In June 2017, the federal government released a discussion paper that outlines the changes being considered for Canada’s environmental assessment and regulatory processes. The changes are intended to:
- Regain public trust;
- Protect the environment;
- Advance reconciliation with Indigenous peoples and,
- Ensure good projects go ahead and resources get to market.
I would like to provide some reflections from a computational biologist’s perspective on the first two objectives.
Firstly, in order to regain the public trust, the results should be reproducible (Peng 2009). This means all available data (with the possible exception of that related to the spatial location of endangered species) and analysis code should be publicly available.
Secondly in order to protect the environment, decisions should be based on confidence intervals rather than p-values (Gardner and Altman 1986). Confidence intervals indicate the range of possible impacts while p-values indicate whether the confidence intervals include no impact.
Currently projects are only considered to have an impact if the 95% confidence intervals exclude no impact from the range of possible values, ie, the p-value is less than 0.05. This approach violates the precautionary principle since a project is assumed to not harm the environment unless sufficient data are collected to demonstrate that the absence of an impact is unlikely. As a result data collection is disincentivized and harmful projects are permitted.
Instead projects should only be permitted if it can be established that the upper 95% confidence limit is below a pre-specified threshold, ie, the maximum impact is within an environmentally and socially acceptable range. Not only is this approach consistent with the precautionary principle but it also incentivizes meaningful data collection and ensures that impacts are understood and acceptable.
Gardner, M J, and D G Altman. 1986. “Confidence Intervals Rather Than P Values: Estimation Rather Than Hypothesis Testing.” BMJ 292 (6522): 746–50. https://doi.org/10.1136/bmj.292.6522.746.
Peng, R. D. 2009. “Reproducible Research and Biostatistics.” Biostatistics 10 (3): 405–8. https://doi.org/10.1093/biostatistics/kxp014.