Publication details
Visions before models : The ethos of energy modeling in an era of transition
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Periodical |
| Magazine / Source | Energy Research & Social Science |
| MU Faculty or unit | |
| Citation | |
| Web | https://www.sciencedirect.com/science/article/pii/S2214629622000056 |
| Doi | http://dx.doi.org/10.1016/j.erss.2022.102497 |
| Keywords | Energy modeling; Energy policy; Forecasting; Backcasting; Validation; Participatory modeling |
| Attached files | |
| Description | Energy-Economy-Environment (E3) models feature prominently in energy policy and climate mitigation planning. Nevertheless, these models have a mixed track record when assessed retrospectively and exhibit biases that can make them counterproductive for prescriptive policy during transition. We argue that in times of energy transitions it is preferable to develop a vision of the desired future energy system rather than relying on techno-economic solutions based on simple objectives (e.g. lower carbon emissions). We support this argument through reasoned inference supported by historical examples. A critical appraisal of E3 modeling exercises highlights the biases, structural or implicit, favoring existing energy system modalities. As a result, if E3 models are uncritically used to formulate long-term energy policy, there is the risk of unintended or deliberate performativity preventing a radical transition. Given the significant learning-by-doing effects in reducing technology costs, the evolution of energy systems is path-dependent and reinforced by technology policy feedbacks. This is showcased by Germany's Energiewende. Therefore, it is preferable to prioritize a clear articulation of the vision for the future desired end-state which can be shared with stakeholders a priori. Then utilize models as exploratory tools for assessing the economics and scale of corresponding interventions. These should include focused technology policy that aims to commoditize relevant technical innovations through learning-by-doing and scale economies. Ideally such models should be open, exploratory, reflexive and incorporate the dynamics of innovation. |
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