Machine learning-based analysis of economic efficiency disparities and transition drivers between high- and low-carbon industries in China.
- DOI
- 10.1186/s13021-025-00393-2
- Published
- 2026 Feb 14
- Container
- Carbon balance and management
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1186/s13021-025-00393-2,
title = {Machine learning-based analysis of economic efficiency disparities and transition drivers between high- and low-carbon industries in China.},
author = {Huang Z and Zhang Q and Zheng Y and Tian E},
year = {2026},
journal = {Carbon balance and management},
doi = {10.1186/s13021-025-00393-2},
url = {https://doi.org/10.1186/s13021-025-00393-2}
}RIS
TY - JOUR TI - Machine learning-based analysis of economic efficiency disparities and transition drivers between high- and low-carbon industries in China. AU - Huang Z AU - Zhang Q AU - Zheng Y AU - Tian E PY - 2026 JO - Carbon balance and management DO - 10.1186/s13021-025-00393-2 UR - https://doi.org/10.1186/s13021-025-00393-2 ER -
APA
Z, H., Q, Z., Y, Z., & E, T. (2026). Machine learning-based analysis of economic efficiency disparities and transition drivers between high- and low-carbon industries in China.. Carbon balance and management. https://doi.org/10.1186/s13021-025-00393-2
Source records
- pubmed · retrieved 2026-09-26T08:18:43.251Z