Knowing what we don't know (and won't learn): Environmental Regulation under ''Conscious Unawareness'' and ''Negative learning''

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Optimal scale and timing of climate change mitigation measures crucially depend on what we do and do not know about future climate outcomes as well as the rate at which we expect to improve our understanding via scientific progress. While the presence of large uncertainties is typically invoked for timely and substantive action, the prospect of diminishing uncertainty through quick learning supports opposite calls for postponing costly and irrevocable mitigation measures until a better state of knowledge has been reached. What complicates this and similar environmental debates is the nature of uncertainty society is typically confronted with. Not only is it impossible to pinpoint exact probability distributions governing the system's behavior, decision-makers are often even unaware of outcome-relevant contingencies like yet undiscovered climate feedback processes. These ''unknown unknowns'', aggravating things further, also tend to make new information unproductive or even misleading, so-called ''negative learning''. Environmental Economics has developed a rich tool-kit for informing the normative analysis of environmental regulation. While it has repeatedly adopted insights from decision-theory and Bayesian inference to substantiate decision-making under uncertainty and learning, what Environmental Economics currently still cannot address in a sound and substantiated way is the presence of unknown unknowns and negative learning. This research project in theoretical Environmental Economics will make the first step in closing this gap, thus adding an important dimension to the existing literature. Its main contribution and challenge is to complement studies on environmental regulation with recent achievements in decision-theory. The decision-theoretic ''unawareness'' literature has developed frameworks capable of capturing set-ups in which a decision-maker is unaware of relevant contingencies. Importantly, one strand of this literature has specified to what extent a decision-maker can be aware of her own limited understanding. This ''conscious unawareness'' is a particularly apt description of the typical societal level of information in the regulation of environmental problems and thus promises to be a valuable extension of existing models in Environmental Economics. The first work package will develop a tractable framework that makes the abstract decision-theoretical insights accessible to a wider audience. Based on this methodological contribution, further work packages will add novel angles to the general literature on the Precautionary Principle, the debate about the optimal timing of climate actions, and the conditions for negative learning. Bringing these insights together, the final work package will deliver an integrated climate assessment framework for determining optimal climate decision-making under conscious unawareness and negative learning.