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Abstract：
Global warming, caused by greenhouse gas emissions, is a major challenge for all human societies. To ensure that ambitious carbon neutrality and sustainable economic development goals are met, regional human activities and their impacts on carbon emissions must be studied. Guizhou Province is a typical karst area in China that predominantly uses fossil fuels. In this study, a backpropagation (BP) neural network and extreme learning machine (ELM) model, which is advantageous due to its nonlinear processing, were used to predict carbon emissions from 2020 to 2040 in Guizhou Province. The carbon emissions were calculated using conversion and inventory compilation methods with energy consumption data and the results showed an \"S\" growth trend. Twelve influencing factors were selected, however, five with larger correlations were screened out using a grey correlation analysis method. A prediction model for carbon emissions from Guizhou Province was established. The prediction performance of a whale optimization algorithm (WOA)-ELM model was found to be higher than the BP neural network and ELM models. Baseline, high-speed, and low-carbon scenarios were analyzed and the size and time of peak carbon emissions in Liaoning Province from 2020 to 2040 were predicted using the WOA-ELM model.
摘要：
全球变暖,由温室气体排放引起的，是所有人类社会面临的重大挑战。确保实现雄心勃勃的碳中和和可持续经济发展目标，必须研究区域人类活动及其对碳排放的影响。贵州省是中国典型的岩溶地区，主要使用化石燃料。在这项研究中,反向传播(BP)神经网络和极限学习机(ELM)模型，由于其非线性处理，这是有利的，对贵州省2020-2040年的碳排放量进行了预测。使用转换和清单编制方法与能源消耗数据计算碳排放量，结果显示“S”增长趋势。选择12个影响因素，然而,利用灰色关联分析方法筛选出5个关联度较大的指标。建立了贵州省碳排放预测模型。发现鲸鱼优化算法（WOA）-ELM模型的预测性能高于BP神经网络和ELM模型。基线,高速,利用WOA-ELM模型对辽宁省2020-2040年碳排放峰值的大小和时间进行了预测。
