The ambition towards net-zero emission fuels the significance of electric vehicle charging infrastructure (EVCI) as a strategic asset. Yet, a conspicuous gap remains in the comprehensive quantitative analysis of its impact on carbon emissions stemming from fossil fuel combustion, referred to as ODIAC-CE. This study embarks on a longitudinal comparison of EVCI and ODIAC-CE data in 2018 and 2020, further classifying cities by scale to analyze the association between expansion of EVCI and ODIAC-CE change. Utilizing a battery of analytical tools, including correlation analysis, spatial autocorrelation, and coupling coordination analysis, the study dissects the evolving relationship between EVCI and ODIAC-CE within Yangtze River Delta in China. The results underscore a growing interdependence between EVCI expansion and ODIAC-CE change, yet pronounced heterogeneities in coupling coordination are evident across urban scales. Megacity and supercity exhibit quality coordination between rapid expansion of EVCI and ODIAC-CE dynamics. However, in most large, medium-sized, and small cities, the impact of EVCI growth on ODIAC-CE change has proven to be inconsistent or mismatched, affected by various factors such as location and infrastructure, industrial and technological patterns, and social practice and awareness. The study provides systematic insights into potential solutions for decarbonization through EVCI deployment at regional and city levels.
AkbariMBrennaMLongoM (2018) Optimal locating of electric vehicle charging stations by application of genetic algorithm. Sustainability10(4): 1076. DOI: 10.3390/su10041076.
2.
AkogluH (2018) User's guide to correlation coefficients. Turkish Journal of Emergency Medicine18(3): 91–93. DOI: 10.1016/j.tjem.2018.08.001.
ChenFShenXWangZ, et al. (2017) An evaluation of the low-carbon effects of urban rail based on mode shifts. Sustainability9(3): 401. DOI: 10.3390/su9030401.
5.
CHINASAE (2021) Research on the Development Strategy and Roadmap of Electric Vehicle Charging Infrastructure in China (2021-2035). Beijing: China Society of Automotive Engineers(SAE). Available at:https://www.efchina.org/Attachments/Report/report-ctp-20211015
FengTZhouB (2023) Impact of urban spatial structure elements on carbon emissions efficiency in growing megacities: the case of Chengdu. Scientific Reports13: 9939. DOI: 10.1038/s41598-023-36575-6.
9.
GhasriMArdeshiriARashidiT (2019) Perception towards electric vehicles and the impact on consumers’ preference. Transportation Research Part D: Transport and Environment77: 271–291. DOI: 10.1016/j.trd.2019.11.003.
10.
HamurcuMErenT (2023) Multicriteria decision making and goal programming for determination of electric automobile aimed at sustainable green environment: a case study. Environment systems & decisions43(2): 211–231. DOI: 10.1007/s10669-022-09878-8.
11.
HuangQ (2020) Inspirations of the Wuhan lockdown on global urban public health under the outbreak of COVID-19 epidemic. Sustainable Development10(3): 381–388. DOI: 10.12677/SD.2020.103046.
12.
HuangQ (2024) Urban adaptability evaluation and the ADI framework: pioneering sustainable, resilient, and prosperous cities. International Journal of Sustainable Development and Planning19(1): 23–40. DOI: 10.18280/ijsdp.190103.
HuangWLiFCuiS, et al. (2017) Carbon footprint and carbon emission reduction of urban buildings: a case in Xiamen city, China. Procedia Engineering198: 1007–1017. DOI: 10.1016/j.proeng.2017.07.146.
LeeRBrownS (2021) Social & locational impacts on electric vehicle ownership and charging profiles. Energy Reports7(2): 42–48. DOI: 10.1016/j.egyr.2021.02.057.
18.
LiGLuoTSongY (2022) Climate change mitigation efficiency of electric vehicle charging infrastructure in China: from the perspective of energy transition and circular economy. Resources, Conservation and Recycling179: 106048. DOI: 10.1016/j.resconrec.2021.106048.
19.
LiLFanZFengW, et al. (2022) Coupling coordination degree spatial analysis and driving factor between socio-economic and eco-environment in northern China. Ecological Indicators135: 108555. DOI: 10.1016/j.ecolind.2022.108555.
20.
LiuXOuJWangS, et al. (2018) Estimating spatiotemporal variations of city-level energy-related CO2 emissions: an improved disaggregating model based on vegetation adjusted nighttime light data. Journal of Cleaner Production177: 101–114. DOI: 10.1016/j.jclepro.2017.12.197.
21.
MaierdanYArmisteadSJMikofskyRA, et al. (2024) Rheology and 3D printing of alginate bio-stabilized earth concrete. Cement and Concrete Research175: 107380. DOI: 10.1016/j.cemconres.2023.107380.
22.
MastoiMSZhuangSMunirHM, et al. (2022) An in-depth analysis of electric vehicle charging station infrastructure, policy implications, and future trends. Energy Reports8: 11504–11529. DOI: 10.1016/j.egyr.2022.09.011.
23.
OrdJKGetisA (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geographic Analysis27(4): 286–306. DOI: 10.1111/j.1538-4632.1995.tb00912.x.
24.
PeroFDDeloguMPieriniM (2018) Life Cycle Assessment in the automotive sector: a comparative case study of Internal Combustion Engine (ICE) and electric car. Procedia Structural Integrity12: 521–537. DOI: 10.1016/j.prostr.2018.11.066.
25.
QadirSAAhmadFMohsin A B Al-WahediA, et al. (2024) Navigating the complex realities of electric vehicle adoption: a comprehensive study of government strategies, policies, and incentives. Energy Strategy Reviews53: 101379. DOI: 10.1016/j.esr.2024.101379.
26.
QiaoFYangQShiW, et al. (2024) Research on driving mechanism and prediction of electric power carbon emission in Gansu Province under dual-carbon target. Scientific Reports14: 6103. DOI: 10.1038/s41598-024-55721-2.
27.
QinHHuangQZhangZ, et al. (2019) Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: combining geographically weighted regression with two-step cluster. Science of the Total Environment684: 413–424. DOI: 10.1016/j.scitotenv.2019.05.352.
28.
SunXMiZSudmantA, et al. (2022) Using crowdsourced data to estimate the carbon footprints of global cities. Advances in Applied Energy8: 100111. DOI: 10.1016/j.adapen.2022.100111.
29.
TuRGaiYFarooqB, et al. (2020) Electric vehicle charging optimization to minimize marginal greenhouse gas emissions from power generation. Applied Energy277: 115517. DOI: 10.1016/j.apenergy.2020.115517.
30.
VezaIAsyariMZIdrisM, et al. (2023) Electric vehicle (EV) and driving towards sustainability: comparison between EV, HEV, PHEV, and ICE vehicles to achieve net zero emissions by 2050 from EV. Alexandria Engineering Journal82: 459–467. DOI: 10.1016/j.aej.2023.10.020.
31.
WangHLuXDengY, et al. (2019) China’s CO2 peak before 2030 implied from characteristics and growth of cities. Nature Sustainability2(8): 748–754. DOI: 10.1038/s41893-019-0339-6.
32.
WangWYangHXiangC (2023) Green roofs and facades with integrated photovoltaic system for zero energy eco-friendly building -A review. Sustainable Energy Technologies and Assessments60. DOI: 10.1016/j.seta.2023.103426.
33.
WeiZLiJWangZ, et al. (2022) County carbon emissions in the Yangtze River Delta region: spatial layout, dynamic evolution and spatial spillover effects. Frontiers in Environmental Science10: 977198. DOI: 10.3389/fenvs.2022.977198.
34.
WuNShenLZhongS (2019) Spatio-temporal pattern of carbon emissions based on nightlight data of the Shanxi-Shaanxi-Inner Mongolia region of China. Journal of Geo-information Science21(7): 1040–1050. DOI: 10.11821/dlxb201311007.
35.
XiangCMatusiakBS (2019) Facade Integrated Photovoltaic, state of the art of Experimental Methodology. IOP Conference Series: Earth and Environmental Science. doi: 10.1088/1755-1315/352/1/012062
36.
YangDLuanWQiaoL, et al. (2020) Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery. Applied Energy268: 114696. DOI: 10.1016/j.apenergy.2020.114696.
37.
YuXWuZZhengH, et al. (2020) How urban agglomeration improve the emission efficiency? A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China. Journal of Environmental Management260: 110061. DOI: 10.1016/j.jenvman.2019.110061.
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