Proportionate Adaptation and Policy Mixes under Uncertainty

Policies are continually being designed for current and future conditions about which policymakers often have incomplete or no information at all (deep uncertainty including ‘known unknowns’ ‘unknown unknowns’) (Walker et al, 2001). Climate change is a good example of a complex and unstructured policy problem that is characterized by a high degree of uncertainty. Given the likelihood of non-linearity in the future climate, the impacts associated with climate change may be manifested to varying extents (IPCC, 2007). However other kinds of policies exist in which uncertainty is minimal or manageable, such as what happens in transportation policy-making or health policy-making where historical data and linear relationships greatly reduce risks associated with uncertain futures. Failing to correctly identify the bounds and range of these uncertainties is a major cause of policy over and under-reaction (Maor, 2012; Maor, 2014) and over and under-design. Using the example of adaptation policies for climate change, this paper argues that for policy design under uncertainty it is important to utilize ‘appropriate’ design processes that can generate outcomes that are ‘proportionate’ to the level of change in the policy environment (uncertainty). The concern about proportionality of response stems from the need to allocate competing resources judiciously and plan to build necessary adaptive capacities, anticipate risks and vulnerabilities to reduce impacts and potentially avoid maladaptation. The literature reveals that different types of policy processes can be designed to deal with different levels of change in the policy environment (different levels of uncertainty). Broadly these policy processes can be incremental, coping, innovative and reflexive. Such processes and their usefulness in dealing with uncertainty in the policy environment and ability to elicit a proportionate response will be discussed and critiqued in this paper.