Potential environmental benefits from woodfuel transitions in Haiti: Geospatial scenarios to 2027. (1st March 2018)

• Record Type: Journal Article
• Authors: Ghilardi, Adrian; Tarter, Andrew; Bailis, Robert
• Is Part Of: Environmental research letters. Volume 13:Number 3(2018:Mar.)
• Publication Date: 2018-03-01
• Subjects: Haiti -- land use land cover change -- landscape modeling -- woodfuels -- deforestation and forest degradation; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/aaa846
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: 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 that current demand may contribute to moderate levels of degradation, but it is not as severe as is typically portrayed. Under a business-as-usual 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 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.