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Blake Francis
University of Maryland, Baltimore County
  1.  33
    In Defense of National Climate Change Responsibility: A Reply to the Fairness Objection.Blake Francis - 2021 - Philosophy and Public Affairs 49 (2):115-155.
  2.  25
    Climate Change Injustice.Blake Francis - 2022 - Environmental Ethics 44 (1):5-24.
    Many climate change ethicists argue wealthy nations have duties of justice to combat climate change. However, Posner and Weisbach disagree because there is a poor fit between the principles of justice and the problem of climate change. I argue in this paper that Posner and Weisbach’s argument relies on what Judith Shklar calls “the normal model of justice,” the view that injustice results when principles are violated. Applying Shklar’s critique of normal justice, I argue that Posner and Weisbach’s argument limits (...)
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  3. Moral Asymmetries and Economic Evaluations of Climate Change: The Challenge of Assessing Diverse Effects.Blake Francis - 2017 - In Duncan Purves, Säde Hormio & Adrian Walsh (eds.), The Ethical Underpinnings of Climate Economics. New York: pp. 141-162.
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  4.  8
    Debating Science: Deliberation, Values, and the Common Good.Dane Scott & Blake Francis (eds.) - 2011 - Prometheus Books.
    Scholars and experts focus on the larger moral context around the controversies over scientific research and technological innovations with accessible essays, original to this volume, which emphasize ethical deliberation rather than adversarial debate.
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  5.  10
    Deepening transparency about value-laden assumptions in energy and environmental modelling: improving best practices for both modellers and non-modellers.Mark Budolfson, John Bistline & Blake Francis - 2020 - Climate Policy 20.
    Transparency and openness are broadly endorsed in energy and environmental modelling and analysis, but too little attention is given to the transparency of value-laden assumptions. Current practices for transparency focus on making model source code and data available, documenting key equations and parameter values, and ensuring replicability of results. We argue that, even when followed, these guidelines are insufficient for achieving deep transparency, in the sense that results often remain driven by implicit value-laden assumptions that are opaque to other modellers (...)
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