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苏州市低碳发展水平的初步分析 被引量:1

Preliminary Analysis of Low Carbon Development Level in Suzhou
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摘要 在碳中和相关政策稳步推动、投资热度持续升温的背景下,以苏州市作为研究地,选取2009至2020年12年的数据,从经济发展、生态环境、社会发展和碳排放4个角度构建苏州市碳中和能力评价体系,采用熵权法对苏州市碳中和能力进行测算,应用DNN(Deep Neural Network)深度神经网络分析苏州市碳排放状况。结果表明,2009-2020年苏州市碳中和能力在稳步上升,单一指标因子中经济发展对碳中和能力影响最大,其次是碳排放和生态环境,社会发展影响最小。并且随着苏州市经济、社会的发展,碳排放在不断提升,苏州市未来减碳压力还很大。 Under the background of the steady promotion of carbon neutrality policy and the rising investment enthusiasm,this paper takes Suzhou as the research place,select 12 years of data for 2009-2020,the evaluation system of carbon neutrality capacity in Suzhou is constructed from the perspectives of economic development,ecological environment,social development and carbon emission,using entropy weight method to calculate the carbon neutrality capacity of Suzhou,and applying DNN(Deep Neural Network)to analyze carbon emission in Suzhou.The results show that Suzhou carbon neutrality ability were rising steadily in 2009-2020,among the single index factors,economic development has the greatest impact on carbon neutrality,followed by carbon emission and ecological environment,and social development has the least impact.In addition,with the economic and social development of Suzhou City,carbon emissions are constantly increasing,and the pressure of carbon reduction in Suzhou City in the future is still great.
作者 谢芝 冉子妍 王恒达 XIE Zhi;RAN Ziyan;WANG Hengda(Chongqing Institute of Engineering)
机构地区 重庆工程学院
出处 《上海节能》 2022年第12期1495-1500,共6页 Shanghai Energy Saving
基金 基于卷积神经网络的图像分割算法优化研究(KJQN202001903) 基于图卷积神经网络的联合实体关系抽取研究(KJQN202001901) 随机Kaczmarz算法在高维数据中的应用(2019xzky03) 基于深度学习的图像分割研究(2019xzky04)。
关键词 碳中和能力评价体系 熵权法 DNN深度神经网络 低碳发展水平评价体系 Evaluation System of Carbon Neutrality Capacity Entropy Weight Method Deep Neural Network Evaluation System of Low Carbon Development Level
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