Atmospheric pressure as a natural climate regulator for a terrestrial planet with a biosphere

Ecocentrism and Biosphere Life Extension

The biosphere represents the global sum of all ecosystems. According to a prominent view in environmental ethics, ecocentrism, these ecosystems matter for their own sake, and not only because they contribute to human ends. As such, some ecocentrists are critical of the modern industrial civilization, and a few even argue that an irreversible collapse of the modern industrial civilization would be a good thing. However, taking a longer view and considering the eventual destruction of the biosphere by astronomical processes, we argue that humans, a species with considerable technological know-how and industrial capacity could intervene to extend the lifespan of Earth's biosphere, perhaps by several billion years. We argue that human civilization, despite its flaws and harmful impacts on many ecosystems, is the biosphere's best hope of avoiding premature destruction. We argue that proponents of ecocentrism, even those who wholly disregard anthropocentric values, have a strong moral reason preserve the modern industrial civilization, for as long as needed to ensure biosphere survival.

What good are abstract and what-if models? Lessons from the Gaïa hypothesis

This article on the epistemology of computational models stems from an analysis of the Gaïa hypothesis (GH). It begins with James Kirchner's criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities (fictive daisies on an imaginary planet) and trying to answer counterfactual (what-if) questions (how would a planet look like if life had no influence on it?). For these reasons the model has been considered not testable and therefore not legitimate in science, and in any case not very interesting since it explores non actual issues. This criticism implicitly assumes that science should only be involved in the making of models that are "actual" (by opposition to what-if) and "specific" (by opposition to abstract). I challenge both of these criticisms in this article. First by showing that although the testability-understood as the comparison of model output with empirical data-is an important procedure for explanatory models, there are plenty of models that are not testable. The fact that these are not testable (in this restricted sense) has nothing to do with their being "abstract" or "what-if" but with their being predictive models. Secondly, I argue that "abstract" and "what-if" models aim at (respectable) epistemic purposes distinct from those pursued by "actual and specific models". Abstract models are used to propose how-possibly explanation or to pursue theorizing. What-if models are used to attribute causal or explanatory power to a variable of interest. The fact that they aim at different epistemic goals entails that it may not be accurate to consider the choice between different kinds of model as a "strategy".