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Predicting land use allocation in France: A spatial panel data analysis

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  • Chakir, Raja
  • Le Gallo, Julie

Abstract

Predictions of future land use areas are an important issue as land use patterns significantly impact environmental conditions (biodiversity, water pollution, soil erosion, and climate change) as well as economic and social welfare. In order to improve the prediction accuracy of aggregated land use share models, we propose in this paper a methodological contribution by controlling for both unobserved individual heterogeneity and spatial autocorrelation. Our model is a land use shares model applied to aggregated data in France. Our dataset is a panel which covers both time series observations from 1992 to 2003 and cross-sectional observations by Département (equivalent to NUTS3 regions). We consider four land use classes: (1) agriculture, (2) forest, (3) urban and (4) other use. We investigate the relation between the areas in land in different alternative uses and economic and demographic factors influencing land use decisions. Based on the comparison of prediction accuracy of different model specifications, our findings are threefold: First, controlling for both unobserved individual heterogeneity and spatial autocorrelation outperforms any other specification in which spatial autocorrelation and/or individual heterogeneity are ignored. Second, accounting for cross-equation correlations does not seem to improve the prediction performances and finally, ignoring individual heterogeneity introduces substantial loss in prediction accuracy.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecolec:v:92:y:2013:i:c:p:114-125
    DOI: 10.1016/j.ecolecon.2012.04.009
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Camacho, Carmen & Pérez-Barahona, Agustín, 2015. "Land use dynamics and the environment," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 96-118.
    3. Chonabayashi, Shun, 2014. "Accounting for Land Use Adaptation to Climate Change Impacts on US Agriculture," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170710, Agricultural and Applied Economics Association.
    4. Parvez, Md Rezwanul & Ripplinger, David & Maduraperuma, Buddhika, 2015. "Modeling Land Use Pattern Change Analysis in the Northern Great Plains: A Novel Approach," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205868, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    5. Jean-Sauveur Ay & Raja Chakir & Luc Doyen & Frédéric Jiguet & Paul Leadley, 2014. "Integrated models, scenarios and dynamics of climate, land use and common birds," Climatic Change, Springer, vol. 126(1), pages 13-30, September.
    6. Min Zhou & Shukui Tan & Lizao Tao & Xiangbo Zhu & Ghulam Akhmat, 2015. "An interval fuzzy land-use allocation model (IFLAM) for Beijing in association with environmental and ecological consideration under uncertainty," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2269-2290, November.
    7. Bingkui Qiu & Shasha Lu & Min Zhou & Lu Zhang & Yu Deng & Ci Song & Zuo Zhang, 2015. "A Hybrid Inexact Optimization Method for Land-Use Allocation in Association with Environmental/Ecological Requirements at a Watershed Level," Sustainability, MDPI, Open Access Journal, vol. 7(4), pages 1-25, April.
    8. Abildtrup, Jens & Garcia, Serge & Stenger, Anne, 2013. "The effect of forest land use on the cost of drinking water supply: A spatial econometric analysis," Ecological Economics, Elsevier, vol. 92(C), pages 126-136.
    9. repec:eee:ecolec:v:147:y:2018:i:c:p:134-154 is not listed on IDEAS
    10. repec:cai:recosp:reco_683_0435 is not listed on IDEAS
    11. repec:gam:jlands:v:7:y:2018:i:2:p:62-:d:145362 is not listed on IDEAS
    12. repec:cai:recosp:reco_683_0471 is not listed on IDEAS
    13. Raja Chakir & Thibault Laurent & Anne Ruiz-Gazen & Christine Thomas-Agnan & Céline Vignes, 2017. "Prédiction de l’usage des sols sur un zonage régulier à différentes résolutions et à partir de covariables facilement accessibles," Revue économique, Presses de Sciences-Po, vol. 68(3), pages 435-469.
    14. Raja Chakir & Stéphane De Cara & Bruno Vermont, 2017. "Price-Induced Changes in Greenhouse Gas Emissions from Agriculture, Forestry, and Other Land Use: A Spatial Panel Econometric Analysis," Revue économique, Presses de Sciences-Po, vol. 68(3), pages 471-490.

    More about this item

    Keywords

    Land use share model; Spatial autocorrelation; Panel data; Prediction;

    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
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • 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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