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Urban shrinkage and carbon emission prediction structures: A methodological extension using interpretable machine learning
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DOI：10.1016/j.scs.2026.107817.png)
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Abstract
En 中文
<ul class="list">
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="para0001">
Relative shrinkage captures pre-decline carbon risks across the YREB.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="para0002">
Most shrinking cities record emission growth.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="para0003">
High emissions remain embedded in metropolitan areas and industrial corridors.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="para0004">
XGBoost-SHAP characterize response boundaries and non-additive configurations.
</div></span></li>
</ul>
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