IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3346-d808244.html
   My bibliography  Save this article

Partitioning for “Common but Differentiated” Precise Air Pollution Governance: A Combined Machine Learning and Spatial Econometric Approach

Author

Listed:
  • Yang Yi

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

  • Le Wen

    (Energy Centre, Department of Economics, The University of Auckland, Auckland 1142, New Zealand)

  • Shan He

    (School of Economics and Management, Zhumadian Vocational and Technical College, Zhumadian 463000, China)

Abstract

Effective governance of air pollution requires precise identification of its influencing factors. Most existing studies attempt to identify the socioeconomic factors but lack consideration of multidimensional heterogeneous characteristics. This paper fills this long-ignored research gap by differentiating governance regions with regard to multidimensional heterogeneity characteristics. Decision tree recursive analysis combined with a spatial autoregressive model is used to identify governance factors in China. Empirical results show several interesting findings. First, geographic location, administrative level, economic zones and regional planning are the main heterogeneous features of accurate air pollution governance in Chinese cities. Second, significant influencing factors of air pollution in different delineated regions are identified, especially significant differences between coastal and non-coastal cities. Third, the trends of heterogeneity in urban air governance in China are to some extent consistent with national policies. The approach identifies factors influencing air pollution, thus providing a basis for accurate air pollution governance that has wider applicability.

Suggested Citation

  • Yang Yi & Le Wen & Shan He, 2022. "Partitioning for “Common but Differentiated” Precise Air Pollution Governance: A Combined Machine Learning and Spatial Econometric Approach," Energies, MDPI, vol. 15(9), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3346-:d:808244
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3346/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3346/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Achim Zeileis, 2005. "A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 445-466.
    2. Abdalla Sirag & Bolaji Tunde Matemilola & Siong Hook Law & A. N Bany-Ariffin, 2018. "Does environmental Kuznets curve hypothesis exist? Evidence from dynamic panel threshold," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 7(2), pages 145-165, April.
    3. List, John A. & Co, Catherine Y., 2000. "The Effects of Environmental Regulations on Foreign Direct Investment," Journal of Environmental Economics and Management, Elsevier, vol. 40(1), pages 1-20, July.
    4. Park, Soonae & Lee, Youngmi, 2011. "Regional model of EKC for air pollution: Evidence from the Republic of Korea," Energy Policy, Elsevier, vol. 39(10), pages 5840-5849, October.
    5. Shen, Junyi, 2006. "A simultaneous estimation of Environmental Kuznets Curve: Evidence from China," China Economic Review, Elsevier, vol. 17(4), pages 383-394.
    6. Hansen, Bruce E, 1997. "Approximate Asymptotic P Values for Structural-Change Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 60-67, January.
    7. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    8. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    9. Xiaoguang Chen & Jingjing Ye, 2019. "When the wind blows: spatial spillover effects of urban air pollution in China," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 62(8), pages 1359-1376, July.
    10. Guo, Qingbin & Wang, Yong & Dong, Xiaobin, 2022. "Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China," Applied Energy, Elsevier, vol. 313(C).
    11. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    12. Yi, Ming & Wang, Yiqian & Sheng, Mingyue & Sharp, Basil & Zhang, Yao, 2020. "Effects of heterogeneous technological progress on haze pollution: Evidence from China," Ecological Economics, Elsevier, vol. 169(C).
    13. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    14. Yin Feng & Jinhua Cheng & Jun Shen & Han Sun, 2019. "Spatial Effects of Air Pollution on Public Health in China," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(1), pages 229-250, May.
    15. Friedl, Birgit & Getzner, Michael, 2003. "Determinants of CO2 emissions in a small open economy," Ecological Economics, Elsevier, vol. 45(1), pages 133-148, April.
    16. Achim Zeileis & Torsten Hothorn, 2013. "A toolbox of permutation tests for structural change," Statistical Papers, Springer, vol. 54(4), pages 931-954, November.
    17. Le Wen, Basil Sharp, and Erwann Sbai, 2020. "Spatial Effects of Wind Generation and Its Implication for Wind Farm Investment Decisions in New Zealand," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 47-72.
    18. Kaufmann, Robert K. & Davidsdottir, Brynhildur & Garnham, Sophie & Pauly, Peter, 1998. "The determinants of atmospheric SO2 concentrations: reconsidering the environmental Kuznets curve," Ecological Economics, Elsevier, vol. 25(2), pages 209-220, May.
    19. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    20. Natalia Zugravu-Soilita, 2017. "How does Foreign Direct Investment Affect Pollution? Toward a Better Understanding of the Direct and Conditional Effects," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 66(2), pages 293-338, February.
    21. CARSON, RICHARd T. & JEON, YONGIL & McCUBBIN, DONALD R., 1997. "The relationship between air pollution emissions and income: US Data," Environment and Development Economics, Cambridge University Press, vol. 2(4), pages 433-450, November.
    22. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    Full references (including those not matched with items on IDEAS)

    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. Martin Wagner & Achim Zeileis, 2019. "Heterogeneity and Spatial Dependence of Regional Growth in the EU: A Recursive Partitioning Approach," German Economic Review, Verein für Socialpolitik, vol. 20(1), pages 67-82, February.
    2. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    3. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.
    4. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    5. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
    6. Daniel Levy & Tamir Mayer & Alon Raviv, 2020. "Academic Scholarship in Light of the 2008 Financial Crisis: Textual Analysis of NBER Working Papers," Working Papers 2020-01, Bar-Ilan University, Department of Economics.
    7. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    8. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
    9. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
    10. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    11. Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2022. "Economists in the 2008 Financial Crisis: Slow to See, Fast to Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Forthcomi.
    12. Matthew A. Cole & Robert J R Elliott & Bowen Liu, 2020. "The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 553-580, August.
    13. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    14. Paolo Verme, 2020. "Which Model for Poverty Predictions?," Working Papers 521, ECINEQ, Society for the Study of Economic Inequality.
    15. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    16. Mahmood, Faisal & Ben Zaied, Younes & Abedin, Mohammad Zoynul, 2024. "Role of green finance instruments in shaping economic cycles," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    17. Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
    18. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    19. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    20. Mehmet Güney Celbiş & Pui‐hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 559-584, April.

    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:jeners:v:15:y:2022:i:9:p:3346-:d:808244. 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.