Review
Version 1
Preserved in Portico This version is not peer-reviewed
Quantifying Greenhouse Gas Emissions in Human Deaths to Guide Energy Policy
Version 1
: Received: 28 July 2023 / Approved: 31 July 2023 / Online: 31 July 2023 (10:54:43 CEST)
A peer-reviewed article of this Preprint also exists.
Pearce, J.M.; Parncutt, R. Quantifying Global Greenhouse Gas Emissions in Human Deaths to Guide Energy Policy. Energies 2023, 16, 6074. Pearce, J.M.; Parncutt, R. Quantifying Global Greenhouse Gas Emissions in Human Deaths to Guide Energy Policy. Energies 2023, 16, 6074.
Abstract
When attempting to quantify future harms caused by carbon emissions and to set appropriate energy policies, it has been argued that the most important metric is the number of human deaths caused by climate change. Several studies have attempted to overcome the uncertainties associated with such forecasting. In this article, approaches to estimating future human deaths tolls from climate change are compared and synthesized, and implications for energy policy are considered. Several studies are consistent with the “1000-ton rule,” according to which a future person is killed every time 1000 tons of fossil carbon are burned (order-of-magnitude estimate). If warming reaches or exceeds 2°C this century, mainly richer humans will be responsible for killing roughly 1 billion mainly poorer humans through anthropogenic global warming. Such mass manslaughter is clearly unacceptable. On this basis, relatively aggressive energy policies are summarized that would enable immediate and substantive decreases to carbon emissions. The limitations to such calculations are outlined and future work is recommended to accelerate the decarbonization of the global economy while minimizing the number of sacrificed human lives.
Keywords
carbon emissions; greenhouse gas emissions; global catastrophic risk; climate change; energy policy; human mortality; climate genocide
Subject
Engineering, Energy and Fuel Technology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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