IDEAS home Printed from https://ideas.repec.org/p/ags/feemmi/257876.html
   My bibliography  Save this paper

How Do Regional Interactions in Space Affect China’s Mitigation Targets and Economic Development?

Author

Listed:
  • Wang Lu
  • Hao Yu
  • Wei Yi-Ming

Abstract

China is faced with the big challenge of maintaining a remarkable economic growth in an environmental friendly manner; that is why forecasting the turning point is of necessity. Traditional econometric approaches do not consider the spatial dependence that inevitably exists in the economic units, which probably risks misspecification and generating a biased estimation result. This paper firstly constructs Theil index to measure the intra-and inter regional inequality of CO2 emissions, we find that difference in emissions between regions is narrowed but gap within the Western China is sharply expanding. Then the Spatial Durbin model is employed to shape the relationship between mitigation and economic growth using the panel data of 29 provinces ranging from 1995 to 2011. Results show that the peak of per capita carbon dioxide emissions in China would be seen when GDP per capita reaches between $USD 21594 to 24737 (at 2000 constant price), much smaller when compared with the estimations of models which ignore the spatial dependence. This implies that territorial policy and industry transfer, on one hand would favor those underdeveloped regions with investment, technology and labors transfer; on the other hand enables developed regions more potential to mitigation, thus, chances are that China achieves the emissions peak of carbon dioxide earlier than conventional wisdom.

Suggested Citation

  • Wang Lu & Hao Yu & Wei Yi-Ming, 2017. "How Do Regional Interactions in Space Affect China’s Mitigation Targets and Economic Development?," MITP: Mitigation, Innovation and Transformation Pathways 257876, Fondazione Eni Enrico Mattei (FEEM).
  • Handle: RePEc:ags:feemmi:257876
    DOI: 10.22004/ag.econ.257876
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/257876/files/NDL2017-021.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.257876?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lee, Lung-fei, 2007. "The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 140(1), pages 155-189, September.
    2. Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
    3. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2013. "A Generalized Spatial Panel Data Model with Random Effects," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 650-685, August.
    4. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    5. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    6. Niebuhr, Annekatrin, 2000. "Convergence and the effects of spatial interaction," HWWA Discussion Papers 110, Hamburg Institute of International Economics (HWWA).
    7. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    8. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    9. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    10. Anselin, Luc & Hudak, Sheri, 1992. "Spatial econometrics in practice : A review of software options," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 509-536, September.
    11. Richard Schmalensee & Thomas M. Stoker & Ruth A. Judson, 1998. "World Carbon Dioxide Emissions: 1950-2050," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 15-27, February.
    12. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    13. Gene M. Grossman & Alan B. Krueger, 1991. "Environmental Impacts of a North American Free Trade Agreement," NBER Working Papers 3914, National Bureau of Economic Research, Inc.
    14. James P. Lesage, 2008. "An Introduction to Spatial Econometrics," Revue d'économie industrielle, De Boeck Université, vol. 0(3), pages 19-44.
    15. Nicole Madariaga & Sandra Poncet, 2007. "FDI in Chinese Cities: Spillovers and Impact on Growth," The World Economy, Wiley Blackwell, vol. 30(5), pages 837-862, May.
    16. Maddison, David, 2006. "Environmental Kuznets curves: A spatial econometric approach," Journal of Environmental Economics and Management, Elsevier, vol. 51(2), pages 218-230, March.
    17. Garrett, Thomas A. & Marsh, Thomas L., 2002. "The revenue impacts of cross-border lottery shopping in the presence of spatial autocorrelation," Regional Science and Urban Economics, Elsevier, vol. 32(4), pages 501-519, July.
    18. Auffhammer, Maximilian & Carson, Richard T., 2008. "Forecasting the path of China's CO2 emissions using province-level information," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 229-247, May.
    19. Wei, Yi-Ming & Wang, Lu & Liao, Hua & Wang, Ke & Murty, Tad & Yan, Jinyue, 2014. "Responsibility accounting in carbon allocation: A global perspective," Applied Energy, Elsevier, vol. 130(C), pages 122-133.
    20. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    21. J. Paul Elhorst, 2014. "Spatial Panel Data Models," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 37-93, Springer.
    22. Clarke-Sather, Afton & Qu, Jiansheng & Wang, Qin & Zeng, Jingjing & Li, Yan, 2011. "Carbon inequality at the sub-national scale: A case study of provincial-level inequality in CO2 emissions in China 1997-2007," Energy Policy, Elsevier, vol. 39(9), pages 5420-5428, September.
    23. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    24. Richmond, Amy K. & Kaufmann, Robert K., 2006. "Is there a turning point in the relationship between income and energy use and/or carbon emissions?," Ecological Economics, Elsevier, vol. 56(2), pages 176-189, February.
    25. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    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. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    2. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2012. "Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration," Journal of Econometrics, Elsevier, vol. 167(1), pages 16-37.
    3. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    4. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    5. Hao, Yu & Liu, Yiming & Weng, Jia-Hsi & Gao, Yixuan, 2016. "Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial econometric analysis," Energy, Elsevier, vol. 114(C), pages 1214-1223.
    6. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    7. Wang, Wei & Lee, Lung-fei, 2013. "Estimation of spatial panel data models with randomly missing data in the dependent variable," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 521-538.
    8. Debarsy, Nicolas & Ertur, Cem, 2010. "Testing for spatial autocorrelation in a fixed effects panel data model," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 453-470, November.
    9. Su, Liangjun & Yang, Zhenlin, 2015. "QML estimation of dynamic panel data models with spatial errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 230-258.
    10. Xiaoyi Han & Lung-Fei Lee, 2016. "Bayesian Analysis of Spatial Panel Autoregressive Models With Time-Varying Endogenous Spatial Weight Matrices, Common Factors, and Random Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 642-660, October.
    11. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    12. Xiaowen Dai & Shidan Huang & Libin Jin & Maozai Tian, 2023. "Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
    13. repec:rri:wpaper:201303 is not listed on IDEAS
    14. Gianfranco Piras, 2013. "Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 370-388, September.
    15. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    16. Qu, Xi & Lee, Lung-fei & Yu, Jihai, 2017. "QML estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices," Journal of Econometrics, Elsevier, vol. 197(2), pages 173-201.
    17. Lung‐fei Lee & Jihai Yu, 2012. "Spatial Panels: Random Components Versus Fixed Effects," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1369-1412, November.
    18. Harry H. Kelejian & Gianfranco Piras, 2013. "A J-Test for Panel Models with Fixed Effects, Spatial and Time," Working Papers Working Paper 2013-03, Regional Research Institute, West Virginia University.
    19. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    20. Yang, Zhenlin & Yu, Jihai & Liu, Shew Fan, 2016. "Bias correction and refined inferences for fixed effects spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 61(C), pages 52-72.
    21. Moscone, Francesco & Tosetti, Elisa & Canepa, Alessandra, 2014. "Real estate market and financial stability in US metropolitan areas: A dynamic model with spatial effects," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 129-146.

    More about this item

    Keywords

    Environmental Economics and Policy;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • P48 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ags:feemmi:257876. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/feemmit.html .

    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.