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Peak oil is the point in time when the maximum rate of global oil production is reached, after which production will begin an irreversible decline. It is related to the distinct concept of oil depletion; while global petroleum reserves are finite, the limiting factor is not whether the oil exists but whether it can be extracted economically at a given price. A secular decline in oil extraction could be caused both by depletion of accessible reserves and by reductions in demand that reduce the price relative to the cost of extraction, as might be induced to reduce carbon emissions. Numerous predictions of the timing of peak oil have been made over the past century before being falsified by subsequent growth in the rate of petroleum extraction. M. King Hubbert is often credited with introducing the notion in a 1956 paper which presented a formal theory and predicted U.S. extraction to peak between 1965 and 1971. Hubbert's original predictions for world peak oil produ

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'Peak Oil' As Classical Economic Process

François Meynard

Forecasts about 'peak oil', the limits of oil production, are the subject of controversies. 'Peak oil' forecasts are based on extrapolations of a priori chosen mathematical models fitted to field data. Scientifically that is not very sound. To clarify this point, in this paper 'peak oil' is modelled in a generic reductionist, approach, in which the conflicting dynamic between economic forces and physical laws are well identified. This model finds a natural interpretation just supress in a classical economic analysis frame. The 'peak oil' controversy is thus brought into the field of economic thought as a debate between classical and mainstream economics, in particular between an objective and a faith based appraisal about this historical event. The goal is not to improve forecasts about 'peak oil', which is very likely not a predictable event, but to propose an objective framework for the analysis of its systemic effects on economic production.

Multi-Science Publ Co Ltd2014

Statistical physics of vaccination

Marcel Salathé, Zhen Wang

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination one of the most important preventive measures of modern times is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research. (C) 2016 Elsevier B.V. All rights reserved.

Elsevier Science Bv2016

Peak Oil & Iran

Mohammadjavad Faraji, Ramtin Pedarsani

2010

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