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The present study develops a methodology to assess transportation-related CO2 emissions of European city tourism. In doing so, not only is travel distance considered, but also the chosen transportation modes and the particularities of the different cities' source markets. The major contribution of this study is the implementation of this learning methodology into a decision support system for destination management organizations of cities. Based on a sample from 2018 of 48 European cities with at least 40 source markets, the range of aggregate transportation-related CO2 emissions of European city tourism is estimated. Moreover, a longitudinal analysis of the exemplary city of Vienna covering the period of 1990 to 2018 is carried out. Finally, some policy recommendations of how destination management organizations can contribute to make the estimated transportation-related CO2 emissions even more precise and on how to make European city tourism more environmentally sustainable are drawn. [ABSTRACT FROM AUTHOR]

The widespread use of the Internet and online forecasting systems offer unprecedented opportunities to leverage collective intelligence to produce increasingly accurate forecasts. Forecast support systems also offer the opportunity to address one of the weakest aspects of expert forecasting methods, the identification of experts. In the published literature, significant criticism is addressed to the subjectivity of expert identification methods, as different methods can lead, ceteris paribus, to significantly different results. This article introduces an approach to define objectively levels of expertise in large groups in a panel setting. This information is used to fine-tune panel members' contribution to the compound forecast in an attempt to improve the accuracy of the aggregated forecast. Tested on prospects collected from the UN World Tourism Organization Panel of Tourism Experts - probably world's most widely used and influential forecasts for the tourism sector - the proposed approach proves efficient in identifying experts in large groups of individuals. The results also indicate that the method is promising in leveraging their collective knowledge to return more accurate forecasts compared to simpler methods. [ABSTRACT FROM AUTHOR]