A global fingerprint of macro-scale changes in urban structure from 1999 to 2009

Urban population now exceeds rural population globally, and 60–80% of global energy consumption by households, businesses, transportation, and industry occurs in urban areas. There is growing evidence that built-up infrastructure contributes to carbon emissions inertia, and that investments in infrastructure today have delayed climate cost in the future. Although the United Nations statistics include data on urban population by country and select urban agglomerations, there are no empirical data on built-up infrastructure for a large sample of cities. Here we present the first study to examine changes in the structure of the world\u27s largest cities from 1999 to 2009. Combining data from two space-borne sensors—backscatter power (PR) from NASA\u27s SeaWinds microwave scatterometer, and nighttime lights (NL) from NOAA\u27s defense meteorological satellite program/operational linescan system (DMSP/OLS)—we report large increases in built-up infrastructure stock worldwide and show that cities are expanding both outward and upward. Our results reveal previously undocumented recent and rapid changes in urban areas worldwide that reflect pronounced shifts in the form and structure of cities. Increases in built-up infrastructure are highest in East Asian cities, with Chinese cities rapidly expanding their material infrastructure stock in both height and extent. In contrast, Indian cities are primarily building out and not increasing in verticality. This new dataset will help characterize the structure and form of cities, and ultimately improve our understanding of how cities affect regional-to-global energy use and greenhouse gas emissions

Does Size Matter? Scaling of CO\u3csub\u3e2\u3c/sub\u3e Emissions and U.S. Urban Areas

Urban areas consume more than 66% of the world’s energy and generate more than 70% of global greenhouse gas emissions. With the world’s population expected to reach 10 billion by 2100, nearly 90% of whom will live in urban areas, a critical question for planetary sustainability is how the size of cities affects energy use and carbon dioxide (CO2) emissions. Are larger cities more energy and emissions efficient than smaller ones? Do larger cities exhibit gains from economies of scale with regard to emissions? Here we examine the relationship between city size and CO2 emissions for U.S. metropolitan areas using a production accounting allocation of emissions. We find that for the time period of 1999–2008, CO2 emissions scale proportionally with urban population size. Contrary to theoretical expectations, larger cities are not more emissions efficient than smaller ones

Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya

Beyond technology : demand-side solutions for climate change mitigation

The assessment literature on climate change solutions to date has emphasized technologies and options based on cost-effectiveness analysis. However, many solutions to climate change mitigation misalign with such analytical frameworks. Here, we examine demand-side solutions, a crucial class of mitigation options that go beyond technological specification and costbenefit analysis. To do so, we synthesize demand-side mitigation options in the urban, building, transport, and agricultural sectors. We also highlight the specific nature of demand-side solutions in the context of development. We then discuss key analytical considerations to integrate demand-side options into overarching assessments on mitigation. Such a framework would include infrastructure solutions that interact with endogenous preference formation. Both hard infrastructures, such as the built environment, and soft infrastructures, such as habits and norms, shape behavior and as a consequence offer significant potential for reducing overall energy demand and greenhouse gas emissions. We conclude that systemic infrastructural and behavioral change will likely be a necessary component of a transition to a low-carbon society

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km(2), nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr(−1)), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses

Multi-hazard susceptibility and exposure assessment of the Hindu Kush Himalaya

Mountainous regions are highly hazardous, and these hazards often lead to loss of human life. The Hindu Kush Himalaya (HKH), like many mountainous regions, is the site of multiple and overlapping natural hazards, but the distribution of multi-hazard risk and the populations exposed to it are poorly understood. Here, we present high-resolution transboundary models describing susceptibility to floods, landslides, and wildfires to understand population exposure to multi-hazard risk across the HKH. These models are created from historical remotely sensed data and hazard catalogs by the maximum entropy (Maxent) machine learning technique. Our results show that human settlements in the HKH are disproportionately concentrated in areas of high multi-hazard risk. In contrast, low-hazard areas are disproportionately unpopulated. Nearly half of the population in the region lives in areas that are highly susceptible to more than one hazard. Warm low-altitude foothill areas with peren- nially moist soils were identified as highly susceptible to multiple hazards. This area comprises only 31% of the study region, but is home to 49% of its population. The results also show that areas susceptible to multiple hazards are also major corridors of current migration and urban expansion, suggesting that current rates and patterns of urbanization will continue to put more people at risk. This study establishes that the population in the HKH is concentrated in areas susceptible to multiple hazards and suggests that current patterns of human movement will continue to increase exposure to multi-hazards in the HKH

Conceptualizing Distal Drivers in Land Use Competition

This introductory chapter explores the notion of ‘distal drivers’ in land use competition. Research has moved beyond proximate causes of land cover and land use change to focus on the underlying drivers of these dynamics. We discuss the framework of telecoupling within human–environment systems as a first step to come to terms with the increasingly distal nature of driving forces behind land use practices. We then expand the notion of distal as mainly a measure of Euclidian space to include temporal, social, and institutional dimensions. This understanding of distal widens our analytical scope for the analysis of land use competition as a distributed process to consider the role of knowledge and power, technology, and different temporalities within a relational or systemic analysis of practices of land use competition. We conclude by pointing toward the historical and social contingency of land use competition and by acknowledging that this contingency requires a methodological–analytical approach to dynamics that goes beyond linear cause–effect relationships. A critical component of future research will be a better understanding of different types of feedback processes reaching from biophysical feedback loops to feedback produced by individual or institutional reflexivity

Patient Contact Recall after SARS Exposure

We reinterviewed healthcare workers who had been exposed to a patient with severe acute respiratory syndrome (SARS) in an intensive care unit to evaluate the effect of time on recall reliability and willingness to report contact activities and infection control precautions. Healthcare workers reliably recalled events 6 months after exposure