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 energyrelated carbon emissions in Shandong province

作　　者：宛如星 张立[2] 钱双月 阮建辉 张哲 吴军[1] 汤铃 蔡博峰 WAN Ruxing;ZHANG Li;QIAN Shuangyue;RUAN Jianhui;ZHANG Zhe;WU Jun;TANG Ling;CAI Bofeng(School of Economics and Management,Beijing University of Chemical Technology,Beijing,100029,China;Ministry of Education Key Laboratory for Earth System Modeling,Department of Earth System Science,Tsinghua University,Beijing 100084,China;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;Center for Carbon Neutrality,Chinese Academy of Environmental Planning,Beijing 100043,China)

机构地区：[1]北京化工大学经济管理学院,北京100029 [2]清华大学地球系统科学系,北京100084 [3]中国科学院大学经济与管理学院,北京100190 [4]生态环境部环境规划院碳达峰碳中和研究中心,北京100043

出　　处：《环境工程学报》2024年第12期3405-3413,共9页Chinese Journal of Environmental Engineering

基　　金：国家重点研发计划资助项目(2023YFC3807700);国家自然科学基金资助项目(71971007);北京市自然科学基金资助项目(JQ21033)。

摘　　要：县域是落实碳减排政策的关键行政单位,研究县域层面的碳排放时空特征和影响因素对实现“双碳”目标具有重要意义。近年来,山东省已成为中国最大的碳排放省份之一,但现有研究未能捕捉到县域层面的最新趋势以及其驱动因素。研究基于2016—2020年夜间灯光数据,在使用反向传播神经网络算法估算山东省县域层面的月度能源消费碳排放量的基础上,结合空间自相关和空间计量模型等方法研究了能源消费碳排放的时空演变特征和影响因素。研究结果表明:1)2016—2020年,山东省能源消费碳排放总体呈上升趋势,并呈现出显著季节性趋势,月度的碳排放量和人均碳排放量在每年1、2月份最低,在7、8和12月份最高;2)空间上,山东省县域能源消费碳排放存在显著异质性,高排放区域主要集中在青岛和济南等城市,并在县域层面显示较大的空间扩张;3)影响山东省能源消费碳排放的5个影响因素中,除人口密度对能源消费碳排放有负向影响,其余4个影响因素与对能源消费碳排放有正向影响且其影响程度分别为经济发展水平、人口规模、城镇化水平和产业结构。研究结果可以为县域层面制定精准化减排政策提供参考。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 

分 类 号：X22[环境科学与工程—环境科学]