ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Management 18 January 2023

Empirical analysis of influencing factors of carbon emissions in transportation industry in Fujian Province, China

  • 1.

    School of Economics and Trade, Fujian Jiangxia University, Fuzhou 350108, China

  • 2.

    School of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0079
More Information
  • Author Bio:

    Mingchun Zhong is an Associate Professor at Fujian Jiangxia University. He received his Ph.D. degree in Economics from Fujian Normal University in 2010. His research interests focus on environmental economics and industrial economics

    Guofu Lian is a Professor at Fujian University of Technology. He received his Ph.D. degree in Engineering from the University of Science and Technology of China in 2011. His research interests focus on additive manufacturing and industrial economics

  • Corresponding author: E-mail: gflian@mail.ustc.edu.cn
  • Received Date: 10 May 2022
  • Accepted Date: 10 July 2022
    • Available Online: 18 January 2023
    Abstract

  • Abstract

    The transportation industry has become an important source of carbon emissions with rapid economic development and the acceleration of urbanization. Identifying the key factors of carbon emissions is crucial for energy conservation, emission reduction and green development in the transportation industry. Here, variance analysis was used to study the influencing factors of carbon emissions in the transportation industry in Fujian Province, China. The results showed that transportation efficiency have the most significant impact on carbon emissions, followed by carbon emission intensity in transportation, and then the transportation structure. Meanwhile, there was a significant interaction between transportation efficiency and structure. Therefore, innovative energy-saving and emission-reduction technologies for transportation efficiency should be studied as the focus for the green and low-carbon development of the transportation industry.

    Graphical abstract

    Key factors decomposed by LMDI to study carbon emissions in the transportation industry.

    Abstract

    The transportation industry has become an important source of carbon emissions with rapid economic development and the acceleration of urbanization. Identifying the key factors of carbon emissions is crucial for energy conservation, emission reduction and green development in the transportation industry. Here, variance analysis was used to study the influencing factors of carbon emissions in the transportation industry in Fujian Province, China. The results showed that transportation efficiency have the most significant impact on carbon emissions, followed by carbon emission intensity in transportation, and then the transportation structure. Meanwhile, there was a significant interaction between transportation efficiency and structure. Therefore, innovative energy-saving and emission-reduction technologies for transportation efficiency should be studied as the focus for the green and low-carbon development of the transportation industry.

    • Public Summary

    • The variance analysis was used to investigate the influence factors of carbon emissions in the transportation industry in Fujian Province.
    • A mathematical regression model of carbon emissions was established by the stepwise method to reveal the influence of the transportation industry on carbon emissions and the interaction mechanism of various factors on carbon emissions.
    • This paper focuses on innovative energy-saving and emission-reduction technologies for transportation efficiency and provides suggestions to promote the green and low-carbon development of the transportation industry.

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    • References(30)
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    Zhang G X, Su Z X. Analysis of influencing factors and scenario prediction of transportation carbon emissions in the Yellow River Basin. Management Review, 2020, 32 (12): 283–294.
    [28]
    Li X Y, Li D. Driving factors and spatial pattern analysis of the transportation carbon emission in Henan Province. Journal of Lanzhou University: Natural Sciences, 2019, 55 (4): 430–435. doi: 10.13885/j.issn.0455-2059.2019.04.002
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    Zeng X Y, Qiu R Z, Lin D T, et al. Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China. China Environmental Science, 2020, 40 (10): 4304–4313. doi: 10.3969/j.issn.1000-6923.2020.10.013
    [30]
    Lyu Q. Study on the driving factors of vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. China Environmental Science, 2022, 38 (10): 3689–3697. doi: 10.3969/j.issn.1000-6923.2018.10.011
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    Figure  1.  Main effect of the carbon-emission-ratio model.

    Figure  2.  Carbon emissions and surface of transport efficiency and structure.

    [1]
    Danielis R, Scorrano M, Giansoldati M. Decarbonising transport in Europe: Trends, goals, policies and passenger car scenarios. Research in Transportation Economics, 2022, 91: 101068. doi: 10.1016/j.retrec.2021.101068
    [2]
    Balat M, Balat H. Recent trends in global production and utilization of bio-ethanol fuel. Applied Energy, 2009, 86: 2273–2282. doi: 10.1016/j.apenergy.2009.03.015
    [3]
    Liu Z, Guan D B, Wei W, et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature, 2015, 524 (7565): 335–338. doi: 10.1038/nature14677
    [4]
    Xu B Y, Wang T J, Li S, et al. Assessment of the impact of “dual-carbon” goal on future changes in air pollution and climate in China. Chinese Science Bulletin, 2022, 67: 784–794. doi: 10.1360/TB-2021-1091
    [5]
    Liu J, Li S, Ji Q. Regional differences and driving factors analysis of carbon emission intensity from transport sector in China. Energy, 2021, 224: 120178. doi: 10.1016/j.energy.2021.120178
    [6]
    Wang J T, Ma X M. Influencing factors of carbon emissions from transportation in China: Empirical analysis based on two-level econometrics method. Acta Scientiarum Naturalium Universitatis Pekinensis, 2021, 57 (6): 1133–1142. doi: 10.13209/j.0479-8023.2021.086
    [7]
    Yu J, DA Y B, Bin Q Y. Analysis of carbon emission changes in China’s transportation industry based on LMDI decomposition method. China Journal of Highway and Transport, 2015, 28 (10): 112–119. doi: 10.3969/j.issn.1001-7372.2015.10.015
    [8]
    Wang L B, Zhang Y. Factors decomposition and scenario prediction of energy-related CO2 emissions in China. Electric Power Construction, 2021 (9): 1–9. doi: 10.12204/j.issn.1000-7229.2021.09.001
    [9]
    Alshehry A S, Belloumi M. Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia. Renewable and Sustainable Energy Reviews, 2017, 75: 1339–1347. doi: 10.1016/j.rser.2016.11.122
    [10]
    Han X, Xu Y, Kumar A, et al. Decoupling analysis of transportation carbon emissions and economic growth in China. Environmental Progress & Sustainable Energy, 2018, 37 (5): 1696–1704. doi: 10.1002/ep.12857
    [11]
    Li Yi, Du Q, Lu X R, et al. Relationship between the development and CO2 emissions of transport sector in China. Transportation Research Part D, 2019, 74: 1–14. doi: 10.1016/j.trd.2019.07.011
    [12]
    Ru C S, Zhang S, Yuan C W. China’s transportation economy development and carbon environmental efficiency evaluation. China Journal of Highway and Transport, 2019, 32 (1): 154–161. doi: 10.3969/j.issn.1001-7372.2019.01.017
    [13]
    Yuan C W, Zhang S, Jiao P, et al. Temporal and spatial variation and influencing factors research on total factor efficiency for transportation carbon emissions in China. Resources Science, 2017, 39 (4): 687–697. doi: 10.18402/resci.2017.04.10
    [14]
    Lu J F, Fu H, Wang X X. Research on the impact of regional transportation emissions efficiency factors. Journal of Transportation Systems Engineering and Information Technology, 2016, 16 (2): 25–30. doi: 10.3969/j.issn.1009-6744.2016.02.006
    [15]
    Ma H, Sun W, Wang S, et al. Structural contribution and scenario simulation of highway passenger transit carbon emissions in the Beijing-Tianjin-Hebei metropolitan region, China. Resources Conservation and Recycling, 2019, 140: 209–215. doi: 10.1016/j.resconrec.2018.09.028
    [16]
    Xu L, Chen N, Chen Z. Will China make a difference in its carbon intensity reduction targets by 2020 and 2030? Applied Energy, 2017, 203 (10): 847–882. doi: 10.1016/j.apenergy.2017.06.087
    [17]
    Li Z, Li Y, Shao S. Analysis of influencing factors and trend forecast of carbon emission from energy consumption in China based on expanded STIRPAT model. Energies, 2019, 12 (16): 3054. doi: 10.3390/en12163054
    [18]
    Chen L, Wang J H, He T, et al. Forecast study of regional transportation carbon emissions based on SVR. Journal of Transportation Systems Engineering and Information Technology, 2018, 18 (2): 13–19. doi: 10.16097/j.cnki.1009-6744.2018.02.003
    [19]
    Ang B W. LMDI decomposition approach: A guide for implementation. Energy Policy, 2015, 86: 233–238. doi: 10.1016/j.enpol.2015.07.007
    [20]
    Wu K Y, He C H, Wang G X, et al. Measurement and decomposition analysis on carbon emissions of transportation industry in Shanghai. Economic Geography, 2012, 32 (11): 45–50. doi: 10.15957/j.cnki.jjdl.2012.11.008
    [21]
    Zhuang Y, Xia B. Estimation of CO2 emissions from the transport sector in Guangdong Province, China and analysis of factors affecting emissions. Research of Environmental Sciences, 2017, 30 (7): 1154–1162. doi: 10.13198/j.issn.1001-6929.2017.02.43
    [22]
    Guo M, Meng J. Exploring the driving factors of carbon dioxide emission from transport sector in Beijing-Tianjin-Hebei region. Journal of Cleaner Production, 2019, 226: 692–705. doi: 10.1016/j.jclepro.2019.04.095
    [23]
    Solaymani S. CO2 emissions patterns in 7 top carbon emitter economies: The case of transport sector. Energy, 2019, 168: 989–1001. doi: 10.1016/j.energy.2018.11.145
    [24]
    Zhang C, Zhang W, Luo W, et al. Analysis of influencing factors of carbon emissions in China’s logistics industry: A GDIM-based indicator decomposition. Energies, 2021, 14: 5742. doi: 10.3390/en14185742
    [25]
    Xu B, Lin B. Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: Evidence from a quantile regression model. Journal of Cleaner Production, 2018, 175: 109–122. doi: 10.1016/j.jclepro.2017.12.022
    [26]
    Lin B, Benjamin N I. Influencing factors on carbon emissions in China transport industry: A new evidence from quantile regression analysis. Journal of Cleaner Production, 2017, 150: 175–187. doi: 10.1016/j.jclepro.2017.02.171
    [27]
    Zhang G X, Su Z X. Analysis of influencing factors and scenario prediction of transportation carbon emissions in the Yellow River Basin. Management Review, 2020, 32 (12): 283–294.
    [28]
    Li X Y, Li D. Driving factors and spatial pattern analysis of the transportation carbon emission in Henan Province. Journal of Lanzhou University: Natural Sciences, 2019, 55 (4): 430–435. doi: 10.13885/j.issn.0455-2059.2019.04.002
    [29]
    Zeng X Y, Qiu R Z, Lin D T, et al. Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China. China Environmental Science, 2020, 40 (10): 4304–4313. doi: 10.3969/j.issn.1000-6923.2020.10.013
    [30]
    Lyu Q. Study on the driving factors of vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. China Environmental Science, 2022, 38 (10): 3689–3697. doi: 10.3969/j.issn.1000-6923.2018.10.011
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