Global Catastrophes and Trends

Smil, Vaclav

doi:10.7551/mitpress/7582.001.0001

Cited by 62 publications

(9 citation statements)

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“…‘We do not know…the future rates of fossil fuel combustion, land use changes, fertilizer use, and meat production. They will depend on the continuing increases of energy use, the extent of discoveries of new hydrocarbon deposits, the rates of penetration of nonfossil energy conversions, national land use policies, disposable income, and the overall vitality of the global economy’ (see Ref 29, p. 178). Thus, the statistics of the atmosphere can also not be predicted.…”

Section: Common Usages Of Oceanic Atmospheric and Climate Modelsmentioning

confidence: 99%

Constructing climate knowledge with computer models

Müller

2010

WIREs Climate Change

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Computer models are powerful tools that allow us to analyze problems in unprecedented detail and to conduct experiments impossible with the real system. Reliance on computer models in science and policy decisions has been challenged by philosophers of science on methodological and epistemological grounds. This challenge is examined for the case of climate models by reviewing what they are and what climate scientists do with them, followed by an analysis of how they can be used to construct new trustworthy knowledge. A climate model is an executable computer code that solves a set of mathematical equations assumed to represent the climate system. Climate modelers use these models to simulate present and past climates and forecast likely and plausible future evolutions. Model uncertainties and model calibration are identified as the two major concerns. Climate models of different complexity address different question. Their interplay helps to weed out model errors, identify robust features, understand the climate system, and build confidence in the models, but is no guard against flaws in the underlying physics.

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Section: Common Usages Of Oceanic Atmospheric and Climate Modelsmentioning

confidence: 99%

Constructing climate knowledge with computer models

Müller

2010

WIREs Climate Change

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“…1. See Rees (2003), Jacobs (2004), Diamond (2005), McGuire (2006), Kolbert (2007), Campbell (2008), Smil (2008), Orr (2009), Friedman (2009), Hamilton (2010), Klein (2014), and Foster (2015). 2.…”

Section: Notesmentioning

confidence: 99%

Mediating climate politics

Dayrell

Urry

2015

European Journal of Social Theory

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This article examines the centrality of Brazil within the future of climate policy and politics. The state of the carbon sink of the Amazon rainforest has long been an iconic marker of the condition of the Earth. Brazil has been innovative in developing many non-carbon forms of energy generation and use and it has played a major role in international debates on global warming since the Rio Earth Summit in 1992. We examine various ways in which climate change has come to be centrally important in Brazilian public opinion. Survey evidence shows that Brazilians are the most concerned about issues of climate change – with less climate change scepticism as compared with more ‘advanced’ societies. Through using techniques of corpus linguistics we examine how Brazilian media has engendered and stabilized such a high and striking level of climate change concern. We show that the media helped to fix a ‘climate change framing’ of recent often strange weather. The article analyses the newly constructed Brazilian Corpus on Climate Change, presenting data on a scale and reach that is unique in this area of research.

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“…One starting point for deciding whether or not a risk is acceptable is comparison. There is no generally agreed metric for comparing risks, but Smil [9], in his book Global catastrophes and trends, offers one option: mortality per person per hour of exposure. Imperfect though it is (for instance, it does not include morbidity), Smil's metric reveals some interesting comparative figures.…”

Section: Comparing Risksmentioning

confidence: 99%

Risk, uncertainty and regulation

Krebs

2011

Phil. Trans. R. Soc. A.

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This paper reviews the relationship between scientific evidence, uncertainty, risk and regulation. Risk has many different meanings. Furthermore, if risk is defined as the likelihood of an event happening multiplied by its impact, subjective perceptions of risk often diverge from the objective assessment. Scientific evidence may be ambiguous. Scientific experts are called upon to assess risks, but there is often uncertainty in their assessment, or disagreement about the magnitude of the risk. The translation of risk assessments into policy is a political judgement that includes consideration of the acceptability of the risk and the costs and benefits of legislation to reduce the risk. These general points are illustrated with reference to three examples: regulation of risk from pesticides, control of bovine tuberculosis and pricing of alcohol as a means to discourage excessive drinking.

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Global Catastrophes and Trends

Cited by 62 publications

References 0 publications

Constructing climate knowledge with computer models

Constructing climate knowledge with computer models

Mediating climate politics

Risk, uncertainty and regulation

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