Getting models and modellers to inform deep decarbonization strategies

An increasing number of countries issue Deep Decarbonization Strategies (DDS). While DDS contents are well analysed, the processes by which they are developed attract less attention. This paper investigates what numerical model(s) are used in these processes, how they are used, and how models, modellers and stakeholders jointly contribute to the production of DDS. It draws lessons from an in-depth analysis of the second French national low-carbon strategy, complemented with insights from the US, Swedish and Brazilian DDS production processes. While configurations differ, DDS processes typically rely on multiple, sometimes overlapping models and involve a broad range of stakeholders. Articulating models together, and through stakeholder consultations, produces both numbers and collectives that share visions of the future–and both are equally important. Setting up and coordinating such assemblages of models and stakeholders requires effort, time, anticipation and resources. The cases presented here highlight the importance of technical, institutional and relational legacies (e.g. prior experience of joint work, hybrid communities), and of political support. We conclude on the importance for policymakers to account for these dimensions when setting up DDS processes and layout avenues for further research. Key policy insights The production of Deep Decarbonization Strategies (DDS) relies on multi-model, multi-stakeholder processes and requires developing models in consultation with stakeholders. These modelling assemblages produce numbers, shared visions and also relationships–all of which are equally important when it comes to elaborating DDS. Such assemblages require time, resources, trial and error, which in turn points to the importance of institutional, relational and technical legacies (e.g. prior experience with and around modelling tools, hybrid communities), as well as of political support, in facilitating this work. DDS elaboration processes constitute a learning experiment and present opportunities for technical and institutional innovation. Policymakers should anticipate and plan for these dimensions when deciding to produce DDS.