摘要
为了研究能源消耗碳排放的时空演变规律并进行“碳达峰”预测,该研究以山西省为例,基于1997-2020年夜间灯光数据反演该省碳排放量,并采用引力模型与标准差椭圆模型对高碳排放区展开为期20年的时空演变分析,通过长短期记忆网络模型对山西省“碳达峰”进行预测。结果显示,1997-2020年山西省碳排放量以5.8%的增长率呈上升趋势;太原市和大同市为高碳排放区,阳泉市为低碳排放区;太原对其周边城市碳排放产生明显引力作用,形成以其为中心的高碳排放区,碳排放重心由晋中地区向太原迁移;目前山西省煤炭产业仍然占据优势地位,但节能减排成效显著,有望在2030年实现“碳达峰”,且峰值排放量为14.5亿t,研究结果将为区域社会经济绿色转型发展及碳减排机制的制定提供科学依据。
In order to study the temporal and spatial evolution of carbon emissions from energy consumption and make“carbon peak”prediction,this research takes Shanxi Province as an example,inverts the carbon emissions of the province based on night light data from 1997 to 2020,and uses gravity model and standard deviation ellipse model to analyze the temporal and spatial evolution of high-carbon emission areas for 20 years.Through the long and short-term memory network model,the carbon peak of the Province is predicted.The results show that the carbon emissions of the Province increases by 5.8%from 1997 to 2020.Taiyuan and Datong are high carbon emission zones,while Yangquan is low carbon emission zone.Taiyuan produces obvious gravity of carbon emissions to the surrounding cities,forming with the center of the high carbon emission zone,central carbon emissions from Jinzhong region to Taiyuan migration.At present,the coal industry of the Province still occupies a dominant position,but has achieved remarkable results in energy conservation and emission reduction.It is expected to achieve“carbon peak”in 2030,and the peak emission is 1.45 billion tons.The research results will provide scientific basis for the development of green transformation of regional social economy and the formulation of carbon emission reduction mechanism.
作者
常建波
付兴涛
郑美君
郝舒哲
CHANG Jianbo;FU Xingtao;ZHENG Meijun;HAO Shuzhe(School of Water Science and Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《环境科学与技术》
CAS
CSCD
北大核心
2023年第3期81-89,共9页
Environmental Science & Technology
关键词
能源革命
夜间灯光数据
碳排放
时空演变
长短期记忆网络
energy revolution
night light data
carbon emissions
space-time evolvement
long and short term memory networks