统计与数据科学学院知识库

《黄河流域生态保护和高质量发展规划纲要》指出黄河流域实现“双碳”目 标对于中国提出的2030年前实现碳达峰，2060年前实现碳中和两目标具有强有力的推动作用和重要意义。因此针对黄河流域的碳达峰预测就显得尤为必要，在新形势下加强黄河流域生态环境保护的同时也助推黄河流域高质量发展，同时通过不同路径设置下的碳达峰时间对比，为低碳减排政策的提出提供科学的参考和依据。以黄河流域碳达峰这一重要环节促进全国碳达峰碳中和目标实现。

本文根据黄河流域沿线56个城市2000-2021年社会发展、自然发展数据及碳排放量数据，将PSO-XGBoost-RF模型与RF-XGB、RF等六个模型进行拟合效果对比，根据模型评价指标结果，选取PSO-XGBoost-RF模型作为黄河流域沿线城市整体碳达峰预测的最终模型，又根据前期皮尔逊相关性分析选取的黄河流域沿线城市二氧化碳排放量影响因素指标，设定了六条不同的碳排放量预测路径，综合对比碳达峰的时间和峰值，最终选择相对有效的碳达峰路径并提出相应的政策建议。

基准路径下在2033年实现碳达峰，峰值为205132万吨，无法实现2030年前碳达峰的目标。在经济、社会人口发展保持快速增长的前提下，黄河流域沿线城市整体甚至无法在2035年前实现碳排放达峰，即经济和人口增长会推迟碳达峰时间。黄河流域沿线城市整体在产业结构优化、能源消费量减缓和组合路径下，均能实现2030年前碳达峰目标，且三种路径碳达峰时间依次提前；且在产业结构优化和能源消费量减缓路径下达峰时间分别是2031年和2029年，峰值分别为201979.5 万吨和 198083.5 万吨。在经济社会保持稳定增长的同时，全面优化产业结构，全面减缓能源消耗量，即组合路径下黄河流域沿线城市整体将于2026年实现碳达峰，峰值是191713.2万吨。在上述研究的基础上，本文提出黄河流域沿线城市碳减排的措施：提高人口质量，优化城乡规划；减缓能源消费，优化能源结构；优化产业结构，实现碳排放脱钩经济增长。

The Outline of the Yellow River Basin Ecological Protection and High quality Development Plan points out that achieving the "dual carbon" goals in the Yellow River Basin has a strong driving role and important significance for China's proposal to achieve carbon peak before 2030 and carbon neutrality before 2060. Therefore, it is particularly necessary to predict the carbon peak in the Yellow River Basin, to strengthen the ecological and environmental protection of the Yellow River Basin under the new situation, and at the same time to promote the high-quality development of the Yellow River Basin, and to provide a scientific reference and basis for the proposal of low-carbon emission reduction policies through the comparison of carbon peak time under different path settings, and stimulate the endogenous power of energy conservation. Promote the realization of the national carbon peak and carbon neutrality goal with the important link of carbon peak in the Yellow River Basin.

Based on the social development, natural development data and carbon emission data of 56 cities along the Yellow River Basin from 2000 to 2021, this paper compares the fitting effect of the PSO-XGBoost-RF model with six models such as RF-XGB and RF, and selects the PSO-XGBoost-RF model as the final model for the prediction of the overall carbon peak of cities along the Yellow River Basin according to the results of the model evaluation index. According to the influencing factors of carbon dioxide emissions in cities along the Yellow River Basin selected by Pearson correlation analysis in the previous stage, six different carbon emission prediction paths were set up, and the time and peak of carbon peak were comprehensively compared, and finally a relatively effective carbon peak path was selected and corresponding policy suggestions were put forward.

Under the baseline pathway, carbon peaking will be achieved in 2033, with a peak of 205132 million tons, and the goal of carbon peaking before 2030 cannot be achieved. Under the premise of maintaining rapid economic, social and demographic development, the cities along the Yellow River Basin as a whole will not even be able to peak carbon emissions before 2035, that is, economic and population growth will delay the time of carbon peaking. Cities along the Yellow River Basin as a whole can achieve the carbon peak goal before 2030 under the optimization of industrial structure, energy consumption mitigation and combination pathways, and the carbon peak time of the three pathways is advanced in turn. In addition, the peak time of industrial structure optimization and energy consumption mitigation path is 2031 and 2029, with peaks of 2,019,795,000 tons and 1,980,835,000 tons, respectively. While maintaining stable economic and social growth, we will comprehensively optimize the industrial structure and comprehensively reduce energy consumption, that is, the cities along the Yellow River Basin will achieve carbon peak in 2026, with a peak of 1,917,132,000 tons. On the basis of the above research, this paper proposes measures to reduce carbon emissions in cities along the Yellow River Basin: improving population quality and optimizing urban and rural planning; Slow down energy consumption and optimize the energy structure; Optimize the industrial structure and achieve economic growth that decouples carbon emissions.