Policies aimed at Reducing Emissions from Deforestation and Forest Degradation (REDD+) can provide a way for tackling global warming and climate change. Deforestation is a transformation of forestland for various land uses. A spatial model of deforestation is considered for designing effective REDD+ policies in view of internalizing the external costs of deforestation. The causes of deforestation can be classified at different levels. In this study, the causes are considered at two levels as the direct (endogenous) and the indirect (exogenous) causes. An econometric model, recursive in nature, is estimated in two stages for analysing the interactions between the direct and the indirect causes. At the first stage, the direct causes are regressed on the indirect causes by Seemingly Unrelated Regression (SUR) method to account for the correlations between the direct causes. At the second stage, the SUR estimates of the direct causes are used for the regression of a deforestation equation. The results are discussed in relation to Asian, African and Latin American regions to provide an understanding of the mechanism for deforestation process at two levels. The spatial model presented, along with the regression results, can effectively provide guidance for designing REDD+ policies. Causes of Deforestation and Policies for Reduced Emissions (REDD+): A Cross-country Analysis - ResearchGate. Available from: http://www.researchgate.net/publication/270511966_Causes_of_Deforestation_and_Policies_for_Reduced_Emissions_(REDD)_A_Cross-country_Analysis [accessed May 12, 2015].
|Number of pages||21|
|Journal||The ICFAI Journal of Applied Economics|
|Publication status||Published - Oct 2014|