IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China

Listed author(s):
  • Xueting Zhao

    ()

  • J. Burnett

    ()

Due to criticisms of potential identification issues within spatial panel data models, this study contributes to the literature by comparing forecasts of province-level carbon dioxide emissions against empirical reality using dynamic, spatial panel data models with and without fixed effects. From a policy standpoint, understanding how to predict emissions is important for designing climate change mitigation policies. From a statistical standpoint, it is important to test spatial econometrics models to see if they are a valid strategy to describe the underlying data. We find that the best model is the spatio-temporal panel data model which controls for fixed effects. Our findings demonstrate the importance of considering not only spatial and temporal dependence but also the individual or heterogeneous characteristics within each province. Copyright Springer-Verlag Berlin Heidelberg 2014

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://hdl.handle.net/10.1007/s12076-013-0109-4
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Springer in its journal Letters in Spatial and Resource Sciences.

Volume (Year): 7 (2014)
Issue (Month): 3 (October)
Pages: 171-183

as
in new window

Handle: RePEc:spr:lsprsc:v:7:y:2014:i:3:p:171-183
DOI: 10.1007/s12076-013-0109-4
Contact details of provider: Web page: http://www.springer.com/

Order Information: Web: http://www.springer.com/economics/journal/12076

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. 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.
  2. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "The relative efficiencies of various predictors in spatial econometric models containing spatial lags," Regional Science and Urban Economics, Elsevier, vol. 37(3), pages 363-374, May.
  3. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
  4. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
  5. Elhorst, J. Paul, 2010. "Dynamic panels with endogenous interaction effects when T is small," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 272-282, September.
  6. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, 02.
  7. Harry H. Kelejian & Dennis P. Robinson, 2000. "Returns to investment in navigation infrastructure: An equilibrium approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 34(1), pages 83-108.
  8. Burnett, J. Wesley & Bergstrom, John C. & Dorfman, Jeffrey H., 2013. "A spatial panel data approach to estimating U.S. state-level energy emissions," Energy Economics, Elsevier, vol. 40(C), pages 396-404.
  9. 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.
  10. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
  11. Yoshihiro Ohtsuka & Kazuhiko Kakamu, 2013. "Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 75-85, 01.
  12. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
  13. Conley, Timothy G & Ligon, Ethan, 2002. "Economic Distance and Cross-Country Spillovers," Journal of Economic Growth, Springer, vol. 7(2), pages 157-187, June.
  14. Konstantin A. Kholodilin & Andreas Mense, 2012. "Forecasting the Prices and Rents for Flats in Large German Cities," Discussion Papers of DIW Berlin 1207, DIW Berlin, German Institute for Economic Research.
  15. Maximilian Auffhammer & Ralf Steinhauser, 2012. "Forecasting The Path of U.S. CO_2 Emissions Using State-Level Information," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 172-185, February.
  16. Ana Angulo & F. Trívez, 2010. "The impact of spatial elements on the forecasting of Spanish labour series," Journal of Geographical Systems, Springer, vol. 12(2), pages 155-174, June.
  17. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
  18. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
  19. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
  20. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
  21. Maximilian Auffhammer & Ralf Steinhauser, 2007. "The Future Trajectory Of U.S. Co," Journal of Regional Science, Wiley Blackwell, vol. 47(1), pages 47-61.
  22. Mark D. Partridge & Marlon Boarnet & Steven Brakman & Gianmarco Ottaviano, 2012. "Introduction: Whither Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 167-171, 05.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:spr:lsprsc:v:7:y:2014:i:3:p:171-183. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)

or (Rebekah McClure)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.