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<front>
<journal-meta>
<journal-id journal-id-type="publisher">AGILE-GISS</journal-id>
<journal-title-group>
<journal-title>AGILE: GIScience Series</journal-title>
<abbrev-journal-title abbrev-type="publisher">AGILE-GISS</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">AGILE GIScience Ser.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2700-8150</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/agile-giss-7-38-2026</article-id>
<title-group>
<article-title>From Individual Choices to Collective Impact: Quantifying the Role of Behavioural Changes in Greenhouse Gas Mitigation</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Naghdizadegan Jahromi</surname>
<given-names>Maryam</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>Bruno</surname>
<given-names>Sébastien</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>Le Maréchal</surname>
<given-names>Marius</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>Roche</surname>
<given-names>Stéphane</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Center for Research in Geospatial Data and Intelligence (CRDIG), Laval University, Quebec City, Canada</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Institute for Environment, Development and Society (EDS), Quebec City, Canada</addr-line>
</aff>
<pub-date pub-type="epub">
<day>10</day>
<month>06</month>
<year>2026</year>
</pub-date>
<volume>7</volume>
<elocation-id>38</elocation-id>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Maryam Naghdizadegan Jahromi 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://agile-giss.copernicus.org/articles/7/38/2026/agile-giss-7-38-2026.html">This article is available from https://agile-giss.copernicus.org/articles/7/38/2026/agile-giss-7-38-2026.html</self-uri>
<self-uri xlink:href="https://agile-giss.copernicus.org/articles/7/38/2026/agile-giss-7-38-2026.pdf">The full text article is available as a PDF file from https://agile-giss.copernicus.org/articles/7/38/2026/agile-giss-7-38-2026.pdf</self-uri>
<abstract>
<p>Household emissions, particularly from food consumption, represent a substantial yet underutilized component of urban greenhouse gas (GHG) emissions. While most research focuses on national or industrial scales, household behaviours remain underrepresented in local mitigation strategies. Addressing this gap requires approaches that account for both household choices and spatial context.&lt;/p&gt;
&lt;p&gt;This study applies a behavioural&amp;ndash;geospatial framework to survey data from Qu&amp;eacute;bec City households to quantify and spatialize diet-related GHG emissions. Meat consumption frequencies were converted into emissions using food-specific factors, and four realistic dietary scenarios were modelled at the district level.&lt;/p&gt;
&lt;p&gt;The results reveal substantial intra-urban disparities. Districts with higher reported meat consumption exhibit significantly greater mitigation potential, independent of population size. When combined with sociodemographic indicators such as income, the analysis highlights spatial structuring of behaviours commonly perceived as private choices.&lt;/p&gt;
&lt;p&gt;By transforming survey-based behavioural data into spatially explicit GHG information, this study expands geospatial analytics beyond infrastructure-based emissions and into the domain of lifestyle-driven carbon footprints. The proposed framework enables municipalities to identify high-potential districts, design targeted interventions, and integrate eco-conscious consumption into climate resilience and socio-ecological transition strategies.</p>
</abstract>
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