Deepening transparency about value-laden assumptions in energy and environmental modelling: improving best practices for both modellers and non-modellers

Transparency and openness are broadly endorsed in energy and environmental modelling and analysis, but too little attention is given to the transparency of value-laden assumptions. Current practices for transparency focus on making model source code and data available, documenting key equations and parameter values, and ensuring replicability of results. We argue that, even when followed, these guidelines are insufficient for achieving deep transparency, in the sense that results often remain driven by implicit value-laden assumptions that are opaque to other modellers and researchers, and that may not be understood by wider audiences to be controversial. This paper identifies additional best practices for achieving transparency by highlighting issues where disagreement over value judgements will persist for the foreseeable future. Recommendations for deepening transparency are developed by learning from successes and ongoing challenges represented in three case studies. We provide recommendations to accelerate the adoption of additional best practices for deepening transparency of energy and environmental modelling in policy-relevant domains, increasing stakeholder participation with non-modellers, and encouraging interdisciplinary dialogue.Key policy insights Achieving all of the goals associated with transparency requires more than current practices of providing open source data, code, and model documentation.Greater interdisciplinary dialogue could improve transparency beyond current practices, including in model development, application, and communications.Better practices for addressing contentious and value-laden assumptions include providing accessible documentation for non-specialists, increasing policymaker participation to ensure that model outputs can inform questions, and performing sensitivity analyses that cover a range of reasonable views about value-laden assumptions.Energy and environmental modellers should account for audience-specific considerations to promote transparency, especially accounting for needs of non-modellers such as policymakers.