We show that models are used and have an impact on policymaking. Depending on countries context, we reveal that models are used to push ambitious climate and energy policy, while in other cases models are not used at all, or model results are used to justify political inaction. We also show that modelling tools function as ‘laboratories of sustainable transition’ and support decision-making processes along the whole policy-cycle: from target setting, through policy formulation to evaluation. Models are especially useful when they are set up to directly answer specific questions that policymakers might have, i.e. to explore the implications of options that they are considering. In contrast, they are less useful when they tell policymakers what course of action, from the modeller’s perspective, would be best. We find, however, that model use is also limited, because of the complexity of modelling processes, as well as the lack of open data and open-source models. In the end, models have to compete with other information sources and concerns. We also show that policymakers influence models and modellers. Government-commissioned modelling allows policymakers to set the framework conditions of modelling performed. Even a higher level of the policymakers’ influence is reflected by deciding how models and their results are politically used. Overall, the case studies demonstrate, energy modelling and policymaking can influence each other ‘for the good and for the bad’: they can foster radical policy changes and ambitious target setting, or they can be used to justify inaction and radical no-change, respectively.
In SENTINEL, we apply these insights to improve and link our models. We do so not around problems chosen by researchers, but rather in the course of case studies at a range of geographic scales. Linking models designed as stand alone entities is a challenge and we have a full time coder working with the separate modelling teams in order to develop protocols needed to soft-link the results. In a second phase of linkages, we will allow for the propagation of uncertainties across the linked models. The entire packages will then be available on a single platform. Our goal is to make models more useful to a wide set of stakeholders than they have been in the past, in a manner that is open, robust, and transparent.
