文献类型：期刊文献

英文题名：Carbon peaking prediction scenarios based on different neural network models: A case study of Guizhou Province

作者：Lian, Da Yang, Shi Qiang Yang, Wu Zhang, Min Ran, Wen Rui

第一作者：Lian, Da

通信作者：Yang, W[1]

机构：[1]China Railway Fifth Bur Grp Co Ltd, Guiyang, Peoples R China;[2]Geol Brigade Guizhou Prov Bur Geol & Mineral Resou, Zunyi, Peoples R China;[3]Guizhou Inst Technol, Fac Resources & Environm Engn, Guiyang, Peoples R China;[4]Guizhou Nat Resources Survey & Planning Res Inst, Guiyang, Peoples R China

第一机构：China Railway Fifth Bur Grp Co Ltd, Guiyang, Peoples R China

通信机构：corresponding author), Guizhou Inst Technol, Fac Resources & Environm Engn, Guiyang, Peoples R China.|贵州理工学院;

年份：2024

卷号：19

期号：6

外文期刊名：PLOS ONE

收录：;Scopus(收录号：2-s2.0-85197085814);WOS:【SCI-EXPANDED(收录号:WOS:001253508900047)】；

基金：This research was supported by Concealed Ore Deposit Exploration and Innovation Team of Guizhou Colleges and Universities (Guizhou Education and Cooperation Talent Team [2015]56), Provincial Key Discipline of Geological Resources and Geological Engineering of Guizhou Province (ZDXK[2018]001), Huang Danian Resources of National colleges and universities Teachers' Team of Exploration Engineering (Teacher Letter [2018] No. 1), Geological Resources and Geological Engineering Talent Base of Guizhou Province (RCJD2018-3), Key Laboratory of Karst Engineering Geology and Hidden Mineral Resources of Guizhou Province (Qianjiaohe KY [2018] No. 486 Guizhou Institute of Technology Rural Revitalization Soft Science Project(2022xczx10), Education and Teaching Reform Research Project of Guizhou Institute of Technology (JGZD202107, 2022TDFJG01).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

语种：英文

摘要：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.