基于历史数据的碳排放预测OA

Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;however,uncertainties in future development often lead to deviations from these assumptions.To address this limita-tion,this study proposes a data-driven approach for evaluat-ing national carbon emissions using historical data.Coun-tries with similar energy consumption patterns were selected as reference samples,and their emission pathways were ana-lyzed to predict future emissions for countries that have not yet reached their peak.Key indicators,including peak lev-els,timing,plateau duration,and post-peak decline rates,were identified.The results indicate that the trends in un-peaked economies can be effectively assessed based on the emission patterns of countries with comparable energy struc-tures.Applying this framework to China suggests a carbon peak between 2027 and 2030,in the range of 14.207 to 16.234 Gt,followed by a gradual decline from 2031 to 2036.Compared with the average results of the existing studies,the predicted minimum and maximum emissions show error margins of 10.1%and 1.41%,respectively.This study proposes a top-down methodology that provides a transparent,reproducible,and empirical framework for forecasting carbon emission pathways,thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.