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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ World Data Center fo...arrow_drop_down
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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ World Data Center fo...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ World Data Center fo...arrow_drop_down
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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Mohlakoana, N;

    ‘Productive Uses of Energy and gender in the Street Food Sector’, is a title of our four year project which is part of the DFID funded ENERGIA Gender and Energy Research programme. This research focuses on male and female owned micro enterprises preparing and selling food in Rwanda, Senegal and South Africa. This sector provides livelihoods for many women and men in these countries and this project provides the gender and energy nexus analysis. One of the primary goals of this project is to influence energy policy making and implementation in the focus countries.

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    B2FIND
    Dataset . 2018
    Data sources: B2FIND
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    EASY
    Dataset . 2018
    Data sources: EASY
    EASY
    Dataset . 2018
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      B2FIND
      Dataset . 2018
      Data sources: B2FIND
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      EASY
      Dataset . 2018
      Data sources: EASY
      EASY
      Dataset . 2018
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sepehr Eslami; Jannis M. Hoch; Edwin H. Sutanudjaja; Hal E. Voepel;

    Projections of Sea Level Rise (SLR) under RCP 4.5 and RCP 8.5 (AR5) along the Mekong Coast, Published1 by the Ministry of Natural Resources and Environment (MONRE), Hanoi, Vietnam. Projections of Mekong River discharge during the dry season under RCP 4.5 and RCP 8.5 at Kratie, Cambodia. The data contains the cumulative, minimum and maximum dry season (January-1st to April-30th) discharge from 5 different climate models. PCR-GLOBWB2 was run at 5 arc-min spatial resolution and forced with the data based on output from five ISIMIP CMIP5 global climate models (HadGEM2-ES, GFDL-ESM2, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M). 1. Ministry of Natural Resources and Environment (MONRE), V. Climate change and sea level rise scenarios for Vietnam, Ministry of Natural Resources and Environment. (2016). 2. Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018). {"references": ["Sutanudjaja et al. (2018)", "Ministry of Natural Resources and Environment (2016)"]}

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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    downloaddownloads75
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Liu, Jia; Van Kooten, G. Cornelis; Pitt, Lawrence; Liu, Jia; +2 Authors

    As a renewable energy source, wind power is gaining popularity as a favoured alternative to fossil fuel, nuclear and hydro power generation. In Europe, countries are required to achieve 15% of their energy consumption from wind by 2010 as the EU strives to meet its Kyoto obligations. Wind power is considered to be environmentally friendly and low cost. While environmental friendliness has come under scrutiny because wind turbines continue to pose a hazard to birds, are visually unappealing, affect the uses of land and change air flows, the purpose of this paper is to examine the question of its presumed low cost and effectiveness at reducing CO2 emissions by replacing power generated from fossil fuels. To do so, we develop a mathematical programming model of an electrical energy grid that employs power generated by a base-load nuclear power plant, a coal-fired power plant and a gas facility, with the latter used primarily to meet peak-load demand. We then introduce varying levels of wind power generating capacity into the grid. The results indicate that, at low levels of penetration, wind power can provide CO2 mitigation benefits at low cost. However, as the degree of penetrability increases, the costs of reducing CO2 emissions rise rapidly because of the spinning reserves required in the coal- and gas-fired power plants. Fossil fuels are consumed even though no power is generated in the eventuality that wind power is suddenly unavailable. The whimsical nature of wind energy makes it a less than desirable long-term source of energy.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Wageningen Staff Pub...arrow_drop_down
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    https://dx.doi.org/10.22004/ag...
    Other literature type . 2005
    Data sources: Datacite
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      https://dx.doi.org/10.22004/ag...
      Other literature type . 2005
      Data sources: Datacite
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    Authors: Lesschen, J.P.; Elbersen, H.W.; Poppens, R.; Galytska, M.; +2 Authors

    Biomass production has both direct effects and indirect effects. Direct effects such as the energy balance and GHG balance can be directly measured, to make sure that impacts are (significantly) below the fossil fuel comparator. In recent years it has also been recognized that the production and use of biomass for energy has indirect effects which are caused by competition for inputs and land. The most important indirect effect is ILUC (indirect land use change) and the associated GHG emissions, which have been quantified in different studies. Avoiding ILUC is now becoming important. An important option is the use of land that would otherwise not be used for food or feed production. This generally means that lower quality or marginal land will be used. Switchgrass is one of the main perennial biomass crops that can produce high biomass yields under low input conditions and which can be established at low cost by seeds. In Ukraine this crop has in recent years been tested, yielding information that can be used to assess the cost and GHG balance of growing the crop, pelletizing, transport to the Netherlands and conversion into electricity. Results show that GHG emissions on low quality soil without ILUC (12.5 g CO2 MJ-1 pellet) are higher than for good quality soil grown switchgrass with ILUC (0.1 g CO2 MJ-1 pellet). Analysis of the costs of growing switchgrass on low productive soils are 22% higher compared to high quality soils. We conclude that ILUC avoidance needs to be quantified and rewarded. Proceedings of the 20th European Biomass Conference and Exhibition, 18-22 June 2012, Milan, Italy, pp. 1988-1991

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    https://dx.doi.org/10.5071/20t...
    Conference object . 2012
    Data sources: Datacite
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      https://dx.doi.org/10.5071/20t...
      Conference object . 2012
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    Authors: Meesters, K.P.H.; Abelha, P.; Kroon, P.; Saric, M.; +3 Authors

    Large quantities of biomass will be needed to feed the biobased economy. Use of crops and wood may cause (indirect) land use change related greenhouse gas emissions. Agro-residues could be an interesting alternative. However, several issues are hindering efficient application: high potassium and chlorine content and low bulk density are the most important issues. In this research, a series of processes is proposed to overcome these issues. Through a combination of extraction (to remove potassium and chlorine), steam treatment and pelleting, Clean Agro-Pellet Commodities(CAPCOMs) were produced. The pellets showed improved handling properties. Combustion tests showed improved ash melting behavior, reduced fouling of heat exchangers and low emissions of NOx and fines. Fermentation tests showed that pellets produced at low severity factors were easily hydrolized and fermented to produce ethanol at normal yields. Some inhibition was seen with undiluted hydrolysates. Based on the results a techno-economical evaluation showed that pellets from agro-residues could be produced and transported at a cost of around 6 EURO/GJHHV. Sustainability analysis revealed that pellets could be produced with GHG emissions of 3 to 6.4 kgCO2eq/GJLHV. Via the combination of processes described in this paper, a huge potential of nowadays unused biomass can be made applicable for the bioeconomy. Proceedings of the 29th European Biomass Conference and Exhibition, 26-29 April 2021, Online, pp. 791-794

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    https://dx.doi.org/10.5071/29t...
    Conference object . 2021
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  • Authors: Pieringer, Maximilian;

    The aim of this work was to gain insight and knowledge with the basic concepts of the Bitcoinnetwork and its relation to energy consumption. Precisely, the ambition of this literature-based research was to identify key determinants of the network ́s energy intensiveness. After an extensive review of the relevant literature on the topic, key principles of Bitcoin ́s electricity consumption were derived.With regards to environmental sustainability concerns, it was found that the network ́sproperties, in theory, allow for improvements of the unit economics of renewable energy production facilities and renewable intensive energy grids. Such applications, however, are dependent on the unforecastable market dynamics of the Bitcoin price.In a contextualization approach it was attempted to categorize the electricity consumption levels of the Bitcoin network based on the services offered with similar but not comparable entities.This approach finds that energy consumption does not provide a conclusive and instructive comparative parameter to determine whether energy consumption levels of the Bitcoin network.The paper concludes with advocating for location dependent policy approaches that encourage the strategic deployment of Bitcoin mining hardware to minimize environmental and economic opportunity cost.

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    Authors: de Wit, S.I. (author); Bobbink, I. (author);

    Landscape architectonic compositions that draw on the underlying landscape structure can function as a carrier for changing programmes, cultures, processes, etc. Precisely such an explicitly spatial design is required to foster the inclusive city, one that is not only socially just but also sensitive to the environment while allowing for and evoking diverse social and natural processes. The objective of an ‘inclusive city’ is often related to social issues, which might easily lead to the exclusion of ecological values; the opposite approach may prove equally exclusive. Inclusivity also means creating room for the unexpected. From a design point of view, this requires two underlying attitudes: a willingness to see any design assignment from different perspectives as well as a readiness to create sustainable, flexible and open designs. These two attitudes are inherent to landscape architecture, which traditionally prioritizes the site over the programme, and—because of the long term, time-based condition of the landscape—is forced to think in open-ended designs. In this paper we discuss a selection of graduation projects of the landscape architecture track at the TU Delft in order to illustrate how inclusivity is inherent to a complete understanding of landscape architecture. Four essential perspectives on analysis and design—perception, palimpsest, process and scale continuum—are discussed in order to reveal their capacity to serve as a basis for designing inclusive urban landscapes. Research in Urbanism Series, Vol. 6 (2020): Inclusive Urbanism: Advances in research, education and practice

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    https://dx.doi.org/10.7480/riu...
    Article . 2020
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    Research in Urbanism Series
    Article . 2020
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      Research in Urbanism Series
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mathisen, T.A. (author); Annema, J.A. (author); Kroesen, M. (author);

    Weather is identified as one of many factors that influence the demand for cycling. Weather patterns will change due to expected climate change. The aim of this article is to study the extent to which climate change influences the cycling frequency. The analysis in this article is conducted using an econometric model based on data spanning over four years on weather indicators and the cycling frequency in the Norwegian city of Bodø, which is located north of the Arctic Circle. According to the projections for climate change, both temperature and quantity of precipitation are expected to increase in this area during the next century. An important consequence of changes in the climate in the studied region is the reduced duration of what can be characterised as the winter season. However, this consequence is highly uncertain. When using Norway’s middle projections for climate change by 2050, the analysis shows a moderate increase in cycling frequency of 6.2%. For the reduced winter period, the cycle rate might be two and three times higher in 2050 compared to the current level. Both estimates assume that every other potential impact on cycling rates remain equal.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
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    Data sources: Datacite
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      World Data Center for Climate
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
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      World Data Center for Climate
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    Authors: Mohlakoana, N;

    ‘Productive Uses of Energy and gender in the Street Food Sector’, is a title of our four year project which is part of the DFID funded ENERGIA Gender and Energy Research programme. This research focuses on male and female owned micro enterprises preparing and selling food in Rwanda, Senegal and South Africa. This sector provides livelihoods for many women and men in these countries and this project provides the gender and energy nexus analysis. One of the primary goals of this project is to influence energy policy making and implementation in the focus countries.

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    B2FIND
    Dataset . 2018
    Data sources: B2FIND
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    EASY
    Dataset . 2018
    Data sources: EASY
    EASY
    Dataset . 2018
    Data sources: Datacite
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      B2FIND
      Dataset . 2018
      Data sources: B2FIND
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      EASY
      Dataset . 2018
      Data sources: EASY
      EASY
      Dataset . 2018
      Data sources: Datacite
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    Authors: Sepehr Eslami; Jannis M. Hoch; Edwin H. Sutanudjaja; Hal E. Voepel;

    Projections of Sea Level Rise (SLR) under RCP 4.5 and RCP 8.5 (AR5) along the Mekong Coast, Published1 by the Ministry of Natural Resources and Environment (MONRE), Hanoi, Vietnam. Projections of Mekong River discharge during the dry season under RCP 4.5 and RCP 8.5 at Kratie, Cambodia. The data contains the cumulative, minimum and maximum dry season (January-1st to April-30th) discharge from 5 different climate models. PCR-GLOBWB2 was run at 5 arc-min spatial resolution and forced with the data based on output from five ISIMIP CMIP5 global climate models (HadGEM2-ES, GFDL-ESM2, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M). 1. Ministry of Natural Resources and Environment (MONRE), V. Climate change and sea level rise scenarios for Vietnam, Ministry of Natural Resources and Environment. (2016). 2. Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018). {"references": ["Sutanudjaja et al. (2018)", "Ministry of Natural Resources and Environment (2016)"]}

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Liu, Jia; Van Kooten, G. Cornelis; Pitt, Lawrence; Liu, Jia; +2 Authors

    As a renewable energy source, wind power is gaining popularity as a favoured alternative to fossil fuel, nuclear and hydro power generation. In Europe, countries are required to achieve 15% of their energy consumption from wind by 2010 as the EU strives to meet its Kyoto obligations. Wind power is considered to be environmentally friendly and low cost. While environmental friendliness has come under scrutiny because wind turbines continue to pose a hazard to birds, are visually unappealing, affect the uses of land and change air flows, the purpose of this paper is to examine the question of its presumed low cost and effectiveness at reducing CO2 emissions by replacing power generated from fossil fuels. To do so, we develop a mathematical programming model of an electrical energy grid that employs power generated by a base-load nuclear power plant, a coal-fired power plant and a gas facility, with the latter used primarily to meet peak-load demand. We then introduce varying levels of wind power generating capacity into the grid. The results indicate that, at low levels of penetration, wind power can provide CO2 mitigation benefits at low cost. However, as the degree of penetrability increases, the costs of reducing CO2 emissions rise rapidly because of the spinning reserves required in the coal- and gas-fired power plants. Fossil fuels are consumed even though no power is generated in the eventuality that wind power is suddenly unavailable. The whimsical nature of wind energy makes it a less than desirable long-term source of energy.

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    https://dx.doi.org/10.22004/ag...
    Other literature type . 2005
    Data sources: Datacite
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      https://dx.doi.org/10.22004/ag...
      Other literature type . 2005
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    Authors: Lesschen, J.P.; Elbersen, H.W.; Poppens, R.; Galytska, M.; +2 Authors

    Biomass production has both direct effects and indirect effects. Direct effects such as the energy balance and GHG balance can be directly measured, to make sure that impacts are (significantly) below the fossil fuel comparator. In recent years it has also been recognized that the production and use of biomass for energy has indirect effects which are caused by competition for inputs and land. The most important indirect effect is ILUC (indirect land use change) and the associated GHG emissions, which have been quantified in different studies. Avoiding ILUC is now becoming important. An important option is the use of land that would otherwise not be used for food or feed production. This generally means that lower quality or marginal land will be used. Switchgrass is one of the main perennial biomass crops that can produce high biomass yields under low input conditions and which can be established at low cost by seeds. In Ukraine this crop has in recent years been tested, yielding information that can be used to assess the cost and GHG balance of growing the crop, pelletizing, transport to the Netherlands and conversion into electricity. Results show that GHG emissions on low quality soil without ILUC (12.5 g CO2 MJ-1 pellet) are higher than for good quality soil grown switchgrass with ILUC (0.1 g CO2 MJ-1 pellet). Analysis of the costs of growing switchgrass on low productive soils are 22% higher compared to high quality soils. We conclude that ILUC avoidance needs to be quantified and rewarded. Proceedings of the 20th European Biomass Conference and Exhibition, 18-22 June 2012, Milan, Italy, pp. 1988-1991

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    https://dx.doi.org/10.5071/20t...
    Conference object . 2012
    Data sources: Datacite
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      https://dx.doi.org/10.5071/20t...
      Conference object . 2012
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    Authors: Meesters, K.P.H.; Abelha, P.; Kroon, P.; Saric, M.; +3 Authors

    Large quantities of biomass will be needed to feed the biobased economy. Use of crops and wood may cause (indirect) land use change related greenhouse gas emissions. Agro-residues could be an interesting alternative. However, several issues are hindering efficient application: high potassium and chlorine content and low bulk density are the most important issues. In this research, a series of processes is proposed to overcome these issues. Through a combination of extraction (to remove potassium and chlorine), steam treatment and pelleting, Clean Agro-Pellet Commodities(CAPCOMs) were produced. The pellets showed improved handling properties. Combustion tests showed improved ash melting behavior, reduced fouling of heat exchangers and low emissions of NOx and fines. Fermentation tests showed that pellets produced at low severity factors were easily hydrolized and fermented to produce ethanol at normal yields. Some inhibition was seen with undiluted hydrolysates. Based on the results a techno-economical evaluation showed that pellets from agro-residues could be produced and transported at a cost of around 6 EURO/GJHHV. Sustainability analysis revealed that pellets could be produced with GHG emissions of 3 to 6.4 kgCO2eq/GJLHV. Via the combination of processes described in this paper, a huge potential of nowadays unused biomass can be made applicable for the bioeconomy. Proceedings of the 29th European Biomass Conference and Exhibition, 26-29 April 2021, Online, pp. 791-794

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    https://dx.doi.org/10.5071/29t...
    Conference object . 2021
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      https://dx.doi.org/10.5071/29t...
      Conference object . 2021
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  • Authors: Pieringer, Maximilian;

    The aim of this work was to gain insight and knowledge with the basic concepts of the Bitcoinnetwork and its relation to energy consumption. Precisely, the ambition of this literature-based research was to identify key determinants of the network ́s energy intensiveness. After an extensive review of the relevant literature on the topic, key principles of Bitcoin ́s electricity consumption were derived.With regards to environmental sustainability concerns, it was found that the network ́sproperties, in theory, allow for improvements of the unit economics of renewable energy production facilities and renewable intensive energy grids. Such applications, however, are dependent on the unforecastable market dynamics of the Bitcoin price.In a contextualization approach it was attempted to categorize the electricity consumption levels of the Bitcoin network based on the services offered with similar but not comparable entities.This approach finds that energy consumption does not provide a conclusive and instructive comparative parameter to determine whether energy consumption levels of the Bitcoin network.The paper concludes with advocating for location dependent policy approaches that encourage the strategic deployment of Bitcoin mining hardware to minimize environmental and economic opportunity cost.

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    Authors: de Wit, S.I. (author); Bobbink, I. (author);

    Landscape architectonic compositions that draw on the underlying landscape structure can function as a carrier for changing programmes, cultures, processes, etc. Precisely such an explicitly spatial design is required to foster the inclusive city, one that is not only socially just but also sensitive to the environment while allowing for and evoking diverse social and natural processes. The objective of an ‘inclusive city’ is often related to social issues, which might easily lead to the exclusion of ecological values; the opposite approach may prove equally exclusive. Inclusivity also means creating room for the unexpected. From a design point of view, this requires two underlying attitudes: a willingness to see any design assignment from different perspectives as well as a readiness to create sustainable, flexible and open designs. These two attitudes are inherent to landscape architecture, which traditionally prioritizes the site over the programme, and—because of the long term, time-based condition of the landscape—is forced to think in open-ended designs. In this paper we discuss a selection of graduation projects of the landscape architecture track at the TU Delft in order to illustrate how inclusivity is inherent to a complete understanding of landscape architecture. Four essential perspectives on analysis and design—perception, palimpsest, process and scale continuum—are discussed in order to reveal their capacity to serve as a basis for designing inclusive urban landscapes. Research in Urbanism Series, Vol. 6 (2020): Inclusive Urbanism: Advances in research, education and practice

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    https://dx.doi.org/10.7480/riu...
    Article . 2020
    License: CC BY
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    Research in Urbanism Series
    Article . 2020
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    Authors: Mathisen, T.A. (author); Annema, J.A. (author); Kroesen, M. (author);

    Weather is identified as one of many factors that influence the demand for cycling. Weather patterns will change due to expected climate change. The aim of this article is to study the extent to which climate change influences the cycling frequency. The analysis in this article is conducted using an econometric model based on data spanning over four years on weather indicators and the cycling frequency in the Norwegian city of Bodø, which is located north of the Arctic Circle. According to the projections for climate change, both temperature and quantity of precipitation are expected to increase in this area during the next century. An important consequence of changes in the climate in the studied region is the reduced duration of what can be characterised as the winter season. However, this consequence is highly uncertain. When using Norway’s middle projections for climate change by 2050, the analysis shows a moderate increase in cycling frequency of 6.2%. For the reduced winter period, the cycle rate might be two and three times higher in 2050 compared to the current level. Both estimates assume that every other potential impact on cycling rates remain equal.

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