Forest and Landscape Restoration (FLR) is carried out with the objective of regaining ecological functions and enhancing human well-being through intervention in degrading ecosystems. However, uncertainties and risks related to FLR make it difficult to predict long-term outcomes and inform investment plans. We applied a Stochastic Impact Evaluation framework (SIE) to simulate returns on investment in the case of FLR interventions in a degraded dry Afromontane forest while accounting for uncertainties. We ran 10,000 iterations of a Monte Carlo simulation that projected FLR outcomes over a period of 25 years. Our simulations show that investments in assisted natural regeneration, enrichment planting, exclosure establishment and soil-water conservation structures all have a greater than 77% chance of positive returns. Sensitivity analysis of these outcomes indicated that the greatest threat to positive cashflows is the time required to achieve the targeted ecological outcomes. Value of Information (VOI) analysis indicated that the biggest priority for further measurement in this case is the maturity age of exclosures at which maximum biomass accumulation is achieved. The SIE framework was effective in providing forecasts of the distribution of outcomes and highlighting critical uncertainties where further measurements can help support decision-making. This approach can be useful for informing the management and planning of similar FLR interventions.

进行森林和景观恢复（FLR）的目的是通过干预生态系统退化来恢复生态功能并增进人类福祉。但是，与FLR相关的不确定性和风险使得难以预测长期结果并为投资计划提供依据。我们应用了随机影响评估框架（SIE）来模拟退化退化的Afromontane森林中进行FLR干预时的投资回报，同时考虑了不确定性。我们对Monte Carlo模拟进行了10,000次迭代，该迭代预测了25年内的FLR结果。我们的模拟表明，在辅助自然更新，富集种植，排泄物建立和水土保持结构方面的投资，都有超过77％的机会获得正回报。对这些成果的敏感性分析表明，对正现金流量的最大威胁是实现目标生态成果所需的时间。信息价值（VOI）分析表明，在这种情况下，进行进一步测量的最大优先事项是可实现最大生物量积累的排泄物的成熟年龄。SIE框架有效地提供了结果分布的预测，并突出了关键的不确定性，在这些不确定性中进一步的测量可以帮助支持决策。这种方法对于通知类似FLR干预的管理和计划很有用。信息价值（VOI）分析表明，在这种情况下，进行进一步测量的最大优先事项是可实现最大生物量积累的排泄物的成熟年龄。SIE框架有效地提供了结果分布的预测，并突出了关键的不确定性，在这些不确定性中进一步的测量可以帮助支持决策。这种方法对于通知类似FLR干预的管理和计划很有用。信息价值（VOI）分析表明，在这种情况下，进行进一步测量的最大优先事项是可实现最大生物量积累的排泄物的成熟年龄。SIE框架有效地提供了结果分布的预测，并突出了关键的不确定性，在这些不确定性中进一步的测量可以帮助支持决策。这种方法对于通知类似FLR干预的管理和计划很有用。