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Lucas, Nathalia, 2013. Efficiency of bolsa floresta program in the Brazilian amazon. Second cycle, A2E. Uppsala: SLU, Dept. of Economics



All over the world the increasing disappearance of natural habitats and ecosystems is concerning policy makers, populations and nongovernmental organizations. Many ecosystems such as forests are multifunctional landscapes and they provide a range of environmental services (benefits that people obtain from the ecosystem) that are vital for humankind and therefore worth preserving. Payment for environmental services (PES) has been applied increasingly in countries with high conservation concerns. PES is a monetary compensation scheme to “reward” environmental service providers for their actions in protecting the ecosystem. Normally the compensation amount is a random value, based on budget restrictions rather than an accurate sum of social net benefits. This is the case for the biggest PES scheme in Brazil; the Bolsa Floresta program in the Brazilian Amazon forest where the local indigenous populations receive a fixed monthly amount for preserving the forest. The paid fixed monthly value of approximately 30USD may not reflect the true value an Amazonian ecosystem- therefore the purpose of this study.
In a tropical rainforest context, as the Brazilian Amazon, the local population plays an important role when conservation is the final objective of an environmental policy. The decision making process requires an assessment of costs and benefits incurred by the local population. A compensation scheme is often proposed as a solution and part of conservation policy. To assess a fair compensation amount, the economic values associated with those services need to be calculated. Such values are calculated for biodiversity preservation, carbon services and foregone benefits of alternative land use. The selected assessment methodology is benefit transfer, which makes uses of the previously estimated costs and benefits drawn from other studies. Benefit transfer uses economic information captured at a study site to make inferences about the economic value of environmental goods and services at a policy site. The biggest advantage of choosing benefit transfer is to reduce the need for costly and time-consuming original study for non market values. However the disadvantage of the method is the requirement for adjustment due to differences between the study and policy sites. The determinants of willingness to pay for an environmental good can greatly vary according to socio-economic and demographic characteristics.
The optimal payment level is obtained by maximizing the society’s net welfare, when marginal values for producing both market and non market goods are equal. On the benefit
side, the values were based in previous studies estimates for biodiversity protection and carbon services values were taken from market estimations and international default values. On the cost side, the values were transferred from opportunity cost studies for alternative land use made by Brazilian authors. The choice of using benefit transfer gives access to a broad database but also makes inevitable some degree of imprecision and inaccuracy. The analysis of the studied costs and benefits shows that this PES scheme does not compensate opportunity costs and the optimal payment exceeds current payment schemes.

Main title:Efficiency of bolsa floresta program in the Brazilian amazon
Authors:Lucas, Nathalia
Supervisor:Gren, Ing-Marie
Examiner:Drake, Lars
Series:Examensarbete / SLU, Institutionen för ekonomi
Volume/Sequential designation:804
Year of Publication:2013
Level and depth descriptor:Second cycle, A2E
Student's programme affiliation:NM005 Environmental Economics and Management - Master's Programme 120 HEC
Supervising department:(NL, NJ) > Dept. of Economics
Keywords:Amazon, biodiversity, environmental services, environmental policy, forest, payment for environmental services
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Subject. Use of subject categories until 2023-04-30.:Economics and management
Deposited On:10 Jul 2013 12:27
Metadata Last Modified:10 Jul 2013 12:27

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