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  1. Free, publicly-accessible full text available August 30, 2023
  2. Free, publicly-accessible full text available June 1, 2023
  3. Abstract

    We estimate the climate value of offshore wind energy with a highly flexible, forward-looking method that estimates the value in a consistent manner under a range of policies, including carbon caps and taxes. Backward looking methods measure the damages avoided due to emissions reductions attributed to renewable energy under an existing policy structure. Under a carbon cap, however, the climate value of offshore wind energy comes entirely from reducing the cost of meeting the cap. Our method for estimating the prospective climate value compares bothclimate damagesandabatement costsin cases with and without offshore wind energy. This climate value can be compared to the costs of reducing barriers to new technologies, such as streamlining approval processes. The climate value depends on the cost of offshore wind technology, the climate policy under consideration, the severity of damages from climate change, and the discount rate. In the absence of a binding climate policy, the climate value of offshore wind energy ranges from $246 billion to $2.5 trillion under central assumptions about damages and discount rate, and can reach over $30 trillion under certain assumptions (low discount rate, high damages, low technology costs). The value of technical change—of moving from the highest cost tomore »lowest cost assumptions about the technology—is estimated to be $300 billion even under the most unfavorable assumptions, dwarfing worldwide R&D investment inallwind energy technology. Using this method, we find that new low carbon technologies can provide a hedge against uncertainty and error in climate policies.

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  4. Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.