| 98 | 0 | 43 |
| 下载次数 | 被引频次 | 阅读次数 |
准确评估城市温室气体排放并进行预测是建设低碳城市的重要难题之一。为厘清影响青岛市碳排放的主要影响因素，通过IPCC温室气体清单法计算出青岛市2005—2021年全社会能源消费产生的碳排放量及青岛市自然碳汇量；运用熵值法筛选后确定了影响青岛市碳排放的5个主要因素：人口数量、人均GDP、能源结构、能源强度、第二产业占比；基于STIRPAT改进模型，设置了3种情景模式对青岛市2022—2030年的碳排放进行预测，得出以青岛市为代表的典型滨海城市碳排放趋势。结果表明：能源结构是影响滨海城市碳排放的首要因素，能源结构每增加1%，碳排放量增加2.218%；人均GDP的增长，也会对碳排放起到正向作用，人均GDP每增加1%，碳排放量增加0.288%；政府低碳政策调整优化可显著影响城市碳排放水平，说明了建设低碳城市时政策引领的关键作用。
Abstract：Accurately assessing and predicting urban greenhouse gas emissions is a crucial challenge in the development of low-carbon cities.To clarify the key factors influencing carbon emissions in Qingdao,the city′s total carbon emissions from energy consumption and its natural carbon sink from 2005 to 2021 were calculated using the IPCC greenhouse gas inventory method in this study.After applying the entropy method for screening,five main influencing factors were determined,such as population size,per capita GDP,energy structure,energy intensity,and the proportion of the secondary industry.Based on this analysis,an improved STIRPAT model was employed to establish three scenario modes for predicting Qingdao's carbon emissions from 2022 to 2030.The resulting trends provide insights into the carbon emission patterns of typical coastal cities,with Qingdao serving as a representative case.The findings indicate that the energy structure is the most significant determinant of carbon emissions in coastal cities.Specifically,a 1%increase in the reliance on carbon-intensive energy sources leads to a 2.218% rise in carbon emissions.Additionally,GDP also contributes to carbon emissions;a 1%increase in per capita GDP results in a 0.288%increase carbon emissions;effective low-carbon policies and optimization measures can significantly mitigate emission levels,underscoring the critical role of policy guidance in achieving sustainable urban development.
[1]国际能源署．2021年全球二氧化碳排放反弹至历史最高水平[J]．节能与环保，2022(3):8.IEA.Global CO2emissions rebounded to an all-time high in 2021[J].Energy Conservation&Environmental Protection,2022(3):8.
[2]RIBEIRO H V,RYBSKI D,KROPP J P.Effects of changing population or density on urban carbon dioxide emissions[J].Nature Communications,2019(10):3024.
[3]WEIGERT M,MELNYK O,WINKLER L,et al.Carbon emissions of construction processes on urban construction sites[J].Sustainability,2022,14(19):12947.
[4]孙燕燕．上海市旅游碳排放估算及其效应分解[J]．地域研究与开发，2020,39(1):122-126.SUN Yanyan.Estimation of CO2emission and its effect decomposition in tourism sector of Shanghai City[J].Areal Research and Development,2020,39(1):122-126.
[5]贾涛，杨仕浩，李欣，等．武汉居民建筑物碳排放反演计算和时空分析[J]．地球信息科学学报，2020,22(5):1063-1072.JIA Tao,YANG Shihao,LI Xin,et al.Computation of carbon emissions of residential buildings in Wuhan and its spatiotemporal analysis[J].Journal of Geo-information Science,2020,22(5):1063-1072.
[6]WU C B,HUANG G H,XIN B G,et al.Scenario analysis of carbon emissions’anti-driving effect on Qingdao’s energy structure adjustment with an optimization model,PartⅠ：Carbon emissions peak value prediction[J].Journal of Cleaner Production,2018,172:466-474.
[7]YANG F M,SHI LY,GAO L J.Probing CO2emission in Chengdu based on STRIPAT model and Tapio decoupling[J].Sustainable Cities and Society,2023,89:104309.
[8]李捷，刘译蔓，孙辉，等．中国海岸带蓝碳现状分析[J]．环境科学与技术，2019,42(10):207-216.LI Jie,LIU Yiman,SUN Hui,et al.Analysis of blue carbon in China’s coastal zone[J].Environmental Science&Technology,2019,42(10):207-216.
[9]刘岐涛．青岛统计年鉴[M]．北京：中国统计出版社，2020.LIU Qitao.Qingdao statistical yearbook[M].Beijing:China Statistics Press,2020.
[10]王琪．基于STIRPAT模型的河北省碳排放峰值预测研究[D]．保定：华北电力大学，2019.WANG Qi.Forecast of carbon emission peak in Hebei Province based on STIRPAT model[D].Baoding:North China Electric Power University,2019.
[11]闫新杰，孙慧．基于STIRPAT模型的新疆“碳达峰”预测与实现路径研究[J]．新疆大学学报（自然科学版）（中英文），2022,39(2):206-212.YAN Xinjie,SUN Hui.Research on prediction and realization path of“Carbon Peak”in Xinjiang based on STIRPAT model[J].Journal of Xinjiang University(Natural Science Edition in Chinese and English),2022,39(2):206-212.
[12]邓小乐，孙慧．基于STIRPAT模型的西北五省区碳排放峰值预测研究[J]．生态经济，2016,32(9):36-41.DENG Xiaole,SUN Hui.Forecast of the northwest five provinces’carbon emissions based on STIRPAT model[J].Ecological Economy,2016,32(9):36-41.
[13]张春华，居为民，王登杰，等．2004—2013年山东省森林碳储量及其碳汇经济价值[J]．生态学报，2018,38(5):1739-1749.ZHANG Chunhua,JU Weimin,WANG Dengjie,et al.Biomass carbon stocks and economic value dynamics of forests in Shandong Province from 2004to 2013[J].Acta Ecologica Sinica,2018,38(5):1739-1749.
[14]龙飞，沈月琴，吴伟光，等．区域林地利用过程的碳汇效率测度与优化设计[J]．农业工程学报，2013,29(18):251-261.LONG Fei,SHEN Yueqin,WU Weiguang,et al.Measurement and optimum design of carbon sequestration efficiency of regional forestland use process[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(18):251-261.
[15]FOURQUREAN J W,DUARTE C M,KENNEDY H,et al.Seagrass ecosystems as a globally significant carbon stock[J].Nature Geoscience,2012,5(7):505-509.
[16]CHMURA G L,ANISFELD S C,CAHOON D R,et al.Global carbon sequestration in tidal,saline wetland soils[J].Global Biogeochemical Cycles,2003,17(4):22.
[17]唐葆君，吉嫦婧，王翔宇，等．后疫情时期全国碳市场政策对经济和排放的影响[J]．中国环境管理，2021,13(3):19-27.TANG Baojun,JI Changjing,WANG Xiangyu,et al.Impact of national carbon market policy on economy and emissions in the post COVID-19period[J].Chinese Journal of Environmental Management,2021,13(3):19-27.
[18]刘竹，崔夺，邓铸，等．新型冠状病毒肺炎疫情对中国2020年碳排放的影响[J]．科学通报，2021,66(15):1912-1922.LIU Zhu,CUI Duo,DENG Zhu,et al.Impact on China’s CO2emissions from COVID-19pandemic[J].Chinese Science Bulletin,2021,66(15):1912-1922.
[19]李敏．青岛市能源消费碳排放及影响因素分析[J]．经济师，2017(7):158-160.LI Min.Analysis of carbon emissions and influencing factors of energy consumption in Qingdao[J].China Economist,2017(7):158-160.
[20]ALGIERI B,FÜG O,LOMBARDO R.The Italian journey:Carbon dioxide emissions,the role of tourism and other economic and climate drivers[J].Journal of Cleaner Production,2022,375:134144.
[21]FU L Y,WANG Q.Spatial and temporal distribution and the driving factors of carbon emissions from urban production energy consumption[J].International Journal of Environmental Research and Public Health,2022,19(19):12441.
[22]薛悦鑫，谢静超，怀超平，等．北京市能源碳排放影响因素分解分析[J]．建筑节能，2022,50(9):128-132.XUE Yuexin,XIE Jingchao,HUAI Chaoping,et al.Decomposition analysis of influencing factors of energy related carbon emission in Beijing[J].Building Energy Efficiency,2022,50(9):128-132.
[23]初丽霞，黄梦瑶．山东省碳排放脱钩效应及影响因素研究：基于Tapio脱钩指数和LMDI模型分析[J]．环境科学与管理，2022,47(9):20-25.CHU Lixia,HUANG Mengyao.Study on decoupling effect and influencing factors of carbon emissions in Shandong Province:Based on Tapio Decoupling Index and LMDI model analysis[J].Environmental Science and Management,2022,47(9):20-25.
[24]杨青，彭若慧，刘星星，等．基于地理加权回归的省域碳排放影响因素研究[J]．环境工程技术学报，2023,13(1):54-62.YANG Qing,PENG Ruohui,LIU Xingxing,et al.Study on influencing factors of provincial carbon emission based on geographically weighted regression[J].Journal of Environmental Engineering Technology,2023,13(1):54-62.
[25]朱喜安，魏国栋．熵值法中无量纲化方法优良标准的探讨[J]．统计与决策，2015(2):12-15.ZHU Xi’an,WEI Guodong.Discussion on the excellent standard of dimensionless method in entropy method[J].Statistics&Decision,2015(2):12-15.
[26]陆添超，康凯．熵值法和层次分析法在权重确定中的应用[J]．电脑编程技巧与维护，2009(22):19-20.LU Tianchao,KANG Kai.The application of entropy method and AHP in weight determining[J].Computer Programming Skills&Maintenance,2009(22):19-20.
[27]何秀丽．多元线性模型与岭回归分析[D]．武汉：华中科技大学，2005.HE Xiuli.Research on multivariate linear model and ridge regression[D].Wuhan:Huazhong University of Science and Technology,2005.
[28]赵东波．线性回归模型中多重共线性问题的研究[D]．锦州：渤海大学，2017.ZHAO Dongbo.Study on multicollinearity in linear regression model[D].Jinzhou:Bohai University,2017.
[29]陈思琦．青岛市用煤现状评估及对策建议[J]．内蒙古煤炭经济，2020(22):151-152.CHEN Siqi.Assessment of the current situation of coal use in Qingdao and suggestions for countermeasures[J].Inner Mongolia Coal Economy,2020(22):151-152.
[30]张哲，任怡萌，董会娟．城市碳排放达峰和低碳发展研究：以上海市为例[J]．环境工程，2020,38(11):12-18.ZHANG Zhe,REN Yimeng,DONG Huijuan.Research on carbon emissions peaking and low-carbon development of cities:A case of Shanghai[J].Environmental Engineering,2020,38(11):12-18.
[31]宋晓晖，张裕芬，汪艺梅，等．基于IPAT扩展模型分析人口因素对碳排放的影响[J]．环境科学研究，2012,25(1):109-115.SONG Xiaohui,ZHANG Yufen,WANG Yimei,et al.Analysis of impacts of demographic factors on carbon emissions based on the IPAT model[J].Research of Environmental Sciences,2012,25(1):109-115.
[32]21世纪经济研究院碳中和课题组．中国净零碳城市发展报告（2022）：深圳高居榜首北京、青岛、杭州、昆明位列前五[N].21世纪经济报道，2022-06-02(11版)．Carbon Neutrality Research Group of 21st Century Economic Research Institute.China net zero carbon cities development report2022:Shenzhen tops the list,Beijing,Qingdao,Hangzhou and Kunming top five[N].21st Century Business Herald,2022-06-02(11th ed).
基本信息:
中图分类号:X321
引用信息:
[1]明志军,李沁原,周娟,等.中国典型滨海城市碳排放分析及预测——以青岛市为例[J].青岛理工大学学报,2025,46(06):55-63.
基金信息:
山东省住房和城乡建设厅研究开发项目（2022-k7-15）; 青海省重点研发与转化项目（2024-SF-143）