I suggested in my blog on ‘Time-inconsistency’: the heart of climate mitigation as a policy problem’ that our current hegemonic paradigm of classical reductive science might be a reason why we are battling to respond adequately to the urgent and long term policy problem that is climate mitigation. From this paradigm, we typically understand time to consist of uniform, interchangeable units that proceed in a linear fashion from the present through the short – medium and then to the long term.
A complex systems view of time arises from a different logic: there is no long-term, short-term time distinction (Meadows, 2008). Rather, various sub-systems evolve according to different rhythms. Think here of the building of big industrial plant, or rail infrastructure, compared to social media topics, or fashion. In concert, this differentiation of timing results in the long periods of stability punctuated by moments of rapid change in large and complex social systems. As Lenin observed: there are decades where nothing happens, and then decades happen in weeks. Time from complexity is non-linear and multiple, with different time-scales nested within each other.
So what might this alternative ‘complexity’ paradigm reveal in terms of policy mechanisms to address the urgency and long-term characteristics of climate mitigation? Here are four initial ideas gathered from the (few) policy writers who are exploring complexity – I expect though that if this paradigm gains traction it may support a rich stream of insights going forward.
First, policy interventions need to be differentiated according to time as a dimension across various sub-systems. What needs to be tackled where and when (and how?). This directly addresses the urgency issue, and is a priority under a complexity view.
Second, non-linear system mechanisms provide opportunities: Levin et al (2012) write of developing path dependent processes in a low carbon direction, using the logic of positive feedback loops. By paying attention to entrenching and expanding the sub-systems supporting a policy intervention – designing policy for ‘stickiness’ – small incremental changes can gather pace and power without encountering opposition from powerful incumbents at the point of policy promulgation.
Third, Meadows (2008) advocates using the creation of negative (balancing) feedback loops to keep the system evolving in the direction of the low carbon objective. A climate mitigation example would be a carbon tax that rises with the level of fossil fuel exploitation in an economy.
And fourth: The cognitive heuristics described in ‘Time-inconsistency’: the heart of climate mitigation as a policy problem’ are related to delays in feedback loops between the excessive greenhouse gas emissions and the consequences. Policies that create new balancing information feedback loops may reduce the effect of these delays and support the system to adhere to a low carbon direction.