环境工程学报2024，Vol.18Issue(12)：3405-3413,9.DOI:10.12030/j.cjee.202401029
山东省县域能源消费碳排放时空特征及影响因素研究
Spatial-temporal characteristics and influencing factors of county-level energy-related carbon emissions in Shandong province
摘要
Abstract
County is a key administrative unit for carbon emission reduction and policy implementation.It is of great significance to study spatial and temporal characteristics and influencing factors of carbon emission at the county level to achieve the goal of carbon peak and neutrality.In recent years,Shandong Province has become one of the largest carbon emitters in China,but existing studies have failed to capture the latest trends at the county level and their driving factors.Based on night light data from 2016 to 2020,this study used the backpropagation neural network algorithm to estimate monthly energy consumption carbon emissions at the county level in Shandong Province,and combined with spatial autocorrelation and spatial econometric models to study the spatial-temporal evolution characteristics and influencing factors of energy consumption carbon emissions.The results showed that:1)From 2016 to 2020,energy consumption carbon emissions in Shandong Province showed an overall upward trend and a significant seasonal trend.The monthly carbon emissions and per capita carbon emissions were the lowest in January and February,and the highest in July,August,and December;2)Spatially,there was significant heterogeneity of energy consumption carbon emissions at the county level in Shandong Province.The high-emission areas were mainly concentrated in Qingdao and Jinan,and showed a large spatial expansion at the county level;3)Among the five influencing factors affecting carbon emissions of energy consumption in Shandong Province,except population density,which had a negative impact on carbon emissions of energy consumption,the other four influencing factors had a positive impact on carbon emissions of energy consumption,and their influence degrees were economic development level,population size,urbanization level and industrial structure.The results can provide a reference for the formulation of precise emission reduction policies at the county level.关键词
碳排放/夜间灯光/时空特征/影响因素/反向传播神经网络/山东Key words
carbon emission/nighttime light/spatial-temporal characteristics/influencing factors/back propagation neural networks/Shandong Province分类
资源环境引用本文复制引用
宛如星,张立,钱双月,阮建辉,张哲,吴军,汤铃,蔡博峰..山东省县域能源消费碳排放时空特征及影响因素研究[J].环境工程学报,2024,18(12):3405-3413,9.基金项目
国家重点研发计划资助项目(2023YFC3807700) （2023YFC3807700）
国家自然科学基金资助项目(71971007) （71971007）
北京市自然科学基金资助项目(JQ21033) （JQ21033）
