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Forecasting the Path of USS CO2 Emissions Using State-Level Information


  • Maximillian Auffhammer


  • Ralf Steinhauser



In this paper we compare the most common reduced form models used for emissions forecasting, point out shortcomings and suggest improvements. Using a U.S. state level panel data set of CO2 emissions we test the performance of existing models against a large universe of potential reduced form models. Our preferred measure of model performance is the squared out-of-sample prediction error of aggregate CO2 emissions. We find that leading models in the literature, as well as models selected based on an emissions per capita loss measure or different in-sample selection criteria, perform significantly worse compared to the best model chosen based directly on the out-of-sample loss measure defined over aggregate emissions. Unlike the existing literature, the tests of model superiority employed here account for model search or ‘data snooping’ involved in identifying a preferred model. Forecasts from our best performing model for the United States are 100 million tons of carbon lower than existing scenarios predict.

Suggested Citation

  • Maximillian Auffhammer & Ralf Steinhauser, 2010. "Forecasting the Path of USS CO2 Emissions Using State-Level Information," ANU Working Papers in Economics and Econometrics 2010-526, Australian National University, College of Business and Economics, School of Economics.
  • Handle: RePEc:acb:cbeeco:2010-526

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    References listed on IDEAS

    1. Holtz-Eakin, Douglas & Selden, Thomas M., 1995. "Stoking the fires? CO2 emissions and economic growth," Journal of Public Economics, Elsevier, vol. 57(1), pages 85-101, May.
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    Cited by:

    1. Patrick Doupe, 2014. "The Costs of Error in Setting Reference Rates for Reduced Deforestation," CCEP Working Papers 1415, Centre for Climate Economics & Policy, Crawford School of Public Policy, The Australian National University.
    2. 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.
    3. repec:gam:jsusta:v:9:y:2017:i:6:p:886-:d:99649 is not listed on IDEAS
    4. Chen, Cuicui & Zeckhauser, Richard, 2016. "Collective Action in an Asymmetric World," Working Paper Series 16-019, Harvard University, John F. Kennedy School of Government.
    5. Yang, Haisheng & He, Jie & Chen, Shaoling, 2015. "The fragility of the Environmental Kuznets Curve: Revisiting the hypothesis with Chinese data via an “Extreme Bound Analysis”," Ecological Economics, Elsevier, vol. 109(C), pages 41-58.
    6. Buck, Steven & Soldati, Hilary & Sunding, David L., 2015. "Forecasting Urban Water Demand in California: Rethinking Model Evaluation," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205737, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    7. Yanan Liu & Yixuan Gao & Yu Hao & Hua Liao, 2016. "The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data," Energies, MDPI, Open Access Journal, vol. 9(10), pages 1-11, October.
    8. Du, Limin & Hanley, Aoife & Wei, Chu, 2015. "Estimating the Marginal Abatement Cost Curve of CO2 Emissions in China: Provincial Panel Data Analysis," Energy Economics, Elsevier, vol. 48(C), pages 217-229.
    9. James G. Baldwin & Ian Sue Wing, 2013. "The Spatiotemporal Evolution Of U.S. Carbon Dioxide Emissions: Stylized Facts And Implications For Climate Policy," Journal of Regional Science, Wiley Blackwell, vol. 53(4), pages 672-689, October.
    10. Hua Liao & Huaishu Cao, 2012. "How does carbon dioxide emission change with the economic development? Statistical experiences from 132 countries," CEEP-BIT Working Papers 54, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    11. Steven Lugauer & Richard Jensen & Clayton Sadler, 2014. "An Estimate Of The Age Distribution'S Effect On Carbon Dioxide Emissions," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 914-929, April.
    12. Doupe, Patrick, 2014. "The costs of error in setting reference rates for reduced deforestation," Working Papers 249497, Australian National University, Centre for Climate Economics & Policy.
    13. Christoph Jeßberger, 2011. "Multilateral Environmental Agreements up to 2050: Are They Sustainable Enough?," ifo Working Paper Series 98, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

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    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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