of interest. They are usually collected by national statistical offices, government ministries, universities, international organizations or private corporations. Observed outcomes need to meet minimum data quality standards such as sufficient coverage, availability of historical data, accessibility to the wider public and transparency about their methodology.

**Example** Annual primary forest loss in km<sup>2</sup> .

# **2.2 Benchmark outcome**

The benchmark outcomes represent what outcome we would expect without policy intervention in the absence of the SLB issuance. However, the benchmark outcome is not observed because we only observe the outcome with policy intervention. Therefore, this _counterfactual_ outcome has to be estimated using a statistical model, possibly incorporating data from other countries or other indicators. An appropriate, interpretable, robust and transparent benchmark model is essential for the credibility and effectiveness of the REACH framework.

**Example** Expected annual primary forest loss in km<sup>2</sup> , controlling for existing policy, earlier deforestation, previous exchange rates and commodity prices.

# **2.3 Benchmark model**

To produce a credible counterfactual, the benchmark model needs to provide clear and convincing reasoning why its results can be used for performance evaluation.4 We suggest that benchmark models should fulfill the following three requirements:

- (R1) **Transparent benchmark model.** The benchmark model should articulate which factors it considers as being in the issuer’s control and which it considers exogenous. This clarifies immediately how the SLB defines performance towards the SPT. A transparent model specification is also beneficial for communication and instrument pricing and allows the desired level of performance to be specified by choosing the cutoffs and/or step sizes (see Table 1) given historical performance.

- (R2) **Benchmark probability distribution.** The estimated benchmark outcome should be accompanied by a probability distribution around the preferred estimate to understand the likelihood of alternative outcomes. This is necessary to clarify whether the measured performance is due to chance or constitutes actual performance beyond a reasonable statistical doubt. It is worth emphasizing that the benchmark distribution is not merely a tool for performance evaluation. A good understanding of uncertainty around the performance measure is also necessary for accurately setting performance incentives (see Section 3).

- (R3) **Justification for additionality.** The benchmark model needs to be supported by a rigorous, evidence-based justification for why it can be used to measure performance and additionality. Ideally, these claims are supported by historical evaluations and back-testing results. Two possible justifications for additionality can be derived from the _informativeness principle_ (see Section 3.4): first, using the _controllability principle_ which stipulates that incentives should only depend on influenceable factors, and second, through _relative performance evaluation_ , where the performance can be gauged through the behavior of relevant peers.

> 4Antle et al. (1988) define performance evaluation in their model as being about “producing information relevant to the question of whether the desired behavior was supplied”.

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