Delivering on the Paris Agreement: A demand-driven, integrated assessment modelling approach

ended

PARIS REINFORCE aims to underpin climate policymaking with authoritative scientific processes and results, and enhance the science-policy interface, in light of the Paris Agreement and associated challenges. In particular, our aim is to develop a novel, demand-driven, IAM-oriented assessment framework for effectively supporting the design and assessment of climate policies in the EU as well as in other major emitters and selected less emitting/developed countries, in respect to the Paris Agreement objectives. Building on an exhaustive facilitative dialogue and a strong ensemble of complementary—in terms of mathematical structure, geographical, sectoral and focus coverage—integrated assessment, energy system and sectoral models, we will create an open-access and transparent data exchange platform, I2AM PARIS, in order to support the effective implementation of Nationally Determined Contributions, the preparation of future action pledges, the development of 2050 decarbonisation strategies, and the reinforcement of the 2023 Global Stocktake. We also seek to enhance the legitimacy of the scientific processes in support of climate policymaking, by introducing an innovative stakeholder inclusion framework and improving the transparency of the employed models, methods and tools. Beyond effectively communicating respective outputs and fostering wider societal acceptance of climate policy, we actively involve policymakers and other stakeholder groups in all stages: from the formulation of policy questions and the definition of modelling assumptions in a demand-driven approach; to the design of I2AM PARIS interfaces and specifications, and the mobilisation of tacit knowledge embedded in them in the aim of bridging knowledge gaps. Finally, we will introduce innovative integrative processes, in which IAMs are further coupled with well-established methodological frameworks, in order to improve the robustness of modelling outcomes against different types of uncertainties.