IDEAS home Printed from https://ideas.repec.org/p/ags/eaae14/182770.html

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

Author

Listed:
  • 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
    DOI: 10.22004/ag.econ.182770
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/182770/files/Chakir-Price-induced_changes_in_greenhouse_gas_emissions-583_a.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.182770?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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, vol. 70(9), pages 1680-1690, July.
    2. 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.
    3. Raja Chakir & Stéphane De Cara & Bruno Vermont, 2011. "Émissions de gaz à effet de serre dues à l’agriculture et aux usages des sols en France : une analyse spatiale," Économie et Statistique, Programme National Persée, vol. 444(1), pages 201-221.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Jerry Hausman, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    11. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maxence Gérard & Stéphane De Cara & Guy Meunier, 2025. "Mitigating greenhouse gas emissions from the cattle sector: Land‐use regulation as an alternative to emissions pricing," American Journal of Agricultural Economics, John Wiley & Sons, vol. 107(1), pages 312-345, January.
    2. Meng-Shiuh Chang & Chih-Chun Kung, 2018. "The greenhouse gas impact of bioenergy in developing economies: Evidence from Taiwan," Energy & Environment, , vol. 29(3), pages 315-332, May.
    3. Lina Liu & Jiansheng Qu & Feng Gao & Tek Narayan Maraseni & Shaojian Wang & Suman Aryal & Zhenhua Zhang & Rong Wu, 2024. "Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives," Land, MDPI, vol. 13(3), pages 1-24, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lungarska, Anna & Chakir, Raja, 2018. "Climate-induced Land Use Change in France: Impacts of Agricultural Adaptation and Climate Change Mitigation," Ecological Economics, Elsevier, vol. 147(C), pages 134-154.
    2. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    3. Xiaowen Dai & Libin Jin, 2021. "Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-13, December.
    4. Gervásio dos Santos & Weslem Faria, 2012. "Spatial Panel Data Models and Fuel Demand in Brazil," TD NEREUS 10-2012, Núcleo de Economia Regional e Urbana da Universidade de São Paulo (NEREUS).
    5. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    6. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    7. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    8. Badi H. Baltagi & Long Liu, 2016. "Random Effects, Fixed Effects and Hausman's Test for the Generalized Mixed Regressive Spatial Autoregressive Panel Data Model," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 638-658, April.
    9. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    10. repec:rri:wpaper:201303 is not listed on IDEAS
    11. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    12. Arfat Ahmad Sofi & Subash Sasidharan, 2018. "Do Indian States Mimic, Compete or Interact in Local Public Spending? A Spatial Econometric Analysis," Asian Economic Journal, East Asian Economic Association, vol. 32(2), pages 187-213, June.
    13. Lingling Tian & Yunan Su & Chuanhua Wei, 2024. "Tests for time-varying coefficient spatial autoregressive panel data model with fixed effects," Statistical Papers, Springer, vol. 65(9), pages 5481-5501, December.
    14. Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.
    15. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2012. "Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration," Journal of Econometrics, Elsevier, vol. 167(1), pages 16-37.
    16. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
    17. Wang Lu & Hao Yu & Wei Yi-Ming, "undated". "How Do Regional Interactions in Space Affect China’s Mitigation Targets and Economic Development?," MITP: Mitigation, Innovation and Transformation Pathways 257876, Fondazione Eni Enrico Mattei (FEEM).
    18. Fingleton, Bernard, 2010. "Predicting the geography of house prices," LSE Research Online Documents on Economics 33507, London School of Economics and Political Science, LSE Library.
    19. Bernard Fingleton, 2008. "A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(1), pages 27-44.
    20. Lung‐fei Lee & Jihai Yu, 2012. "Spatial Panels: Random Components Versus Fixed Effects," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1369-1412, November.
    21. Harry H. Kelejian & Gianfranco Piras, 2013. "A J-Test for Panel Models with Fixed Effects, Spatial and Time," Working Papers Working Paper 2013-03, Regional Research Institute, West Virginia University.

    More about this item

    Keywords

    ;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:eaae14:182770. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/eaaeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.