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Forecasting U.S. State-Level Carbon Dioxide Emissions

Author

Listed:
  • J. Wesley Burnett

    (College of Charleston)

  • Xueting Zhao

    (West Virginia University)

Abstract

This study explores the use of spatial models in forecasting U.S. state-level carbon dioxide emissions. We compare forecasts against empirical reality using panel data models with and without spatial effects. Understanding how to predict emissions is important for designing climate change mitigation policies. To determine if spatial econometric models can help us predict emissions, it is important to test these models to see if they are a valid strategy to describe the underlying data, in the context of forecasting. We find that a non-spatial OLS estimator performs best in all out-of-sample forecasts; however, the OLS model is not statistically distinguishable from a spatial panel data model with random effects.

Suggested Citation

  • J. Wesley Burnett & Xueting Zhao, 2014. "Forecasting U.S. State-Level Carbon Dioxide Emissions," The Review of Regional Studies, Southern Regional Science Association, vol. 44(3), pages 223-240, Winter.
    • Handle: RePEc:rre:publsh:v44:y:2014:i:3:p:223-240
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    References listed on IDEAS

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    More about this item

    Keywords

    spatial panel data econometrics; forecasting; carbon dioxide emissions;
    All these keywords.

    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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