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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ESSDD</journal-id>
<journal-title-group>
<journal-title>Earth System Science Data Discussions</journal-title>
<abbrev-journal-title abbrev-type="publisher">ESSDD</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Earth Syst. Sci. Data Discuss.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1866-3591</issn>
<publisher><publisher-name></publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/essd-2026-243</article-id>
<title-group>
<article-title>Global consumption-based deforestation carbon emissions and regional hotspots: 2001&amp;ndash;2020</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Tang</surname>
<given-names>Dongmei</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>Yao</surname>
<given-names>Yuanzhi</given-names>
<ext-link>https://orcid.org/0000-0003-2387-4598</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>Li</surname>
<given-names>Rui</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>Li</surname>
<given-names>Xia</given-names>
<ext-link>https://orcid.org/0000-0002-3052-1259</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Geographic Sciences, and Key Lab of Geographic Information Science (Ministry of Education), East China  Normal University, Shanghai 200241, PR China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>08</day>
<month>05</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>20</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Dongmei Tang et al.</copyright-statement>
<copyright-year>2026</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://essd.copernicus.org/preprints/essd-2026-243/">This article is available from https://essd.copernicus.org/preprints/essd-2026-243/</self-uri>
<self-uri xlink:href="https://essd.copernicus.org/preprints/essd-2026-243/essd-2026-243.pdf">The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-243/essd-2026-243.pdf</self-uri>
<abstract>
<p>The consumption-based carbon emissions dataset for deforestation provides an important perspective for developing effective emission mitigation policies. However, existing datasets are largely limited to national-scale and lack spatially explicit, high-resolution data at the global level. Here, we present a global, gridded dataset of consumption-based deforestation carbon emissions at 1 km resolution for 2001&amp;ndash;2020. The dataset integrates spatial information on road networks, deforestation, and forest carbon fluxes with country-level global trade statistics. Our dataset shows that trade-related deforestation emissions amount to 39.6 Gt CO&lt;sub&gt;2&lt;/sub&gt;e, constituting 31.8 % of global deforestation emissions. This new dataset fills a critical gap in spatially explicit consumption-based deforestation emissions data, and supports the development of targeted mitigation strategies from the consumer perspective. It also provides a valuable input for climate and carbon cycle models to assess the contribution of consumption-driven deforestation to global warming. Datasets are available at &lt;a href=&quot;https://doi.org/10.6084/m9.figshare.28091879&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;https://doi.org/10.6084/m9.figshare.28091879&lt;/a&gt; (Tang et al., 2024).</p>
</abstract>
<counts><page-count count="20"/></counts>
<funding-group>
<award-group id="gs1">
<funding-source>National Natural Science Foundation of China</funding-source>
<award-id>Grant No. 42130107</award-id>
</award-group>
<award-group id="gs2">
<funding-source>National Natural Science Foundation of China</funding-source>
<award-id>Grant No. 42371410</award-id>
</award-group>
<award-group id="gs3">
<funding-source>National Natural Science Foundation of China</funding-source>
<award-id>Grant No. 42501514</award-id>
</award-group>
</funding-group>
</article-meta>
</front>
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