Economics at your fingertips  

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

Xueting Zhao () and James Burnett ()

Letters in Spatial and Resource Sciences, 2014, vol. 7, issue 3, 183 pages

Abstract: 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

Keywords: Spatial dynamic panel data; Forecasting; Carbon dioxide emissions; China; C33; C53; Q50 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1007/s12076-013-0109-4

Access Statistics for this article

Letters in Spatial and Resource Sciences is currently edited by Henk Folmer and Amitrajeet A. Batabyal

More articles in Letters in Spatial and Resource Sciences from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2021-01-06
Handle: RePEc:spr:lsprsc:v:7:y:2014:i:3:p:171-183