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<front>
<journal-meta>
<journal-id journal-id-type="publisher">EGUsphere</journal-id>
<journal-title-group>
<journal-title>EGUsphere</journal-title>
<abbrev-journal-title abbrev-type="publisher">EGUsphere</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">EGUsphere</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub"></issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/egusphere-2025-121</article-id>
<title-group>
<article-title>DSCALE v0.1 &amp;ndash; an open-source algorithm for downscaling regional and global mitigation pathways to the country level</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Sferra</surname>
<given-names>Fabio</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>van Ruijven</surname>
<given-names>Bas</given-names>
<ext-link>https://orcid.org/0000-0003-1232-5892</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Riahi</surname>
<given-names>Keywan</given-names>
<ext-link>https://orcid.org/0000-0001-7193-3498</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hackstock</surname>
<given-names>Philip</given-names>
<ext-link>https://orcid.org/0000-0002-1482-1366</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Maczek</surname>
<given-names>Florian</given-names>
<ext-link>https://orcid.org/0000-0003-2194-3609</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Kikstra</surname>
<given-names>Jarmo</given-names>
<ext-link>https://orcid.org/0000-0001-9405-1228</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Haas</surname>
<given-names>Reinhard</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>International Institute for Applied System Analysis (IIASA), Laxenburg, 2361, Austria</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Energy Economics Group, TU Wien, Vienna, 1040, Austria</addr-line>
</aff>
<pub-date pub-type="epub">
<day>21</day>
<month>05</month>
<year>2025</year>
</pub-date>
<volume>2025</volume>
<fpage>1</fpage>
<lpage>57</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2025 Fabio Sferra et al.</copyright-statement>
<copyright-year>2025</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2025/egusphere-2025-121/">This article is available from https://egusphere.copernicus.org/preprints/2025/egusphere-2025-121/</self-uri>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2025/egusphere-2025-121/egusphere-2025-121.pdf">The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2025/egusphere-2025-121/egusphere-2025-121.pdf</self-uri>
<abstract>
<p>Integrated Assessment Models (IAMs) provide low-carbon scenarios at a global scale or for broad economic aggregates, as running these models for every country would be computationally demanding. Lack of national results from IAMs, hinders the enhancement of NDCs (Nationally Determined Contributions) and LTS (Long Term Strategies) in accordance with the 1.5C target and best available science. To address this limitation, we have developed DSCALE (Downscaling Scenarios to the Country level for Assessment of Low carbon Emissions), a novel algorithm designed to downscale regional IAMs outcomes to the country level. In this paper we present the methodology and show results for both current policy and 1.5&amp;deg;C scenarios from the NGFS 2023 release. This downscaling tool provides insights for energy and emission developments and targets at the country level consistent with global scenarios from IAMs. Moreover, this tool facilitates the integration of IAMs results with other models and tools requiring energy and emissions data at the country level, such as the macroeconomic NiGEM model.</p>
</abstract>
<counts><page-count count="57"/></counts>
<funding-group>
<award-group id="gs1">
<funding-source>Horizon 2020</funding-source>
<award-id>821124</award-id>
</award-group>
<award-group id="gs2">
<funding-source>ClimateWorks Foundation</funding-source>
<award-id>20-1540</award-id>
</award-group>
</funding-group>
</article-meta>
</front>
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