Using interpretable machine learning to reveal the impact of rapid development in the emerging urban area on carbon storage: Marginal and interaction effects
With the rapid urbanization of emerging metropolitan areas, accurately assessing carbon storage changes driven by land use transitions is essential for understanding regional carbon cycling and identifying spatial patterns of carbon sources and sinks.
Authors
- Fanqiang Meng (ORCID: https://orcid.org/0000-0003-0443-5071)
- Zilin Ye (ORCID: https://orcid.org/0009-0004-4465-9445)
- Liuhui Zhao (ORCID: https://orcid.org/0000-0001-8674-8631)
- Zhengjun Gong (ORCID: https://orcid.org/0000-0001-5710-3156)
- Yuan-Jun Tong (ORCID: https://orcid.org/0000-0002-4462-2261)
- Xun Gong (ORCID: https://orcid.org/0000-0002-1494-0955)
- Dongmei Wang (ORCID: https://orcid.org/0000-0002-3202-2955)
- Zhixiao Zhang (ORCID: https://orcid.org/0000-0002-7204-071X)
- Yugang Wang
Institutions
- Southwest Jiaotong University (CN)
Publication Details
- Published
- 2025-08-31
- DOI
- https://doi.org/10.22541/au.175665277.72839517/v1
- Primary Topic
- Energy Load and Power Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00