Public policy has grown increasingly reliant on complex analytical models that, while valuable, rarely integrate behavioural constraints in theoretically appropriate or practically relevant ways. By offering a simplistic representation of the real world, these models risk producing unrealistic results and leading to potentially myopic decision making.
For example, many complex socio-technical systems are modeled with the objective of minimizing costs or maximizing societal welfare. Although these models prescribe an optimal course of action for a decision maker, they ignore a large number of societal preferences that can slow technological transitions, including public opposition and inertia in the values and beliefs that determine societal norms and market preferences. Moreover, for models limited to a single economic objective, the optimal solution given to the decision maker is usually dependent on the developer’s structuring and parameterization of the model, and these decisions are subject to human bias—often excluding processes and parameter values that the developer ex-ante deems unlikely or infeasible.
These limitations are especially acute and consequential in models of the global climate and energy systems, which explore pathways to achieve deep decarbonization while minimizing cost or greenhouse gas (GHG) emissions. In elaborating these pathways, models choose among a range of energy technologies, some of which contend with low levels of public acceptance or heightened risk perceptions. Recognizing these limitations, some modellers have recently sought to explicitly incorporate challenges to technology deployment in the real world using exogenous choices—for instance, by constructing narrative scenarios that justify their choices, or by excluding certain technological options entirely (e.g. nuclear power) on the grounds that they are undesirable and contrasting those results with full portfolio, least-cost scenarios.
The goal of this project is to endogenously integrate societal preferences for different energy technologies into large-scale energy system optimization models. This is important because societal preferences have a direct bearing on the extent to which capital mobilizes in support of a technology’s adoption and diffusion, not to mention the range of political and policy measures that can be feasibly deployed to support it. By ignoring these real-world constraints, least-cost or least-emission optimization models generate unrealistic results.