Analysis of carbon emission drivers and peak carbon forecasts for island economies

被引:6
作者
Wang, Geng [1 ,2 ]
Feng, Yan [2 ]
机构
[1] Liaoning Normal Univ, Ctr Studies Marine Econ & Sustainable Dev, Key Res Base Humanities & Social Sci, Minist Educ, Dalian 116029, Peoples R China
[2] Liaoning Normal Univ, Sch Geog Sci, Dalian 116029, Peoples R China
关键词
Island economies; System dynamics; Carbon emissions; LMDI method; MODEL;
D O I
10.1016/j.ecolmodel.2023.110611
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Sea islands are an essential support for expanding the blue economic space, and the study of the carbon emissions of Changhai County's island economies and their carbon peaking time is of great significance for achieving the goal of building an ecological low-carbon demonstration island with northern characteristics in Changhai County. In this study, the Logarithmic Mean Divisia Index (LMDI) method was used to explore the three main drivers affecting the carbon emissions of the island economies in Changhai County. The carbon emissions of Changhai County in 2012-2019 were estimated through the system dynamics model. The low-carbon development path and possible carbon peak time of Changhai County in 2020-2035 were predicted. The results show that: (1)The urban system is the first positive driver of carbon emissions, followed by the residential system, and finally, the fishery system, the energy structure effect is the main positive driving effect affecting carbon emissions, and the population effect always shows a negative inhibitory impact on carbon emissions. (2)The activities on the islands of Changhai County in 2012-2019 cumulatively produced 167.53 x 10(4) t of carbon emissions, 46.83 x 10(4) t of carbon sinks, and their net carbon emissions were 120.69 x 10(4) t. The results of the future scenario projections show that neither non-intervention nor a single policy of only adjusting land use and transport can achieve minor carbon emissions and that the best path for future low-carbon development in Changhai County is to develop all kinds of activities in a low-carbon (L) mode. (4)Among the future forecast scenarios, Scenario 5 is an ideal model for the fastest way to achieve carbon peak, which generates 318.39 x 10(4) t of carbon emissions, and the cumulative emission reduction reaches 26.37 x 10(4) t.
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页数:10
相关论文
共 37 条
[11]   Structural and social-economic determinants of China's transport low-carbon development under the background of aging and industrial migration [J].
Feng, Chao ;
Xia, Yin-Shuang ;
Sun, Lu-Xuan .
ENVIRONMENTAL RESEARCH, 2020, 188
[12]   System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China [J].
Feng, Y. Y. ;
Chen, S. Q. ;
Zhang, L. X. .
ECOLOGICAL MODELLING, 2013, 252 :44-52
[13]   The characteristics and drivers of China's city-level urban-rural activity sectors' carbon intensity gap during urban land expansion [J].
Gao, Ming ;
Ma, Ke ;
Yu, Jie .
ENERGY POLICY, 2023, 181
[14]   Coupled LMDI and system dynamics model for estimating urban CO2 emission mitigation potential in Shanghai, China [J].
Gu, Shuai ;
Fu, Bitian ;
Thriveni, Thenepalli ;
Fujita, Toyohisa ;
Ahn, Ji Whan .
JOURNAL OF CLEANER PRODUCTION, 2019, 240
[15]   Sustainable lifestyle: Urban household carbon footprint accounting and policy implications for lifestyle-based decarbonization [J].
Huang, Liqiao ;
Long, Yin ;
Chen, Jundong ;
Yoshida, Yoshikuni .
ENERGY POLICY, 2023, 181
[16]   How does heterogeneous environmental regulation affect net carbon emissions: Spatial and threshold analysis for China [J].
Huang, Xiaoling ;
Tian, Peng .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2023, 330
[17]  
Intergovernmental Panel on Climate Change (IPCC), 2014, Climate Change 2014: Mitigation of Climate Change, DOI [DOI 10.1017/CBO9780511546013, 10.1017/CBO9780511546013]
[18]   Analysing driving factors of India's transportation sector CO2 emissions: Based on LMDI decomposition method [J].
Jain, Siddharth ;
Rankavat, Shalini .
HELIYON, 2023, 9 (09)
[19]   Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions-Utilizing multi-source remote sensing data and GTWR model [J].
Jiang, Fangming ;
Chen, Binjie ;
Li, Penghan ;
Jiang, Jiawen ;
Zhang, Qingyu ;
Wang, Jinnan ;
Deng, Jinsong .
ENVIRONMENTAL RESEARCH, 2023, 229
[20]  
[焦念志 Jiao Nianzhi], 2021, [中国科学院院刊, Bulletin of the Chinese Academy of Sciences], V36, P179