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      Net-zero emission targets for major emitting countries consistent with the Paris Agreement

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          Abstract

          Over 100 countries have set or are considering net-zero emissions or neutrality targets. However, most of the information on emissions neutrality (such as timing) is provided for the global level. Here, we look at national-level neutrality-years based on globally cost-effective 1.5 °C and 2 °C scenarios from integrated assessment models. These results indicate that domestic net zero greenhouse gas and CO 2 emissions in Brazil and the USA are reached a decade earlier than the global average, and in India and Indonesia later than global average. These results depend on choices like the accounting of land-use emissions. The results also show that carbon storage and afforestation capacity, income, share of non-CO 2 emissions, and transport sector emissions affect the variance in projected phase-out years across countries. We further compare these results to an alternative approach, using equity-based rules to establish target years. These results can inform policymakers on net-zero targets.

          Abstract

          Over 100 countries have set or are considering net-zero emissions targets. Here, the authors show that a country’s potential for negative emissions and methodological issues affect when countries can reach net-zero, calling for clear internationally agreed definitions and accounting methods.

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          Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
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            Health co-benefits from air pollution and mitigation costs of the Paris Agreement: a modelling study

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                Author and article information

                Contributors
                Heleen.vanSoest@pbl.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 April 2021
                9 April 2021
                2021
                : 12
                : 2140
                Affiliations
                [1 ]GRID grid.437426.0, ISNI 0000 0001 0616 8355, PBL Netherlands Environmental Assessment Agency, ; The Hague, The Netherlands
                [2 ]GRID grid.5477.1, ISNI 0000000120346234, Copernicus Institute of Sustainable Development, , Utrecht University, ; Utrecht, The Netherlands
                Author information
                http://orcid.org/0000-0001-5307-5880
                http://orcid.org/0000-0002-5128-8150
                http://orcid.org/0000-0003-0398-2831
                Article
                22294
                10.1038/s41467-021-22294-x
                8035189
                33837206
                a88c4f70-8dbd-43ef-aaf5-94e61fb15258
                © 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
                : 6 May 2020
                : 3 March 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000780, European Commission (EC);
                Award ID: 340201/2017/764007/SER/CLIMA.C1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);
                Award ID: 642147
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                climate-change mitigation,projection and prediction,energy modelling
                Uncategorized
                climate-change mitigation, projection and prediction, energy modelling

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