Abstract
The number of cars is increasing every year and the environmental aspects of transport are becoming a hot topic. The spatial and temporal patterns of motor vehicle carbon monoxide (CO) emissions are still unclear due to the unbalanced economic development and heterogeneous geographic conditions of China. With the objective of realizing a reduction in motor vehicle CO emissions, his study explores the transport carbon emission reduction pathways of China from motor vehicle CO emission. Firstly, the entropy method is adopted to comprehensively evaluate the CO emissions from motor vehicles in each province; secondly, the development of a Geographically and Temporally Weighted Regression (GTWR) model facilitates the examination of the spatiotemporal dynamics pertaining to the influencing factors of motor vehicle CO emissions within each province.; finally, the characteristics of motor vehicle CO emissions in ETS pilot areas and non-ETS pilot areas are compared. The results show that: (1) After the completion of the six ETS pilot areas in 2011, the CO emission from motor vehicles is reduced by 18% compared with 2010.(2)The entropy method shows that the largest CO emissions from motor vehicles are from Beijing, Shanghai, Guangdong and other provinces with high economic levels.(3) The results of the GTWR model show that the positive effects of economic level, population size, road mileage intensity and motor vehicle intensity on motor vehicle CO emissions are decreasing year by year. The negative effect of metro line intensity on CO emission decreases year by year. This study can help decision makers to identify the high emission areas, understand the influencing factors, and formulate emission reduction measures to achieve the purpose of carbon emission reduction in transport.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The raw/processed data required to reproduce the findings in the current manuscript cannot be shared at this time as these data also form a part of an ongoing study.
References
Akaike H (1974) A New Look at the Statistical Model Identification. ITAC 19:716–723. https://doi.org/10.1109/TAC.1974.1100705
Amiri V, Rezaei M, Sohrabi N (2014) Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environ Earth Sci 72:3479–3490. https://doi.org/10.1007/s12665-014-3255-0
Anselin L (1995) Local Indicators of Spatial Association-LISA. Geogr Anal 27:93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Breiman L, Friedman JH (1997) Predicting multivariate responses in multiple linear regression. J R Stat Soc Ser B Stat Methodol 59:3–54. https://doi.org/10.1111/1467-9868.00054
Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geogr Anal 28:281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Brunsdon C, Fotheringham AS, Charlton M (2002) Geographically weighted summary statistics — aframework for localised exploratory data. Comput Environ Urban Syst 26:501–524. https://doi.org/10.1016/S0198-9715(01)00009-6
Buckeridge DL, Glazier R, Harvey BJ, Escobar M, Amrhein C, Frank J (2002) Effect of Motor Vehicle Emissions on Respiratory Health in an Urban Area. Environ Health Perspect 110:293–300. https://doi.org/10.1289/ehp.02110293
Chen SK, Wei W, Mao BH, Guan W (2013) Analysis on urban traffic status based on improved spatio-temporal Moran’s I. AcPSn 62:148901. https://doi.org/10.7498/aps.62.148901
Chen B, Chen F, Ciais P, Zhang H, Lü H, Wang T, Chevallier F, Liu Z, Yuan W, Peters W (2022) Challenges to achieve carbon neutrality of China by 2060: status and perspectives. Sci Bull 67:2030–2035. https://doi.org/10.1016/j.scib.2022.08.025
Dong F, Dai Y, Zhang S, Zhang X, Long R (2019) Can a carbon emission trading scheme generate the Porter effect? Evidence from pilot areas in China. Sci Total Environ 653:565–577. https://doi.org/10.1016/j.scitotenv.2018.10.395
Fan F, Lei Y (2016) Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing. Transp Res Part d: Transport Environ 42:135–145. https://doi.org/10.1016/j.trd.2015.11.001
Fan F, Lei Y (2017) Factor analysis of energy-related carbon emissions: a case study of Beijing. J Clean Prod 163:S277–S283. https://doi.org/10.1016/j.jclepro.2015.07.094
Fotheringham AS, Crespo R, Yao J (2015) Geographical and Temporal Weighted Regression (GTWR). Geogr Anal 47:431–452. https://doi.org/10.1111/gean.12071
Fu W, Zhao K, Zhang C et al (2011) Using Moran’s I and geostatistics to identify spatial patterns of soil nutrients in two different long-term phosphorus-application plots. J Plant Nutr Soil Sci 174:785–798. https://doi.org/10.1002/jpln.201000422
Fu WJ, Jiang PK, Zhou GM, Zhao KL (2014) Using Moran’s I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Bgeo 11:2401–2409. https://doi.org/10.5194/bg-11-2401-2014
Gao Y, Li M, Xue J, Liu Y (2020) Evaluation of effectiveness of China's carbon emissions trading scheme in carbon mitigation. Energy Econ 90. https://doi.org/10.1016/j.eneco.2020.104872
Getis, A. (2010) Spatial Autocorrelation. Handbook of Applied Spatial Analysis, pp. 255–278.
Goldberger AS (1964) Econometric theory. John Wiley & Sons, NewYork
Gorgij AD, Kisi O, Moghaddam AA, Taghipour A (2017) Groundwater quality ranking for drinking purposes, using the entropy method and the spatial autocorrelation index. Environ Earth Sci 76. https://doi.org/10.1007/s12665-017-6589-6
Guo M, Meng J (2019) Exploring the driving factors of carbon dioxide emission from transport sector in Beijing-Tianjin-Hebei region. J Clean Prod 226:692–705. https://doi.org/10.1016/j.jclepro.2019.04.095
Guo B, Geng Y, Franke B, Hao H, Liu Y, Chiu A (2014) Uncovering China’s transport CO2 emission patterns at the regional level. Energy Policy 74:134–146. https://doi.org/10.1016/j.enpol.2014.08.005
Hao Y, Gao C, Deng S, Yuan M, Song W, Lu Z, Qiu Z (2019) Chemical characterisation of PM2.5 emitted from motor vehicles powered by diesel, gasoline, natural gas and methanol fuel. Sci Total Environ 674:128–139. https://doi.org/10.1016/j.scitotenv.2019.03.410
Huang B, Wu B, Barry M (2010) Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int J Geogr Inf Sci 24:383–401. https://doi.org/10.1080/13658810802672469
Huang Y, Surawski NC, Organ B, Zhou JL, Tang OHH, Chan EFC (2019) Fuel consumption and emissions performance under real driving: Comparison between hybrid and conventional vehicles. Sci Total Environ 659:275–282. https://doi.org/10.1016/j.scitotenv.2018.12.349
Huang J, Shen J, Miao L, Zhang W (2021) The effects of emission trading scheme on industrial output and air pollution emissions under city heterogeneity in China. J Clean Prod 315. https://doi.org/10.1016/j.jclepro.2021.128260
Hurvich CM, Simonoff JS, Tsai CL (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc Ser B Stat Methodol 60:271–293. https://doi.org/10.1111/1467-9868.00125
Islam A, Ahmed N, Bodrud-Doza M, Chu R (2017) Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics. Environ Sci Pollut Res Int 24:26350–26374. https://doi.org/10.1007/s11356-017-0254-1
Jiang J, Xie D, Ye B, Shen B, Chen Z (2016) Research on China’s cap-and-trade carbon emission trading scheme: Overview and outlook. Appl Energ 178:902–917. https://doi.org/10.1016/j.apenergy.2016.06.100
Kutner MH, Nachtsheim CJ, Neter J (2004) Applied Linear Regression Models, fifth ed. Technometrics
Li Y, Du Q, Lu X, Wu J, Han X (2019) Relationship between the development and CO2 emissions of transport sector in China. Transp Res Part D: Transport Environ 74:1–14. https://doi.org/10.1016/j.trd.2019.07.011
Li S, Lang J, Zhou Y, Liang X, Chen D, Wei P (2020) Trends in ammonia emissions from light-duty gasoline vehicles in China, 1999–2017. Sci Total Environ 700. https://doi.org/10.1016/j.scitotenv.2019.134359
Li Y, Li T, Lu S (2021) Forecast of urban traffic carbon emission and analysis of influencing factors. Energy Eff 14. https://doi.org/10.1007/s12053-021-10001-0
Lin D, Zhang L, Chen C, Lin Y, Wang J, Qiu R, Hu X (2019) Understanding driving patterns of carbon emissions from the transport sector in China: evidence from an analysis of panel models. Clean Technol Environ Policy 21:1307–1322. https://doi.org/10.1007/s10098-019-01707-y
Link C, Stark J, Sonntag A, Hössinger R (2012) Contribution of an Emission Trading Scheme to Reduce Road Traffic Induced CO2 Emissions in Austria. Procedia Soc Behav Sci 48:1971–1982. https://doi.org/10.1016/j.sbspro.2012.06.1170
Liu Z, Deng Z, Davis SJ, Giron C, Ciais P (2022) Monitoring global carbon emissions in 2021. Na Rev Earth Environ 3:217–219. https://doi.org/10.1038/s43017-022-00285-w
Lu H, Zhu Y, Qi Y, Yu J (2018) Do Urban Subway Openings Reduce PM2.5 Concentrations? Evidence from China. Sustainability 10. https://doi.org/10.3390/su10114147
Luo X, Dong L, Dou Y, Li Y, Liu K, Ren J, Liang H, Mai X (2017) Factor decomposition analysis and causal mechanism investigation on urban transport CO2 emissions: Comparative study on Shanghai and Tokyo. Energy Policy 107:658–668. https://doi.org/10.1016/j.enpol.2017.02.049
Ma X, Ji Y, Yuan Y, Van Oort N, Jin Y, Hoogendoorn S (2020) A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data. Transp Res Part A Policy Pr 139:148–173. https://doi.org/10.1016/j.tra.2020.06.022
McDonald BC, Gentner DR, Goldstein AH, Harley RA (2013) Long-Term Trends in Motor Vehicle Emissions in U.S. Urban Areas Environ Sci Technol 47:10022–10031. https://doi.org/10.1021/es401034z
MEEPRC (2022) Response to Recommendation No. 3124 of the Fifth Session of the 13th National People's Congress. Ministry of Ecology and Environment of the People's Republic of China (MEEPRC), https://www.mee.gov.cn/xxgk2018/xxgk/xxgk13/202301/t20230117_1013332.html
Miller HJ (2004) Tobler’s first law and spatial analysis. Ann Assoc Am Geogr 94:284–289. https://doi.org/10.1111/j.1467-8306.2004.09402005.x
Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23. https://doi.org/10.2307/2332142
O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690. https://doi.org/10.1007/s11135-006-9018-6
Ouyang S, Liu ZW, Li Q, Shi YL (2013) A New Improved Entropy Method and its Application in Power Quality Evaluation. Adv Mater Res 706–708:1726–1733. https://doi.org/10.4028/www.scientific.net/AMR.706-708.1726
Shabbir R, Ahmad SS (2010) Monitoring urban transport air pollution and energy demand in Rawalpindi and Islamabad using leap model. Energy 35:2323–2332. https://doi.org/10.1016/j.energy.2010.02.025
Shannon CE (1948) A mathematical theory of communication. Bell Syst Techn J 27:379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Sheng Q, Liu Y, Zhan Y, Wan Q, Fan L (2023) Urban transportation network allocation considering heterogeneous users in the context of carbon trading. ICTETS 2022, Guangzhou, p 1259134
Shi W, Hou J, Shen X, Xiang R (2022) Exploring the Spatio-Temporal Characteristics of Urban Thermal Environment during Hot Summer Days: A Case Study of Wuhan, China. RSEMS 14:6084. https://doi.org/10.3390/rs14236084
Singh RB, Huber AH (2011) Sensitivity Analysis and Evaluation of MicroFacCO: A Microscale Motor Vehicle Emission Factor Model for CO Emissions. J Air Waste Manag Assoc 51:1087–1099. https://doi.org/10.1080/10473289.2001.10464327
Stogios C, Kasraian D, Roorda MJ, Hatzopoulou M (2019) Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions. Transp Res Part D: Transport Environ 76:176–192. https://doi.org/10.1016/j.trd.2019.09.020
Thomas L (2015) CO2 Intensity and the Importance of Country Level Differences: An Analysis of the Relationship between per Capita Emissions and Population Density. FEEM Working Paper No. 047.2015. https://doi.org/10.2139/ssrn.2609298
Tobler WR (1970) A Computer Movie Simulating Urban Growth in the Detroit Region. Econ Geogr 46:234–240
Wang H, Ou X, Zhang X (2017) Mode, technology, energy consumption, and resulting CO2 emissions in China’s transport sector up to 2050. Energy Policy 109:719–733. https://doi.org/10.1016/j.enpol.2017.07.010
Wang H, Shi W, He Y, Dong J (2022) Spill-over effect and efficiency of seven pilot carbon emissions trading exchanges in China. Sci Total Environ 838. https://doi.org/10.1016/j.scitotenv.2022.156020
Wen Y, Zhang S, Zhang J, Bao S, Wu X, Yang D, Wu Y (2020) Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data. Appl Energ 260. https://doi.org/10.1016/j.apenergy.2019.114357
Xu B, Lin B (2016) Differences in regional emissions in China’s transport sector: Determinants and reduction strategies. Energy 95:459–470. https://doi.org/10.1016/j.energy.2015.12.016
Yan Y, Zhang X, Zhang J, Li K (2020) Emissions trading system (ETS) implementation and its collaborative governance effects on air pollution: The China story. Energy Policy 138. https://doi.org/10.1016/j.enpol.2020.111282
Yang Y, Yuan Z, Chen JJ, Guo M (2017) Assessment of osculating value method based on entropy weight to transportation energy conservation and emission reduction. Environ Eng Manag J 16:2413–2424. https://doi.org/10.30638/eemj.2017.249
Zhang C, Luo L, Xu W, Ledwith V (2008) Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Total Environ 398:212–221. https://doi.org/10.1016/j.scitotenv.2008.03.011
Zhang X, Wang C, Li E, Xu C (2014) Assessment model of ecoenvironmental vulnerability based on improved entropy weight method. ScientificWorldJournal 2014:797814. https://doi.org/10.1155/2014/797814
Zhang H, Liu Y (2022) Can the pilot emission trading system coordinate the relationship between emission reduction and economic development goals in China?. Jo Clean Prod 363. https://doi.org/10.1016/j.jclepro.2022.132629
Zhao J, Ji G, Tian Y, Chen Y, Wang Z (2018) Environmental vulnerability assessment for mainland China based on entropy method. Ecol Ind 91:410–422. https://doi.org/10.1016/j.ecolind.2018.04.016
Funding
This work was supported by National Natural Science Foundation of China (grant numbers 71671079 and 71361018).
Author information
Authors and Affiliations
Contributions
Conceptualization: Shuqin Zhao and Linzhong Liu; Methodology: Shuqin Zhao; Data curation: Shuqin Zhao and Ping Zhao; Writing—original draft preparation: Shuqin Zhao; Writing—review and editing: Linzhong Liu and Ping Zhao; Supervision: Linzhong Liu.
Corresponding author
Ethics declarations
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent to publish
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: V.V.S.S. Sarma
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhao, S., Liu, L. & Zhao, P. Spatial and temporal analysis of influential factors on motor vehicle carbon monoxide emissions in China considering emissions trading scheme. Environ Sci Pollut Res 31, 9811–9830 (2024). https://doi.org/10.1007/s11356-024-31880-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-024-31880-7