山东理工大学学报（自然科学版）

以建筑业碳排放的影响因素作为聚类指标，将具有相似特征的省份划分聚类；应用随机森林算法分别找出影响各类别建筑业碳排放的关键因素，构建STIRPAT模型，通过调控相关影响因素，预测在基准、粗放和低碳3种情境下的碳排放。结果表明：不同类别省域碳排放差异较大，类别1和类别2省域具有极大的碳减排潜力，不仅要加快产业结构升级和能源结构转型，还要注重经济及技术对建筑业碳排放的影响；达峰时间各不相同，类别1省域在基准、粗放和低碳情景的达峰时间分别为2032年、2032年和2030年左右，类别2省域仅在低碳情景下可于2032年达峰，类别3省域无论在何种情景下皆可在2030年左右实现碳达峰。

Taking the influencing factors of carbon emissions in the construction industry as the clustering index, the provinces with similar characteristics were divided into three clusters. The random forest algorithm was used to identify the key factors affecting the carbon emissions of each type of construction industry. The STIRPAT model was constructed to predict carbon emissions under three different scenarios(baseline, extensive and low-carbon) by adjusting the relevant influencing factors. The results show that there are large differences in carbon emissions among different types of provinces. Category 1 and Category 2 have great potential for carbon emission reduction. It is necessary not only to accelerate the upgrading of industrial structure and energy structure transformation, but also to pay attention to the impact of economy and technology on carbon emissions in the construction industry. The peak time varies. The provinces in Category 1 will reach peaks around 2032, 2032 and 2030 under the baseline, extensive and low-carbon scenarios respectively. Provinces in Category 2 can only reach the peak in 2032 under the low-carbon scenario. Provinces in Category 3 can peak their carbon emissions around 2030 under any scenario.