中国碳达峰路径优化数据集（2030年）

在利用随机森林算法对碳强度影响因子重要性刻画的基础上，识别碳强度的关键驱动因子，并将其用于构建偏最小二乘回归预测模型；然后对2030年的化石能源消费、非化石能源消费、服务业比重、技术进步和居民新兴消费设置情景值及其调控模式，结合GDP的增长趋势模拟不同情景下中国CO2排放量的变化轨迹，从而得出各种碳达峰路径。

Based on characterizing the importance of carbon intensity influencing factors using the Random Forest algorithm, this study identifies the key driving factors of carbon intensity and constructs a partial least squares regression prediction model with them. Then, scenario values and their regulation modes are set for fossil energy consumption, non-fossil energy consumption, the proportion of the service industry, technological progress, and residents' emerging consumption in 2030. Combined with the GDP growth trend, the change trajectories of China's CO2 emissions under different scenarios are simulated, thereby deriving various carbon peak paths.

搜集汇总

背景与挑战

背景概述

该数据集聚焦中国2030年碳达峰路径优化，通过随机森林算法和偏最小二乘回归模型，模拟不同情景下的CO2排放轨迹。模型解释精度达85%，为研究碳达峰目标提供科学依据。