Zusammenfassung
Aufgrund einer stetigen Zuwanderung junger Menschen in der Ausbildungs- und Berufsfindungsphase scheinen prosperierende Städte wie Hamburg von den Folgen des demographischen Wandels nicht an erster Stelle betroffen zu sein. Eine kleinräumige Betrachtung der demographischen Entwicklung innerhalb der Stadt verdeutlicht aber, dass insbesondere die monofunktionalen Wohnquartiere in den suburban geprägten Stadtteilen von der Zuwanderung nicht profitieren. Vielmehr weisen diese Gebiete eine starke Überalterung der Wohnbevölkerung auf. Diese Entwicklung lässt einen dringenden Handlungsbedarf für die lokale Stadtentwicklung und Stadtpolitik in den nächsten Jahren erkennen.
Neuroeconomics has the potential to fundamentally change the way economics is done. This article identifies the ways in which this will occur, pitfalls of this approach, and areas where progress has already been made. The value of neuroeconomics studies for social policy lies in the quality, replicability, and relevance of the research produced. While most economists will not contribute to the neuroeconomics literature, we contend that most economists should be reading these studies.
2) See, for example, the study entitled "Creative Cities" (2008) by Roland BeRgeR, which compares the attractiveness of ten German cities to the creative class.
From the current perspectives of urban health and environmental justice research, health is the result of a combination of individual, social and environmental factors. Yet, there are only few attempts to determine their joint influence on health and well-being. Grounded in debates surrounding conceptual models and based on a data set compiled for the city of Hamburg, this paper aims to provide insights into the most important variables influencing urban health. Theoretically, we are primarily referring to the conceptual model of health-related urban well-being (UrbWellth), which systemizes urban influences in four sectors. The systematization of the conceptual model is empirically confirmed by a principal component analysis: the factors derived from the data correspond well with the deductively derived model. Additionally, a multiple linear regression analysis was used to identify the most important variables influencing the participant's self-rated health (SRH): rating of one's social network, rating of neighborhood air quality, rating of neighborhood health infrastructure, heat stress (day/outdoors), cold stress (night/indoors). When controlling for age, income and smoking behavior, these variables explain 12% of the variance of SRH. Thus, these results support the concept of UrbWellth empirically. Finally, the study design helped to identify hotspots with negative impact on SRH within the research areas.
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