IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i2p608-d479124.html
   My bibliography  Save this article

Exploring the Dynamic Spatio-Temporal Correlations between PM 2.5 Emissions from Different Sources and Urban Expansion in Beijing-Tianjin-Hebei Region

Author

Listed:
  • Shen Zhao

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yong Xu

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Due to rapid urbanization globally more people live in urban areas and, simultaneously, more people are exposed to the threat of environmental pollution. Taking PM 2.5 emission data as the intermediate link to explore the correlation between corresponding sectors behind various PM 2.5 emission sources and urban expansion in the process of urbanization, and formulating effective policies, have become major issues. In this paper, based on long temporal coverage and high-quality nighttime light data seen from the top of the atmosphere and recently compiled PM 2.5 emissions data from different sources (transportation, residential and commercial, industry, energy production, deforestation and wildfire, and agriculture), we built an advanced Bayesian spatio-temporal autoregressive model and a local regression model to quantitatively analyze the correlation between PM 2.5 emissions from different sources and urban expansion in the Beijing-Tianjin-Hebei region. Our results suggest that the overall urban expansion in the study area maintained gradual growth from 1995 to 2014, with the fastest growth rate during 2005 to 2010; the urban expansion maintained a significant positive correlation with PM 2.5 emissions from transportation, energy production, and industry; different anti-haze policies should be designated according to respective local conditions in Beijing, Tianjin, and Hebei provinces; and during the period of rapid urban expansion (2005–2010), the spatial correlations between PM 2.5 emissions from different sources and urban expansion also changed, with the biggest change coming from the PM 2.5 emissions from the transport sector.

Suggested Citation

  • Shen Zhao & Yong Xu, 2021. "Exploring the Dynamic Spatio-Temporal Correlations between PM 2.5 Emissions from Different Sources and Urban Expansion in Beijing-Tianjin-Hebei Region," IJERPH, MDPI, vol. 18(2), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:608-:d:479124
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/2/608/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/2/608/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Udi Joshua & Festus V. Bekun & Samuel A. Sarkodie, 2020. "New Insight into the Causal Linkage between Economic Expansion, FDI, Coal consumption, Pollutant emissions and Urbanization in South Africa," Working Papers 20/011, European Xtramile Centre of African Studies (EXCAS).
    2. Li, Li & Hong, Xuefei & Wang, Jun, 2020. "Evaluating the impact of clean energy consumption and factor allocation on China’s air pollution: A spatial econometric approach," Energy, Elsevier, vol. 195(C).
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Yang, Siyuan & Fang, Delin & Chen, Bin, 2019. "Human health impact and economic effect for PM2.5 exposure in typical cities," Applied Energy, Elsevier, vol. 249(C), pages 316-325.
    5. Shen Zhao & Guanpeng Dong & Yong Xu, 2020. "A Dynamic Spatio-Temporal Analysis of Urban Expansion and Pollutant Emissions in Fujian Province," IJERPH, MDPI, vol. 17(2), pages 1-15, January.
    6. Ruhul Salim & Shuddhasattwa Rafiq & Sahar Shafiei & Yao Yao, 2019. "Does urbanization increase pollutant emission and energy intensity? evidence from some Asian developing economies," Applied Economics, Taylor & Francis Journals, vol. 51(36), pages 4008-4024, August.
    7. Alastair Rushworth & Duncan Lee & Christophe Sarran, 2017. "An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 141-157, January.
    8. Ou, Jiamin & Meng, Jing & Zheng, Junyu & Mi, Zhifu & Bian, Yahui & Yu, Xiang & Liu, Jingru & Guan, Dabo, 2017. "Demand-driven air pollutant emissions for a fast-developing region in China," Applied Energy, Elsevier, vol. 204(C), pages 131-142.
    9. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    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. Yin, Kai & Miao, Qin, 2024. "Urbanization and low-carbon cities: Evidence from city-county merger in China," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 724-737.
    2. Mohamad Khoirun Najib & Sri Nurdiati & Ardhasena Sopaheluwakan, 2022. "Multivariate fire risk models using copula regression in Kalimantan, Indonesia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1263-1283, September.

    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. Shen Zhao & Guanpeng Dong & Yong Xu, 2020. "A Dynamic Spatio-Temporal Analysis of Urban Expansion and Pollutant Emissions in Fujian Province," IJERPH, MDPI, vol. 17(2), pages 1-15, January.
    2. Wu, Peijie & Meng, Xianghai & Song, Li, 2021. "Bayesian space–time modeling of bicycle and pedestrian crash risk by injury severity levels to explore the long-term spatiotemporal effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    3. Edmund Ntom Udemba & Naci İbrahim Keleş, 2022. "Interactions among urbanization, industrialization and foreign direct investment (FDI) in determining the environment and sustainable development: new insight from Turkey," Asia-Pacific Journal of Regional Science, Springer, vol. 6(1), pages 191-212, February.
    4. Jun Yang & Yongmei Miao & Yunfan Li & Yiwen Li & Xiaoxue Ma & Shichun Xu & Shuxiao Wang, 2019. "Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    5. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    6. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    7. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    8. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    9. Yuqing Zhou & Haibin Liu, 2023. "Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    10. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    11. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    12. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.
    13. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    14. Tomiwa Sunday Adebayo & Abraham Ayobamiji Awosusi & Seun Damola Oladipupo & Ephraim Bonah Agyekum & Arunkumar Jayakumar & Nallapaneni Manoj Kumar, 2021. "Dominance of Fossil Fuels in Japan’s National Energy Mix and Implications for Environmental Sustainability," IJERPH, MDPI, vol. 18(14), pages 1-20, July.
    15. Edwin Fourrier-Nicolai & Michel Lubrano, 2022. "Bayesian inference for non-anonymous Growth Incidence Curves using Bernstein polynomials: an application to academic wage dynamics," AMSE Working Papers 2227, Aix-Marseille School of Economics, France.
    16. Mardones, Cristian, 2021. "Ex-post evaluation and cost-benefit analysis of a heater replacement program implemented in southern Chile," Energy, Elsevier, vol. 227(C).
    17. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    18. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).
    19. repec:jss:jstsof:21:i08 is not listed on IDEAS
    20. Deng, Yaguo, 2016. "Efficiency evaluation of Spanish hotel chains," DES - Working Papers. Statistics and Econometrics. WS 23897, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Cathy W. S. Chen & Sangyeol Lee, 2017. "Bayesian causality test for integer-valued time series models with applications to climate and crime data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 797-814, August.

    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:jijerp:v:18:y:2021:i:2:p:608-:d:479124. 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.