[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i4p800-d1113357.html
   My bibliography  Save this article

The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies

Author

Listed:
  • Haiyan Lei

    (School of Architecture, Tianjin University, Tianjin 300072, China
    Research Institute of Architectural Design and Urban Planning, Tianjin University, Tianjin 300072, China)

  • Suiping Zeng

    (School of Architecture, Tianjin Chengjian University, Tianjin 300384, China)

  • Aihemaiti Namaiti

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Jian Zeng

    (School of Architecture, Tianjin University, Tianjin 300072, China)

Abstract
Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R 2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas.

Suggested Citation

  • Haiyan Lei & Suiping Zeng & Aihemaiti Namaiti & Jian Zeng, 2023. "The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies," Land, MDPI, vol. 12(4), pages 1-20, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:800-:d:1113357
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/4/800/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/4/800/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yanchun Yi & Yajun Wang & Yaqin Li & Ji Qi, 2021. "Impact of urban density on carbon emissions in China," Applied Economics, Taylor & Francis Journals, vol. 53(53), pages 6153-6165, November.
    2. Georgios Maris & Floros Flouros, 2021. "The Green Deal, National Energy and Climate Plans in Europe: Member States’ Compliance and Strategies," Administrative Sciences, MDPI, vol. 11(3), pages 1-17, July.
    3. Shaohua Wang & Yanyan Chen & Jianling Huang & Ning Chen & Yao Lu, 2019. "Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, May.
    4. Pedro Dorta Antequera & Jaime Díaz Pacheco & Abel López Díez & Celia Bethencourt Herrera, 2021. "Tourism, Transport and Climate Change: The Carbon Footprint of International Air Traffic on Islands," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    5. Rui Jiang & Peng Wu & Chengke Wu, 2022. "Driving Factors behind Energy-Related Carbon Emissions in the U.S. Road Transport Sector: A Decomposition Analysis," IJERPH, MDPI, vol. 19(4), pages 1-17, February.
    6. Chen Li & Le Zhang & Qinyi Gu & Jia Guo & Yi Huang, 2022. "Spatio-Temporal Differentiation Characteristics and Urbanization Factors of Urban Household Carbon Emissions in China," IJERPH, MDPI, vol. 19(8), pages 1-22, April.
    7. Cheng, Yung-Hsiang & Chang, Yu-Hern & Lu, I.J., 2015. "Urban transportation energy and carbon dioxide emission reduction strategies," Applied Energy, Elsevier, vol. 157(C), pages 953-973.
    8. Lee, Sungwon & Lee, Bumsoo, 2014. "The influence of urban form on GHG emissions in the U.S. household sector," Energy Policy, Elsevier, vol. 68(C), pages 534-549.
    9. Saboori, Behnaz & Sapri, Maimunah & bin Baba, Maizan, 2014. "Economic growth, energy consumption and CO2 emissions in OECD (Organization for Economic Co-operation and Development)'s transport sector: A fully modified bi-directional relationship approach," Energy, Elsevier, vol. 66(C), pages 150-161.
    10. Hanhua Shao & Jixin Cheng & Yuansheng Wang & Xiaoming Li, 2022. "Can Digital Finance Promote Comprehensive Carbon Emission Performance? Evidence from Chinese Cities," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    11. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    12. Sheng Zheng & Yukuan Huang & Yu Sun, 2022. "Effects of Urban Form on Carbon Emissions in China: Implications for Low-Carbon Urban Planning," Land, MDPI, vol. 11(8), pages 1-17, August.
    13. Changzheng Zhu & Wenbo Du, 2019. "A Research on Driving Factors of Carbon Emissions of Road Transportation Industry in Six Asia-Pacific Countries Based on the LMDI Decomposition Method," Energies, MDPI, vol. 12(21), pages 1-19, October.
    14. Changlong Sun & Yongli Zhang & Wenwen Ma & Rong Wu & Shaojian Wang, 2022. "The Impacts of Urban Form on Carbon Emissions: A Comprehensive Review," Land, MDPI, vol. 11(9), pages 1-20, August.
    15. Underwood, Anthony & Fremstad, Anders, 2018. "Does sharing backfire? A decomposition of household and urban economies in CO2 emissions," Energy Policy, Elsevier, vol. 123(C), pages 404-413.
    16. Mariaflavia Harari, 2020. "Cities in Bad Shape: Urban Geometry in India," American Economic Review, American Economic Association, vol. 110(8), pages 2377-2421, August.
    17. Dong, Zhaoyingzi & Xia, Chuyu & Fang, Kai & Zhang, Weiwen, 2022. "Effect of the carbon emissions trading policy on the co-benefits of carbon emissions reduction and air pollution control," Energy Policy, Elsevier, vol. 165(C).
    18. Aditjandra, Paulus Teguh & Mulley, Corinne & Nelson, John D., 2013. "The influence of neighbourhood design on travel behaviour: Empirical evidence from North East England," Transport Policy, Elsevier, vol. 26(C), pages 54-65.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinju Kim & Jaecheol Kim & Sangkyeong Lee, 2024. "Impact of Spatial Characteristics on Gendered Retail Consumption in Seoul: A Gender-Sensitive Urban Planning Perspective," Sustainability, MDPI, vol. 16(14), pages 1-23, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chengye Jia & Shuang Feng & Hong Chu & Weige Huang, 2023. "The Heterogeneous Effects of Urban Form on CO 2 Emissions: An Empirical Analysis of 255 Cities in China," Land, MDPI, vol. 12(5), pages 1-24, April.
    2. Lyu Jun & Shuang Lu & Xiang Li & Zeng Li & Chenglong Cao, 2023. "Spatio-Temporal Characteristics of Industrial Carbon Emission Efficiency and Their Impacts from Digital Economy at Chinese Prefecture-Level Cities," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    3. Yu, Shiwei & Agbemabiese, Lawrence & Zhang, Junjie, 2016. "Estimating the carbon abatement potential of economic sectors in China," Applied Energy, Elsevier, vol. 165(C), pages 107-118.
    4. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
    5. Na Yu & Jianghua Chen & Lei Cheng, 2022. "Evolutionary Game Analysis of Carbon Emission Reduction between Government and Enterprises under Carbon Quota Trading Policy," IJERPH, MDPI, vol. 19(14), pages 1-22, July.
    6. Yukun Ma & Shaojian Wang & Chunshan Zhou, 2023. "Can the Development of the Digital Economy Reduce Urban Carbon Emissions? Case Study of Guangdong Province," Land, MDPI, vol. 12(4), pages 1-13, March.
    7. Xiaoli Wu & Yaoyao Qin & Qizhuo Xie & Yunyi Zhang, 2022. "The Mediating and Moderating Effects of the Digital Economy on PM 2.5 : Evidence from China," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    8. Wu, Rong & Wang, Jieyu & Wang, Shaojian & Feng, Kuishuang, 2021. "The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Ke Luo & Shuo Chen & Shixi Cui & Yuantao Liao & Yu He & Chunshan Zhou & Shaojian Wang, 2023. "Examining the Overall and Heterogeneous Impacts of Urban Spatial Structure on Carbon Emissions: A Case Study of Guangdong Province, China," Land, MDPI, vol. 12(9), pages 1-19, September.
    10. Song, Siqi & Diao, Mi & Feng, Chen-Chieh, 2016. "Individual transport emissions and the built environment: A structural equation modelling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 206-219.
    11. Xiaochun Zhao & Huixin Xu & Qun Sun, 2022. "Research on China’s Carbon Emission Efficiency and Its Regional Differences," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    12. Taehyun Kim & Youngre Noh, 2024. "Planning factors affecting carbon footprints of residents: Density, land use, and suburbanization," Environment and Planning B, , vol. 51(1), pages 157-173, January.
    13. Yahui Guang & Yongbin Huang, 2022. "Urban Form and Household Energy Consumption: Evidence from China Panel Data," Land, MDPI, vol. 11(8), pages 1-15, August.
    14. Yao Xu & Liang Sun & Bo Wang & Shanmin Ding & Xichen Ge & Shuangrong Cai, 2023. "Research on the Impact of Carbon Emissions and Spatial Form of Town Construction Land: A Study of Macheng, China," Land, MDPI, vol. 12(7), pages 1-23, July.
    15. Xu Yang & Xuan Zou & Xueqi Liu & Qixuan Li & Siqian Zou & Ming Li, 2023. "The Spatiotemporal Pattern and Driving Mechanism of Urban Sprawl in China’s Counties," Land, MDPI, vol. 12(3), pages 1-16, March.
    16. Lingzhang Kong & Jinye Li, 2022. "Digital Economy Development and Green Economic Efficiency: Evidence from Province-Level Empirical Data in China," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    17. Junhong Qu & Xiaoli Hao, 2022. "Digital Economy, Financial Development, and Energy Poverty Based on Mediating Effects and a Spatial Autocorrelation Model," Sustainability, MDPI, vol. 14(15), pages 1-24, July.
    18. Tiantian Gu & Shuyu Liu & Xuefan Liu & Yujia Shan & Enyang Hao & Miaomiao Niu, 2023. "Evaluation of the Smart City and Analysis of Its Spatial–Temporal Characteristics in China: A Case Study of 26 Cities in the Yangtze River Delta Urban Agglomeration," Land, MDPI, vol. 12(10), pages 1-23, September.
    19. Ho, Sy-Hoa & OUEGHLISSI, Rim & EL FERKTAJI, Riadh, 2019. "The dynamic causality between ESG and economic growth: Evidence from panel causality analysis," MPRA Paper 95390, University Library of Munich, Germany.
    20. Xinkuo Xu & Liyan Han, 2017. "Diverse Effects of Consumer Credit on Household Carbon Emissions at Quantiles: Evidence from Urban China," Sustainability, MDPI, vol. 9(9), pages 1-25, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:800-:d:1113357. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.