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      No COVID-19 climate silver lining in the US power sector

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          Abstract

          Recent studies conclude that the global coronavirus (COVID-19) pandemic decreased power sector CO 2 emissions globally and in the United States. In this paper, we analyze the statistical significance of CO 2 emissions reductions in the U.S. power sector from March through December 2020. We use Gaussian process (GP) regression to assess whether CO 2 emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic and adjusting for weather, seasonality, and recent emissions trends. We find that monthly CO 2 emissions reductions are only statistically significant in April and May 2020 considering hypothesis tests at 5% significance levels. Separately, we consider the potential impact of COVID-19 on coal-fired power plant retirements through 2022. We find that only a small percentage of U.S. coal power plants are at risk of retirement due to a possible COVID-19-related sustained reduction in electricity demand and prices. We observe and anticipate a return to pre-COVID-19 CO 2 emissions in the U.S. power sector.

          Abstract

          COVID-19 has decreased power sector emissions globally and in the United States. Here the authors assess whether such reductions would have occurred in the United States in the absence of the pandemic, as well as the potential impact of COVID-19 on coal-fired power plant retirements through 2022.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            A Limited Memory Algorithm for Bound Constrained Optimization

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                Author and article information

                Contributors
                max@highland.energy
                leesj@mit.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 August 2021
                3 August 2021
                2021
                : 12
                : 4675
                Affiliations
                [1 ]Highland Energy Analytics, Boston, MA USA
                [2 ]GRID grid.411639.8, ISNI 0000 0001 0571 5193, Department of Computer Science and Engineering, Manipal Institute of Technology, ; Manipal, Karnataka India
                [3 ]GRID grid.147455.6, ISNI 0000 0001 2097 0344, Department of Engineering and Public Policy, Carnegie Mellon University, ; Pittsburgh, PA USA
                [4 ]GRID grid.411639.8, ISNI 0000 0001 0571 5193, Department of Aeronautical and Automobile Engineering, Manipal Institute of Technology, ; Manipal, Karnataka India
                [5 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, ; Cambridge, MA USA
                Author information
                http://orcid.org/0000-0002-5949-8136
                http://orcid.org/0000-0003-4839-7644
                http://orcid.org/0000-0001-7562-7623
                Article
                24959
                10.1038/s41467-021-24959-z
                8333368
                34344875
                cb4589cb-241d-48a7-a225-41746d006b48
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 August 2020
                : 29 June 2021
                Categories
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                © The Author(s) 2021

                Uncategorized
                energy and society,energy economics,energy policy
                Uncategorized
                energy and society, energy economics, energy policy

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