Improving averted loss estimates 397
uncertain, as the ability to make defensible predictions decreases.
Sources of error when estimating likelihood of loss There are three main sources of error when estimating the likelihood of loss:
Lack of explicit assumptions about counterfactual scen- arios If an offset policy provides for averted loss offsets, there is an assumption that the counterfactual scenario is one of ongoing loss (Maron et al., 2015, 2018). However, as we have shown above, the estimated rate of this assumed loss is often not explicitly stated.
Failure to distinguish between drivers of loss that would trig- ger offset requirements and those that would not For the purpose of estimating likelihood of loss, impacts on bio- diversity can be categorized into two types. Type I impacts are any impacts caused by an activity subject to legislative or policy controls that, following implementation of the mitigation hierarchy, require an offset,whereas type II im- pacts are all impacts resulting from activities that are not addressed by legislation or policy (Maron et al., 2018). When type I impacts are captured in probability of loss es- timations (P), the amount of benefit calculated is incor- rectly claimed. This is because type I impacts are subject to policy controls meaning that losses would be avoided or offset, thus any loss of the site would have to be ba- lanced elsewhere, so the gain in protecting a site from type I impacts is zero.
Cognitive biases These can occur as loss aversion bias, availability heuristic, and probability neglect bias. Loss aver- sion bias stems from a tendency of humans to be risk-averse, placing more emphasis on perceived losses than gains and focusing more on perceived consequence than likelihood of occurrence (Tversky & Kahneman, 1974; Kahneman et al., 1991). Thus, concerns over the consequence of future biodiversity loss can unduly influence the estimates of the likelihood of this loss actually occurring when the stakes are high (e.g. when the site contains threatened taxa), be- cause we wish to avoid the loss. The availability heuristic results from a tendency to make assessments based on the most recent information received (Tversky & Kahneman, 1974; Kahneman et al., 1991). Therefore, if development has recently occurred in a similar area, it is plausible to over- estimate the likelihood of loss at an offset site. The probabil- ity neglect bias occurs when uncertainty surrounding the likelihood of an anticipated event occurring in the future is high. A greater range of probabilities can appear plau- sible under such circumstances. Thus, when faced with
the inherent uncertainty in decision-making that involves predictions, people have a tendency to disregard probability (Sunstein, 2003).
These errors, cognitive biases, and uncertainties can
influence a decision-maker’s ability to make an unbiased judgement of the likelihood of site loss, particularly when credible evidence is scarce. These factors often work in combination and typically result in an overestimation of the benefit of averted loss offsets. Recognizing this is chal- lenging because in many cases protecting a site from future threats, by securing legal protection, is generally considered a positive outcome. It is counterintuitive to consider that such an action at the site or project scale may be detrimental at the policy or landscape scale. For parties with a vested interest inminimizing the costs
of meeting offset obligations, there is an incentive to over- estimate the benefit of averted loss offsets (Gordon et al., 2015; Maron et al., 2016). The combination of cognitive biases, errors and asymmetric information provide consid- erable scope for the manipulation of likelihood of loss esti- mates, which can result in the selection of low-quality offset sites (Ferraro, 2008; Ruhl & Salzman, 2011). Clear guidance is therefore needed to reduce such influences on likelihood of loss estimates.
Improving transparency and credibility of estimates of future biodiversity loss
To overcome the issues outlined above we propose an ob- jective, robust, and repeatable process for calculating ap- propriate likelihood of loss estimates under both the offset (Po) and the counterfactual (Pwo) scenarios. Our proposed method uses demonstrated past rates of loss to inform es- timates of future likelihood of loss and is underpinned by five principles: (1) Recent past rates of loss in similar sites are usually a sound basis for predicting future rates of loss and should be used where available. (2) The likelihood of loss is site-specific but estimates should be informed by landscape-scale estimates. (3) Estimates of particularly high likelihood of loss at a site must be supported by cred- ible and robust evidence. (4) The time horizon over which likelihood of loss is estimated, and thus the time over which benefit from averting loss is accrued, is clearly de- fined. (5) Type I impacts (those caused by activities that would be subject to legislative or policy controls requiring an offset) are excluded from likelihood of loss estimates. These principles underpin our proposed method for es-
timating likelihood of loss under scenarios with and without offset action. The method is detailed below and illustrated in Fig. 2 (estimating likelihood of loss under a counterfactual scenario, Pwo) and Fig. 3 (estimating likelihood of loss under the offset scenario, Po).
Oryx, 2021, 55(3), 393–403 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319000528
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