marco Formenton    
Antonio M. Nicolosi    
Elena Calcagni    
Valentina Cavedon    
Victor Estella Perez    
Gertie Geertsema    
Folmer Krikken    
Kristian Lautrup Nielsen    
Marcello Petitta    
José Vidal    
Martijn De Ruiter    
Ian Savage and Jon UptonaddShow full author listremoveHide full author list    

Resumen

There is a growing need for more systematic, robust, and comprehensive information on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments that focus on the decision-making context, involve participatory or co-evaluation approaches, avoid over-simplification, and address both the quantitative (e.g., economic) and qualitative (e.g., social) values of climate services. The 12 case studies that formed the basis of the European Union-funded SECLI-FIRM project were co-designed by industrial and research partners in order to address these gaps while focusing on the use of tailored sub-seasonal and seasonal forecasts in the energy and water industries. For eight of these case studies, it was possible to apply quantitative economic valuation methods: econometric modelling was used in five case studies while three case studies used a cost/loss (relative economic value) analysis and avoided costs. The case studies illustrated the challenges in attempting to produce quantitative estimates of the economic value-add of these forecasts. At the same time, many of them highlighted how practical value for users?transcending the actual economic value?can be enhanced; for example, through the provision of climate services as an extension to their current use of weather forecasts and with the visualisation tailored towards the user.

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