The financing of tropical forest conservation projects through the sale of carbon credits remains a key opportunity to curb forest loss. Reducing emissions from deforestation and forest degradation (REDD)+ projects generate carbon credits by reducing forest loss within the project area compared with a counterfactual area that faces similar pressures (known as ‘additionality’). Several methods are available for constructing counterfactuals, but comparing their reliability is challenging. Here, we present an evaluation approach based on the creation of placebo projects, where there are no REDD+ activities and in which we would not expect project and counterfactual outcomes to diverge. We compare four methods based on pixel matching that estimate counterfactual deforestation rates. Using 27 placebo projects spread across the tropics, we found that pixel-matching is a reliable way of estimating a key element of additionality (i.e. deforestation in counterfactual areas) when based on data gathered at the end of an evaluation period after project start (i.e. ex-post estimation). However, forecasting counterfactual deforestation rates from information available at the start of a project (i.e. ex-ante estimation) is much less reliable, reinforcing existing concerns about ex-ante crediting mechanisms. We argue that systematic application of the placebo approach can accelerate the development and adoption of more credible counterfactual-estimating methods. As counterfactuals are the basis which underpins the validity claims of most nature credits, strengthening the credibility of counterfactuals will enhance the effectiveness of conservation finance, helping REDD+ and other nature-based solutions realise their full potential in delivering real, measurable benefits.

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