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      Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies

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          Abstract

          A rapid and deep decarbonization of power supply worldwide is required to limit global warming to well below 2 °C. Beyond greenhouse gas emissions, the power sector is also responsible for numerous other environmental impacts. Here we combine scenarios from integrated assessment models with a forward-looking life-cycle assessment to explore how alternative technology choices in power sector decarbonization pathways compare in terms of non-climate environmental impacts at the system level. While all decarbonization pathways yield major environmental co-benefits, we find that the scale of co-benefits as well as profiles of adverse side-effects depend strongly on technology choice. Mitigation scenarios focusing on wind and solar power are more effective in reducing human health impacts compared to those with low renewable energy, while inducing a more pronounced shift away from fossil and toward mineral resource depletion. Conversely, non-climate ecosystem damages are highly uncertain but tend to increase, chiefly due to land requirements for bioenergy.

          Abstract

          There lacks a consistent and holistic evaluation of co-benefits of different mitigation pathways in studies on Integrated Assessment Models. Here the authors quantify environmental co-benefits and adverse side-effects of a portfolio of alternative power sector decarbonisation pathways and show that the scale of co-benefits as well as profiles of adverse side-effects depend strongly on technology choice.

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          Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems.

          The cycles of the key nutrient elements nitrogen (N) and phosphorus (P) have been massively altered by anthropogenic activities. Thus, it is essential to understand how photosynthetic production across diverse ecosystems is, or is not, limited by N and P. Via a large-scale meta-analysis of experimental enrichments, we show that P limitation is equally strong across these major habitats and that N and P limitation are equivalent within both terrestrial and freshwater systems. Furthermore, simultaneous N and P enrichment produces strongly positive synergistic responses in all three environments. Thus, contrary to some prevailing paradigms, freshwater, marine and terrestrial ecosystems are surprisingly similar in terms of N and P limitation.
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            An Estimate of the Global Burden of Anthropogenic Ozone and Fine Particulate Matter on Premature Human Mortality Using Atmospheric Modeling

            Background Ground-level concentrations of ozone (O3) and fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)] have increased since preindustrial times in urban and rural regions and are associated with cardiovascular and respiratory mortality. Objectives We estimated the global burden of mortality due to O3 and PM2.5 from anthropogenic emissions using global atmospheric chemical transport model simulations of preindustrial and present-day (2000) concentrations to derive exposure estimates. Methods Attributable mortalities were estimated using health impact functions based on long-term relative risk estimates for O3 and PM2.5 from the epidemiology literature. Using simulated concentrations rather than previous methods based on measurements allows the inclusion of rural areas where measurements are often unavailable and avoids making assumptions for background air pollution. Results Anthropogenic O3 was associated with an estimated 0.7 ± 0.3 million respiratory mortalities (6.3 ± 3.0 million years of life lost) annually. Anthropogenic PM2.5 was associated with 3.5 ± 0.9 million cardiopulmonary and 220,000 ± 80,000 lung cancer mortalities (30 ± 7.6 million years of life lost) annually. Mortality estimates were reduced approximately 30% when we assumed low-concentration thresholds of 33.3 ppb for O3 and 5.8 μg/m3 for PM2.5. These estimates were sensitive to concentration thresholds and concentration–mortality relationships, often by > 50%. Conclusions Anthropogenic O3 and PM2.5 contribute substantially to global premature mortality. PM2.5 mortality estimates are about 50% higher than previous measurement-based estimates based on common assumptions, mainly because of methodologic differences. Specifically, we included rural populations, suggesting higher estimates; however, the coarse resolution of the global atmospheric model may underestimate urban PM2.5 exposures.
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              Bioenergy and climate change mitigation: an assessment

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                Author and article information

                Contributors
                luderer@pik-potsdam.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 November 2019
                19 November 2019
                2019
                : 10
                : 5229
                Affiliations
                [1 ]ISNI 0000 0004 0493 9031, GRID grid.4556.2, Potsdam Institute for Climate Impact Research (PIK), ; P.O. Box 60 12 03, 14412 Potsdam, Germany
                [2 ]ISNI 0000 0001 2292 8254, GRID grid.6734.6, Chair of Global Energy Systems, , Technische Universität Berlin, ; Straße des 17. Juni 135, 10623 Berlin, Germany
                [3 ]ISNI 0000 0001 1516 2393, GRID grid.5947.f, Industrial Ecology Programme and Department of Energy and Process Engineering, , Norwegian University of Science and Technology (NTNU), ; Høgskoleringen 5, 7034 Trondheim, Norway
                [4 ]GRID grid.423669.c, Luxembourg Institute of Science and Technology (LIST), ; 41 rue du Brill, L-4422 Belvaux, Luxembourg
                [5 ]ISNI 0000 0001 0616 8355, GRID grid.437426.0, PBL Netherlands Environmental Assessment Agency, ; Bezuidenhoutseweg 30, The Hague, The Netherlands
                [6 ]ISNI 0000 0001 1955 9478, GRID grid.75276.31, International Institute for Applied Systems Analysis (IIASA), ; Schlossplatz 1, 2361 Laxenburg, Austria
                [7 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, Joint Global Change Research Institute, , Pacific Northwest National Laboratory, ; 5825 University Research Court Suite 3500, College Park, MD 20740 USA
                [8 ]ISNI 0000 0004 0623 0923, GRID grid.503302.7, Université Grenoble Alpes, CNRS, INRA, Grenoble INP, GAEL, ; 38000 Grenoble, France
                [9 ]ISNI 0000000120346234, GRID grid.5477.1, Copernicus Institute for Sustainable Development, , Utrecht University, ; Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
                [10 ]ISNI 0000000419368710, GRID grid.47100.32, Center for Industrial Ecology, School of Forestry and Environmental Studies, , Yale University, ; New Haven, CT USA
                Author information
                http://orcid.org/0000-0002-9057-6155
                http://orcid.org/0000-0003-2349-6515
                http://orcid.org/0000-0002-1378-3142
                http://orcid.org/0000-0002-2778-8825
                http://orcid.org/0000-0002-8242-6712
                http://orcid.org/0000-0003-2927-9407
                http://orcid.org/0000-0002-3565-7526
                http://orcid.org/0000-0002-3541-6271
                http://orcid.org/0000-0003-0398-2831
                http://orcid.org/0000-0002-4934-3421
                Article
                13067
                10.1038/s41467-019-13067-8
                6864079
                31745077
                ef52de6b-574e-49c8-87bb-0930556c86cd
                © The Author(s) 2019

                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
                : 7 February 2019
                : 14 October 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);
                Award ID: 730403 (INNOPATHS)
                Award ID: 730403 (INNOPATHS)
                Award ID: 730035 (REINVENT)
                Award ID: 730403 (INNOPATHS)
                Award ID: 730035 (REINVENT)
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100011268, EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change));
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award ID: 308329 (ADVANCE)
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100005416, Norges Forskningsråd (Research Council of Norway);
                Award ID: 209697
                Award Recipient :
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Funded by: EC | EC Seventh Framework Programm | FP7 Environment (FP7-ENVIRONMENT - Specific Programme "Cooperation": Environment (Including Climate Change))
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                climate-change mitigation,environmental impact,energy modelling
                Uncategorized
                climate-change mitigation, environmental impact, energy modelling

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