数据驱动的碳排放评估和陶行业碳排放减少的优化
1School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究使用统计模型量化陶行业的碳排放量,
科学领域:
- 环境科学
- 工业生态
- 数据科学
背景情况:
- 陶行业是碳排放的重要贡献者.
- 评估碳排放绩效的区域差异对于有针对性的减排至关重要.
- 了解碳排放的驱动因素对于制定有效的减排战略至关重要.
研究的目的:
- 量化评估和比较中国主要陶生产地区的碳排放性能.
- 确定影响陶行业碳排放的关键因素.
- 提出和验证低碳转型和减排的可行途径.
主要方法:
- 对碳排放性能进行定量评估的TOPSIS模型.
- 通过XGBoost和SHAP的组合建模来确定碳排放的主要驱动因素.
- 统计预测和多代理碳交易模拟模型用于趋势分析和政策探索.
主要成果:
- 佛山的碳排放强度明显高于京城和齐博.
- 高能耗,依赖原煤 (弹性为25.84%) 和环境投资不足是关键问题.
- 能源结构优化,包括天然气替代,可能导致2017年后碳排放量下降.
结论:
- 统计建模和数据分析为陶行业的碳排放提供了精确的定量洞察力.
- 包括能源结构优化和碳交易在内的有针对性的减排途径是可行的.
- 该研究通过数据驱动的建模来验证陶行业绿色转型的可行性.
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