Independent lines of research on urbanization, urban areas, and carbon have advanced our understanding of some of the processes through which energy and land uses affect carbon. This synthesis integrates some of these diverse viewpoints as a first step toward a coproduced, integrated framework for understanding urbanization, urban areas, and their relationships to carbon. It suggests the need for approaches that complement and combine the plethora of existing insights into interdisciplinary explorations of how different urbanization processes, and socio-ecological and technological components of urban areas, affect the spatial and temporal patterns of carbon emissions, differentially over time and within and across cities. It also calls for a more holistic approach to examining the carbon implications of urbanization and urban areas, based not only on demographics or income but also on other interconnected features of urban development pathways such as urban form, economic function, economic-growth policies, and other governance arrangements. It points to a wide array of uncertainties around the urbanization processes, their interactions with urban socio-institutional and built environment systems, and how these impact the exchange of carbon flows within and outside urban areas. We must also understand in turn how carbon feedbacks, including carbon impacts and potential impacts of climate change, can affect urbanization processes. Finally, the paper explores options, barriers, and limits to transitioning cities to low-carbon trajectories, and suggests the development of an end-to-end, coproduced and integrated scientific understanding that can more effectively inform the navigation of transitional journeys and the avoidance of obstacles along the way. Why Urbanization, Urban Areas, and Carbon?In recent years, the relationships between urbanization, urban areas, and the carbon cycle have generated increased interest in research and policy circles for a variety of reasons. We have urbanized our planet to an unprecedented level. The concentrations of infrastructure, economic and social activities, and populations in cities create growing demands for fossil fuels and carbon-intensive materials to build and power domestic services, commercial buildings, industrial processes, telecommunications systems, water COMMENTARY 10.1002/2014EF000258Special Section: Urbanization, carbon cycle, and climate change Key Points:• We need integrated, coproduced approaches to urbanization, urban areas, and carbon • Urbanization uncertainties are of similar magnitude to carbon uncertainties • Lock-ins in urbanization, cities, and carbon constrain low-carbon transitions provision, waste production, travel, and a seemingly endless array of other uses. By 2050, the global urban population is expected to increase from 3.6 billion to over 6 billion, mainly in low-and middle-income countries [United Nations Department of Economic and Social Affairs, 2010]. With urban extent forecast to triple between 2000 and 2030, more urban land e...

Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity—or complete lack—of accurate information regarding existing road infrastructure, challenging the effective identification of where investments should be made. Previous literature has focused on overcoming this gap through the use of satellite imagery to detect and map roads. In this piece, we extend this literature by leveraging satellite imagery to estimate road quality and concomitant information about travel speed. We adopt a transfer learning approach in which a convolutional neural network architecture is first trained on data collected in the United States (where data is readily available), and then “fine-tuned” on an independent, smaller dataset collected from Nigeria. We test and compare eight different convolutional neural network architectures using a dataset of 53,686 images of 2,400 kilometers of roads in the United States, in which each road segment is measured as “low”, “middle”, or “high” quality using an open, cellphone-based measuring platform. Using satellite imagery to estimate these classes, we achieve an accuracy of 80.0%, with 99.4% of predictions falling within the actual or an adjacent class. The highest performing base model was applied to a preliminary case study in Nigeria, using a dataset of 1,000 images of paved and unpaved roads. By tailoring our US-model on the basis of this Nigeria-specific data, we were able to achieve an accuracy of 94.0% in predicting the quality of Nigerian roads. A continuous case estimate also showed the ability, on average, to predict road quality to within 0.32 on a 0 to 3 scale (with higher values indicating higher levels of quality).

Migration provides a strategy for rural Mexican households to cope with, or adapt to, weather events and climatic variability. Yet prior studies on “environmental migration” in this context have not examined the differences between choices of internal (domestic) or international movement. In addition, much of the prior work relied on very coarse spatial scales to operationalize the environmental variables such as rainfall patterns. To overcome these limitations, we use fine-grain rainfall estimates derived from NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite. The rainfall estimates are combined with Population and Agricultural Census information to examine associations between environmental changes and municipal rates of internal and international migration 2005–2010. Our findings suggest that municipal-level rainfall deficits relative to historical levels are an important predictor of both international and internal migration, especially in areas dependent on seasonal rainfall for crop productivity. Although our findings do not contradict results of prior studies using coarse spatial resolution, they offer clearer results and a more spatially nuanced examination of migration as related to social and environmental vulnerability and thus higher degrees of confidence.