@Article{NascimentoWeBiSoOmBö:2020:BaNeAp,
               author = "Nascimento, Nath{\'a}lia Cristina Costa do and West, Thales A. P. 
                         and Biber-Freudenberger, Lisa and Sousa Neto, Er{\'a}clito 
                         Rodrigues de and Ometto, Jean Pierre Henry Balbaud and 
                         B{\"o}rner, Jan",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {New 
                         Zealand Forest Research Institute} and {University of Bonn} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of Bonn}",
                title = "A Bayesian network approach to modelling land-use decisions under 
                         environmental policy incentives in the Brazilian Amazon",
              journal = "Journal of Land Use Science",
                 year = "2020",
               volume = "15",
               number = "2/3",
                pages = "127--141",
                month = "May",
             keywords = "Land-use/cover change, deforestation, participatory process, 
                         agricultural frontiers, Tropical forest.",
             abstract = "Deforestation driven by agricultural expansion is a major threat 
                         to the biodiversity of the Amazon Basin. Modelling how 
                         deforestation responds to environmental policy implementation has 
                         thus become a policy relevant scientific undertaking. However, 
                         empirical parameterization of land-use/cover change (LUCC) models 
                         is challenging due to the high complexity and uncertainty of 
                         land-use decisions. Bayesian Network (BN) modelling provides an 
                         effective framework to integrate various data sources including 
                         expert knowledge. In this study, we integrate remote sensing 
                         products with data from farmhousehold surveys and a decision game 
                         to model LUCC at the BR-163, in Brazil. Our business as 
                         usualscenario indicates cumulative forest cover loss in the study 
                         region of 8,000 km2 between 2014 and 2030, whereas intensified 
                         law-enforcement would reduce cumulative deforestation to 1,600 km2 
                         over the same time interval. Our findings underline the importance 
                         of conservation law enforcement in modulating the impact of 
                         agricultural market incentives on land cover change.",
                  doi = "10.1080/1747423X.2019.1709223",
                  url = "http://dx.doi.org/10.1080/1747423X.2019.1709223",
                 issn = "1747-423X and 1747-4248",
             language = "en",
           targetfile = "nascimento_bayesian.pdf",
        urlaccessdate = "26 abr. 2024"
}