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          <family>Lupton</family>
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          <family>Vagg</family>
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    <title>Dataset and code for: Expanding scenario diversity in prospective LCA: Coupling the TIAM-UCL integrated assessment model with Premise and ecoinvent</title>
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    <abstract>This dataset contains TIAM-UCL scenario and mapping files designed for use with Premise, a Python-based tool for prospective life cycle assessment (LCA). TIAM-UCL is an integrated assessment model (IAM) that projects future scenarios for energy systems and their environmental impacts. The dataset includes four climate change mitigation scenarios, ranging from limiting global warming to 1.5°C to 3.0°C, across 16 global regions. These scenarios cover key sectors such as electricity, fuels, and steel, projecting production volumes, technology mixes, and efficiencies. Examples include the phase-out of fossil fuels and the increased adoption of renewable energies. While primarily developed for LCA applications within Premise, these data can be utilized in other contexts as well. The dataset, code, and additional figures also serve as supplementary information 1-4 for the associated paper.</abstract>
    <date>2025-01-02</date>
    <publisher>University of Bath</publisher>
    <full_text_status>public</full_text_status>
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        <funder_id>https://doi.org/10.13039/501100000266</funder_id>
        <grant_id>EP/S023364/1</grant_id>
        <project_name>EPSRC Centre for Doctoral Training in Advanced Automotive Propulsion Systems</project_name>
      </item>
      <item>
        <funder_name>Engineering and Physical Sciences Research Council</funder_name>
        <funder_id>https://doi.org/10.13039/501100000266</funder_id>
        <grant_id>EP/S029575/1</grant_id>
        <project_name>UK Energy Research Centre Phase 4 - Theme 7</project_name>
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    <research_centres>
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      <item>inst_sust</item>
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    <collection_method>The data was collected directly from the TIAM-UCL model at University College London as several Excel-based outputs. The data defined multiple technology variables across various sectors, regions, and year periods.</collection_method>
    <provenance>These outputs were combined and processed using Python to fulfil the mapping formatting of the intended Premise software and obtain the final files published in this data set.</provenance>
    <techinfo>The data can be viewed and used with Microsoft Excel. If needed, raw, unprocessed data can be obtained directly from TIAM-UCL by contacting the relevant authors.</techinfo>
    <language>en</language>
    <version>1</version>
    <doi>10.15125/BATH-01431</doi>
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        <link>https://doi.org/10.1016/j.rser.2024.115298</link>
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