European Union’s vulnerability to climate change stretches far beyond its borders because many of its economic sectors, such as meat and dairy, use raw materials sourced from far afield. Cross-border climate vulnerability is a relatively new subject in scientific literature, while of high societal and economic relevance. We quantify these climate vulnerabilities with a focus on drought risk and assessed them for 2030, 2050, 2085 and for RCP 2.6 and 6.0 climate scenarios. Here we find that more than 44% of the EU agricultural imports will become highly vulnerable to drought in future because of climate change. The drought severity in production locations of the agricultural imports in 2050 will increase by 35% compared to current levels of drought severity. This is particularly valid for imports that originate from Brazil, Indonesia, Vietnam, Thailand, India and Turkey. At the same time, imports from Russia, Nigeria, Peru, Ecuador, Uganda and Kenya will be less vulnerable in future. We also report that the climate vulnerabilities of meat and dairy, chocolate (cocoa), coffee, palm oil-based food and cosmetic sectors mainly lie outside the EU borders rather than inside.
MULTIFILE
BACKGROUND: Ambulatory children with Spina Bifida (SB) often show a decline in physical activity leading to deconditioning and functional decline. Therefore, assessment and promotion of physical activity is important. Because energy expenditure during activities is higher in these children, the use of existing pediatric equations to predict physical activity energy expenditure (PAEE) may not be valid. AIMS: (1) To evaluate criterion validity of existing predictions converting accelerocounts into PAEE in ambulatory children with SB and (2) to establish new disease-specific equations for PAEE. METHODS: Simultaneous measurements using the Actical, the Actiheart, and indirect calorimetry took place to determine PAEE in 26 ambulatory children with SB. DATA ANALYSIS: Paired T-tests, Intra-class correlations limits of agreement (LoA), and explained variance (R2) were used to analyze validity of the prediction equations using true PAEE as criterion. New equations were derived using regression techniques. RESULTS: While T-tests showed no significant differences for some models, the predictions developed in healthy children showed moderate ICC’s and large LoA with true PAEE. The best regression models to predict PAEE were: PAEE = 174.049 + 3.861 × HRAR – 60.285 × ambulatory status (R2 = 0.720) and PAEE = 220.484 + 0.67 × Actical counts – 60.717 × ambulatory status (R2 = 0.681). CONCLUSIONS: Existing equations to predict PAEE are not valid for use in children with SB for the individual evaluation of PAEE. The best regression model was based on HRAR in combination with ambulatory status, followed by a new model for the Actical monitor. A benefit of HRAR is that it does not require the use of expensive accelerometry equipment. Further cross-validation of these models is still needed.
The following paper presents a methodology we developed for addressing the case of a multi-modal network to be implemented in the future. The methodology is based on a simulation approach and presents some characteristics that make a challenge to be verified and validated. To overcome this limitation, we proposed a novel methodology that implies interaction with subjectmatter experts, revision of current data, collection and assessment of future performance and educated assumptions. With that methodology we could construct the complete passenger trajectory Door to door in Europe. The results indicate that the approach allows to approach infrastructure analysis at an early stage to have an initial estimation of the upper boundary of performance indicators. To exemplify this, we present the results for a case study in Europe.
