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The choice of emission indicators in environmental policy design: an analysis of GHG abatement in different dairy farms based on a bio-economic model approach

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  • Bernd Lengers

    ()
    (Wissenschaftlicher Mitarbeiter, Institut fur Lebensmittel- und Ressourcenökonomik (ILR), Abt. Ressourcen- und Umweltökonomik, Nussallee 21, 53115 Bonn, Allemagne)

  • Wolfgang Britz

    (Wissenschaftlicher Mitarbeiter, Institut fur Lebensmittel- und Ressourcenökonomik (ILR), Abt. Wirtschafts- und Agrarpolitik)

Abstract

The application of economic instruments to GHG emissions from dairy farms needs to rely on GHG indicators as actual emissions are impossible or extremely costly to measure. The choice of indicator impacts chosen abatement options, related costs and GHG actually emitted. A tool to quantify these relations is proposed which at its core consists of a highly detailed, mixed-integer dynamic programming model template able to cover a wide range of dairy farm characteristics and promising indicators. It allows deriving and comparing marginal abatement costs of GHGs emission for different farm types and indicators, informing the policy process about promising indicators, abatement strategies and related abatement and measurement costs.

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Bibliographic Info

Article provided by INRA Department of Economics in its journal Review of Agricultural and Environmental Studies.

Volume (Year): 93 (2012)
Issue (Month): 2 ()
Pages: 117-144

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Handle: RePEc:rae:jourae:v:93:y:2012:i:2:p:117-144

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Keywords: marginal abatement costs; emission indicators; dynamic mixed integer programming; greenhouse gas emissions;

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  1. Stéphane Cara & Martin Houzé & Pierre-Alain Jayet, 2005. "Methane and Nitrous Oxide Emissions from Agriculture in the EU: A Spatial Assessment of Sources and Abatement Costs," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 32(4), pages 551-583, December.
  2. Bruno Vermont & Stephane De Cara, 2010. "How costly is mitigation of non-CO2 greenhouse gas emissions from agriculture?: A meta-analysis," Working Papers 34004, Institut National de la Recherche Agronomique, France.
  3. Kennedy, John O. S., 1988. "Principles of dynamic optimization in resource management," Agricultural Economics, Blackwell, vol. 2(1), pages 57-72, June.
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  5. Dominguez, Ignacio Perez & Britz, Wolfgang, 2010. "Greenhouse Gas Emission Trading In European Agriculture: A Comparison Of Different Policy Implementation Options In Year 2020," Proceedings Issues, 2010: Climate Change in World Agriculture: Mitigation, Adaptation, Trade and Food Security, June 2010, Stuttgart- Hohenheim, Germany 91396, International Agricultural Trade Research Consortium.
  6. S De Cara & P-A Jayet, 2000. "Emissions of greenhouse gases from agriculture: the heterogeneity of abatement costs in France," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 27(3), pages 281-303, September.
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  9. Huirne, R. B. M. & Dijkhuizen, A. A. & van Beek, P. & Hendriks, Th. H. B., 1993. "Stochastic dynamic programming to support sow replacement decisions," European Journal of Operational Research, Elsevier, vol. 67(2), pages 161-171, June.
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Cited by:
  1. Lengers, Bernd & Britz, Wolfgang & Holm-Müller, Karin, 2013. "Trade-off of feasibility against accuracy and cost efficiency in choosing indicators for the abatement of GHG-emissions in dairy farming," Discussion Papers 162877, University of Bonn, Institute for Food and Resource Economics.
  2. Remble, Amber & Britz, Wolfgang & Keeney, Roman, 2013. "Farm Level Tradeoffs in the Regulation of Greenhouse Gas Emissions," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150442, Agricultural and Applied Economics Association.
  3. Kahil, Mohamed Taher & Albiac, José, 2013. "Greenhouse gases mitigation policies in the agriculture of Aragon, Spain," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), issue 1, April.

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