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

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• Xueting Zhao

()

• J. Burnett

()

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

Suggested Citation

• Xueting Zhao & J. Burnett, 2014. "Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 171-183, October.
• Handle: RePEc:spr:lsprsc:v:7:y:2014:i:3:p:171-183
DOI: 10.1007/s12076-013-0109-4
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Keywords

Spatial dynamic panel data; Forecasting; Carbon dioxide emissions; China; C33; C53; Q50;

JEL classification:

• C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
• C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
• Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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