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Deforestation Rate in the Long-run: the Case of Brazil

Author

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  • Di Corato, Luca
  • Moretto, Michele
  • Vergalli, Sergio

Abstract

In this article we study the long-run average rate of forest conversion in Brazil. Deforestation results from the following trade-off: on the one hand, the uncertain value of benefits associated with forest conservation (biodiversity, carbon sequestration and other ecosystem services), on the other hand, the economic profits associated with land development (agriculture, ranching, etc.). We adopt the model by Bulte et al. (2002) as theoretical frame for studying land conversion and then derive, following Di Corato et al. (2013), the associated long-run average rate of forest conversion. We then identify the parameters to be used in our model. The object of our simulation is Brazil and 27 states. Our aim is to compute under several scenarios the time required to develop the remaining forested land in these states. We provide potential future scenarios, in terms of forest coverage, for the next 20, 100 and 200 years. Our results suggest that the uncertainty characterizing forest benefits plays a relevant role in deterring deforestation. We find that these benefits, if growing at a sufficiently high rate, may significantly slow down the conversion process. In contrast, a higher volatility accelerates the process of deforestation. We indicate the Brazilian states where forests are expected to be saturated earlier. In this respect, we find that forestland currently available may be expected to be fully converted within a 200-year horizon.

Suggested Citation

  • Di Corato, Luca & Moretto, Michele & Vergalli, Sergio, 2016. "Deforestation Rate in the Long-run: the Case of Brazil," EIA: Climate Change: Economic Impacts and Adaptation 244528, Fondazione Eni Enrico Mattei (FEEM).
  • Handle: RePEc:ags:feemei:244528
    DOI: 10.22004/ag.econ.244528
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    Keywords

    Environmental Economics and Policy;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q24 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Land
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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