Non-technical summaryManhattan, Berlin and New Delhi all need to take action to adapt to climate change and to reduce greenhouse gas emissions. While case studies on these cities provide valuable insights, comparability and scalability remain sidelined. It is therefore timely to review the state-of-the-art in data infrastructures, including earth observations, social media data, and how they could be better integrated to advance climate change science in cities and urban areas. We present three routes for expanding knowledge on global urban areas: mainstreaming data collections, amplifying the use of big data and taking further advantage of computational methods to analyse qualitative data to gain new insights. These data-based approaches have the potential to upscale urban climate solutions and effect change at the global scale.

The threat of climate change and other risks for ecosystems and human health require a transition of the energy system from fossil fuels towards renewable energies and higher efficiency. The European geographical periphery, and specifically Southern Europe, has considerable potential for renewable energies. At the same time it is also stricken by high levels of public debt and unemployment, and struggles with austerity policies as consequences of the Eurozone crisis. Modeling studies find a broad optimum when searching for a cost-optimal deployment of renewable energy installations. This allows for the consideration of additional policy objectives. Simultaneously, economists argue for an increase in public expenditure to compensate for the slump in private investments and to provide economic stimulus. This paper combines these two perspectives. We assess the potential for renewable energies in the European periphery, and highlight relevant costs and barriers for a large-scale transition to a renewable energy system. We find that a European energy transition with a high-level of renewable energy installations in the periphery could act as an economic stimulus, decrease trade deficits, and possibly have positive employment effects. Our analysis also suggests that country-specific conditions and policy frameworks require member state policies to play a leading role in fostering an energy transition. This notwithstanding, a stronger European-wide coordination of regulatory frameworks and supportive funding schemes would leverage country-specific action. Renewed solidarity could be the most valuable outcome of a commonly designed and implemented European energy transition

Urban street space is increasingly contested. However, it is unclear what a fair street space allocation would look like. We develop a framework of ten ethical principles and three normative perspectives on street space -streets for transport, streets for sustainability, and streets as place -and discuss 14 derived street space allocation mechanisms. We contrast these ethically grounded allocation mechanisms with real-world allocation in 18 streets in Berlin. We find that car users, on average, had 3.5 times more space available than non-car users. While some allocation mechanisms are more plausible than others, none is without normative implications. Without exception, all principles suggest that on-street parking for cars is difficult to justify, and that more space should be allocated to cycling. We argue that street space fairness principles should be systematically integrated into urban and transport planning.

Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.