Source Journal of CSCD
Source Journal for Chinese Scientific and Technical Papers
Core Journal of RCCSE
Included in JST China
Volume 41 Issue 7
Jul.  2023
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DING Yi, YIN Jian, JIANG Hongtao, XIA Ruici, WEI Danqi, LUO Xinyuan. SYSTEM DYNAMICS PREDICTION OF CARBON PEAKING IN PEARL RIVER DELTA[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 22-29. doi: 10.13205/j.hjgc.202307004
Citation: DING Yi, YIN Jian, JIANG Hongtao, XIA Ruici, WEI Danqi, LUO Xinyuan. SYSTEM DYNAMICS PREDICTION OF CARBON PEAKING IN PEARL RIVER DELTA[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 22-29. doi: 10.13205/j.hjgc.202307004

SYSTEM DYNAMICS PREDICTION OF CARBON PEAKING IN PEARL RIVER DELTA

doi: 10.13205/j.hjgc.202307004
  • Received Date: 2022-11-08
  • According to the data from 2000 to 2020, the investigation employed the emission coefficient method provided by IPCC to estimate the total annual carbon emissions of the Pearl River Delta during the study period. Using the STIRPAT model, the influencing factors were categorized into seven dimensions. When analyzing the factors that affected the carbon emissions in the Pearl River Delta, the spatial Dubin model was applied, and eight forecast scenarios were formulated depending on the development goals proposed by Guangdong province. For the prediction of the trend of carbon emissions in the Pearl River Delta, this paper dynamically forecast the situation under different policy plans from 2021 to 2035 on the basis of system dynamics. The research results were as follows:1) under the established policy scenarios, the total carbon emission of the Pearl River Delta would reach its peak in 2030; 2) certain policy interventions could reduce carbon emission, in other words, to achieve carbon peak was inseparable from the active participation of government rather than relaxed management; 3) in the context of single emission reduction policy, the Pearl River Delta would realize the carbon peak in 2025-2030 under most prediction scenarios; 4) the coordinated control of multiple carbon emission reduction policies could accomplish carbon peak in 2024, better than single carbon emission reduction policy. In addition, relying on the consequence of the spatial Dubin model and carbon emission prediction, targeted policy recommendations such as increasing urban greening, paying attention to inter-city spatial connection and policy coordination, optimizing industrial structure, improving power system and strengthening residents' awareness of green consumption were proposed, which would help to achieve carbon peaking.
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  • [1]
    薛成杰,方战强.土壤修复产业碳达峰碳中和路径研究[J].环境工程,2022,40(8):231-238.
    [2]
    杨楠,李艳霞,吕晨,等.唐山市钢铁行业碳排放核算及达峰预测[J].环境工程,2020,38(11):44-52.
    [3]
    ZHANG Y, LIU C, CHEN L, et al.Energy-related CO2 emission peaking target and pathways for China's city:a case study of Baoding city[J].Journal of Cleaner Production, 2019, 226:471-481.
    [4]
    田娟娟,张金锁.基于地理探测器的中国碳排放时空分布特征及驱动因素研究[J].生态经济,2022,38(7):13-20.
    [5]
    曹俊文,张钰玲.中国省域碳排放特征与碳减排路径研究[J].生态经济,2022,38(8):13-19.
    [6]
    胡艳兴,潘竟虎,李真,等.中国省域能源消费碳排放时空异质性的EOF和GWR分析[J].环境科学学报,2016,36(5):1866-1874.
    [7]
    王锋,秦豫徽,刘娟,等.多维度城镇化视角下的碳排放影响因素研究:基于中国省域数据的空间杜宾面板模型[J].中国人口·资源与环境,2017,27(9):151-161.
    [8]
    杨旺舟.中国农村居民食品消费碳排放的时空格局及其影响因素[J].中国环境管理,2022,14(3):112-117.
    [9]
    吐尔逊·买买提,丁为民,谢建华.基于神经网络的碳排放预测及影响因素分析[J].环境工程,2017,35(6):156-160.
    [10]
    曲鲁平,翟腾腾,张全景.基于灰色理论模型的山东省土地利用碳排放研究[J].山东农业大学学报(自然科学版),2019,50(2):290-296.
    [11]
    王少剑,莫惠斌,方创琳.珠江三角洲城市群城市碳排放动态模拟与碳达峰[J].科学通报,2022,67(7):670-684.
    [12]
    方创琳.新发展格局下的中国城市群与都市圈建设[J].经济地理,2021,41(4):1-7.
    [13]
    WANG Y, NIU J, LI M, et al.Spatial structure and carbon emission of urban agglomerations:spatiotemporal characteristics and driving forces[J].Sustainable Cities and Society, 2022, 78(3):103600.
    [14]
    LI F, XU Z, MA H.Can China achieve its CO2 emissions peak by 2030?[J].Ecological Indicators, 2018, 84:337-344.
    [15]
    XU G, SCHWARZ P, YANG H.Adjusting energy consumption structure to achieve China's CO2 emissions peak[J].Renewable and Sustainable Energy Reviews, 2020, 122:109737.
    [16]
    YUAN J, XU Y, HU Z, et al.Peak energy consumption and CO2 emissions in China[J].Energy Policy, 2014, 68:508-523.
    [17]
    NIU S, LIU Y, DING Y, et al.China's energy systems transformation and emissions peak[J].Renewable and Sustainable Energy Reviews, 2016, 58:782-795.
    [18]
    冯烽,白重恩.广东省能源需求预测与碳排放达峰路径研究:基于混合单位能源投入产出模型[J].城市与环境研究,2019(2):8-27.
    [19]
    赵金辉,李景顺,王潘乐,等.基于Lasso-BP神经网络模型的河南省碳达峰路径研究[J].环境工程,2022,40(12):151-156

    ,164.
    [20]
    韩楠,罗新宇.多情景视角下京津冀碳排放达峰预测与减排潜力[J].自然资源学报,2022,37(5):1277-1288.
    [21]
    刘菁,赵静云.基于系统动力学的建筑碳排放预测研究[J].科技管理研究,2018,38(9):219-226.
    [22]
    袁晓玲,郗继宏,李朝鹏,等.中国工业部门碳排放峰值预测及减排潜力研究[J].统计与信息论坛,2020,35(9):72-82.
    [23]
    徐磊,董捷,张俊峰,等.基于SD模型的湖北省农业碳排放系统仿真与政策优化[J].资源开发与市场,2017,33(9):1031-1035.
    [24]
    王火根,肖丽香,廖冰.基于系统动力学的中国碳减排路径模拟[J].自然资源学报,2022,37(5):1352-1369.
    [25]
    赵先贵,肖玲,马彩虹,等.山西省碳足迹动态分析及碳排放等级评估[J].干旱区资源与环境,2014,28(9):21-26.
    [26]
    李国平,吕爽."双碳"目标视角下的京津冀产业结构优化研究[J].河北经贸大学学报,2022,43(2):81-89.
    [27]
    张巍.基于STIRPAT模型的西安市碳足迹预测和情景分析[J].生态经济,2021,37(4):25-29.
    [28]
    ANSELIN L.Spatial Econometrics:Methods and Models[M].Springer Science & Business Media,1988.
    [29]
    李涛,薛领,李国平.产业集聚空间格局演变及其对经济高质量发展的影响:基于中国278个城市数据的实证分析[J].地理研究,2022,41(4):1092-1106.
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