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Price-induced changes in greenhouse gas emissions from agriculture, forestry, and other land use: A spatial panel econometric analysis

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  • Chakir, Raja
  • De Cara, Stéphane
  • Vermont, Bruno

Abstract

This paper provides a quantitative assessment of the effects of input and output prices on French GHG emissions from agriculture, forestry and other land use (AFOLU) at the NUTS2 level. Reduced-form, random-effect spatial error models are estimated for four emissions categories (nitrogen use, manure management, enteric fermentation, and land use, land-use change and forestry) in order to account for both spatial autocorrelation and spatial unobserved heterogeneity. The main findings are: (i) price impacts on emission levels are found to be significant, although sign and magnitude vary from one emission category to the other, (ii) estimated price effects are more apparent when emission categories are analyzed separately rather than aggregated, and (iii) the spatial dimension is found to play an important role. The estimated models are then used to simulate the effects of a doubling of crop prices on AFOLU emissions. The results indicate that this would lead to an 11%-increase in agricultural sources.

Suggested Citation

  • Chakir, Raja & De Cara, Stéphane & Vermont, Bruno, 2014. "Price-induced changes in greenhouse gas emissions from agriculture, forestry, and other land use: A spatial panel econometric analysis," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182770, European Association of Agricultural Economists.
  • Handle: RePEc:ags:eaae14:182770
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    References listed on IDEAS

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    1. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    2. De Cara, Stéphane & Jayet, Pierre-Alain, 2011. "Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement," Ecological Economics, Elsevier, pages 1680-1690.
    3. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    4. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    5. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
    6. Douglas J. Miller, 1999. "An Econometric Analysis of the Costs of Sequestering Carbon in Forests," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 812-824.
    7. Christopher Timmins & Wolfram Schlenker, 2009. "Reduced-Form Versus Structural Modeling in Environmental and Resource Economics," Annual Review of Resource Economics, Annual Reviews, vol. 1(1), pages 351-380, September.
    8. Vermont, Bruno & De Cara, Stéphane, 2010. "How costly is mitigation of non-CO2 greenhouse gas emissions from agriculture?: A meta-analysis," Ecological Economics, Elsevier, vol. 69(7), pages 1373-1386, May.
    9. Lubowski, Ruben N. & Plantinga, Andrew J. & Stavins, Robert N., 2006. "Land-use change and carbon sinks: Econometric estimation of the carbon sequestration supply function," Journal of Environmental Economics and Management, Elsevier, vol. 51(2), pages 135-152, March.
    10. 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.
    11. Chakir, Raja & Le Gallo, Julie, 2013. "Predicting land use allocation in France: A spatial panel data analysis," Ecological Economics, Elsevier, vol. 92(C), pages 114-125.
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    Keywords

    AFOLU; greenhouse gas emissions; spatial autocorrelation; panel data; Environmental Economics and Policy; Q15; Q54; C31; C33;

    JEL classification:

    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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