[基于STIRPAT-LEAP建模的发展路径和西省沉增强和减排的驱动因素]
Yan-Ying Li1, Yu-Cong Jiang1, Wen Li1
1School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China.
Huan jing ke xue= Huanjing kexue
|February 9, 2026
概括
中国中国中国中国.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 区域经济发展 区域经济发展
背景情况:
- 中国各省市的碳排放量超过全国总量的90%.
- 准确预测碳排放对于实现双碳目标和协同经济发展至关重要.
研究的目的:
- 开发一个综合模型来预测山西省的碳排放.
- 为了预测碳排放,并模拟在各种场景下双碳路径.
- 确定减少碳排放的关键驱动因素.
主要方法:
- 为西省开发了一个集成的RR-STIRPAT-LEAP模型.
- 利用2000年至2021年的截面数据进行模型优化.
- 采用了五种联合场景和碳汇吸收模型来模拟路径.
- 应用了ReliefF算法来识别减排驱动因素.
主要成果:
- 综合模型实现了0.24%的预测准确度,明显优于单个模型.
- 西省的碳排放量预计将在2030年达到4.1909亿的峰值.
- 根据一个共同的方案,到2060年可以实现碳中和,生态碳汇发挥着至关重要的作用.
- 能源结构升级,效率提高和消费控制是低碳转型的关键.
结论:
- 该研究为西省的双碳目标提供了一条经过验证的途径.
- 工业,发电和农业是减少排放的优先领域.
- 这些发现为决策者和企业制定低碳战略提供了科学基础.
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