(task) Potential environmental benefits from woodfuel transitions in Haiti: Geospatial scenarios to 2027 | Andrew Tarter, Ph.D. | Pulse | LinkedIn

Haiti Resilience System

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Forests, Energy, Environment

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> https://www.linkedin.com/pulse/potential-environmental-benefits-from-woodfuel-haiti-tarter-ph-d- <https://www.linkedin.com/pulse/potential-environmental-benefits-from-woodfuel-haiti-tarter-ph-d->
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> Potential environmental benefits from woodfuel transitions in Haiti: Geospatial scenarios to 2027
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> Andrew Tarter, Ph.D. <https://www.linkedin.com/in/andrewtarter>January 24, 2018
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> By Adrian Ghilardi, Andrew Tarter and Robert Bailis
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> "Woodfuels constitute nearly 80% of Haiti's primary energy supply. Forests are severely degraded and the nation has long been considered an archetypal case of woodfuel-driven deforestation. However, there is little empirical evidence that woodfuel demand directly contributes to deforestation, but may contribute to degradation. We use MoFuSS (Modeling Fuelwood Sustainability Scenarios), a dynamic landscape model, to assess whether current woodfuel demand is as impactful as it is often depicted by simulating changes in land cover that would result if current demand continues unabated. We also simulate several near-term interventions focused on woodfuel demand reduction to analyze the land cover impacts of different energy trajectories. We find current demand may contribute to moderate levels of degradation, but it is not as severe as is typically portrayed. Under a business-as-usual (BAU) scenario, the simulated regenerative capacity of woody biomass is insufficient to meet Haiti's increasing demand for wood energy and, as a result, between 2017 and 2027 stocks of above-ground (woody) biomass (AGB) could decline by 4 ± 1%. This is an annual loss of 302 ± 29 kton of wood and would emit 555 ± 54 kton CO2 yr-1. Aggressive interventions to reduce woodfuel demand could slow or even reverse woodfuel-driven degradation, allowing woody biomass to recover in some regions. We discuss the policy implications and propose steps to reduce uncertainty and validate the model."
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