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Modelling Deforestation and Land‐Use Change: Sparse Data Environments

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  • Alessandro De Pinto
  • Gerald C. Nelson

Abstract

Land‐use change in developing countries is of great interest to policy‐makers and researchers with diverse interests. Concerns about consequences of deforestation for global climate change and biodiversity have received the most publicity, but loss of wetlands, declining land productivity and watershed management are also problems facing developing countries. Analyses of these problems are especially constrained by lack of data. This article reviews modelling approaches for data‐constrained environments that involve discrete choice methods including neural nets and dynamic programming, and research results that link individual household survey data with satellite images using geographic positioning systems.

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  • Alessandro De Pinto & Gerald C. Nelson, 2007. "Modelling Deforestation and Land‐Use Change: Sparse Data Environments," Journal of Agricultural Economics, Wiley Blackwell, vol. 58(3), pages 502-516, September.
    • Handle: RePEc:bla:jageco:v:58:y:2007:i:3:p:502-516
    DOI: 10.1111/j.1477-9552.2007.00119.x
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    Cited by:

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    2. Levente Tímár, 2011. "Rural Land Use and Land Tenure in New Zealand," Working Papers 11_13, Motu Economic and Public Policy Research.
    3. Corral, Paul & Radchenko, Natalia, 2017. "What’s So Spatial about Diversification in Nigeria?," World Development, Elsevier, vol. 95(C), pages 231-253.
    4. Lu, Xiao & Shi, Yangyang & Chen, Changling & Yu, Miao, 2017. "Monitoring cropland transition and its impact on ecosystem services value in developed regions of China: A case study of Jiangsu Province," Land Use Policy, Elsevier, vol. 69(C), pages 25-40.
    5. Li, Man & De Pinto, Alessandro & Ulimwengu, John M. & You, Liangzhi & Robertson, Richard D., 2012. "Impacts of Paving Roads for Development in the Democratic Republic of Congo: Deforestation and Biological Carbon Loss," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126672, International Association of Agricultural Economists.
    6. 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.
    7. Aguirregabiria, Victor, 2009. "Estimation of Dynamic Discrete Games Using the Nested Pseudo Likelihood Algorithm: Code and Application," MPRA Paper 17329, University Library of Munich, Germany.
    8. Carlos García-Alonso & Leonor Pérez-Naranjo & Juan Fernández-Caballero, 2014. "Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms," Annals of Operations Research, Springer, vol. 219(1), pages 187-202, August.
    9. Walsh, Patrick J. & Bird, Stephen & Heintzelman, Martin D., 2015. "Understanding Local Regulation of Fracking: A Spatial Econometric Approach," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 44(2), pages 1-26, August.
    10. Qianru Yu & Chen-Chieh Feng & NuanYin Xu & Luo Guo & Dan Wang, 2019. "Quantifying the Impact of Grain for Green Program on Ecosystem Service Management: A Case Study of Exibei Region, China," IJERPH, MDPI, vol. 16(13), pages 1-17, June.

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    More about this item

    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
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns

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