| Abstract |
As global commitments to decarbonization intensify, energy-emission models are becoming increasingly vital for policymaking, offering data-driven insights to evaluate the feasibility and impact of climate strategies. These models help governments design evidence-based policies, assess mitigation pathways, and ensure alignment with national and international targets, such as the Paris Agreement and the EU Green Deal. Researchers often spend a lot of time considering their modelling choices to develop the best possible tools in terms of data-requirements, accuracy, computational demand, while there is always a 'debate' of complexity versus explicability and ready-to-use models for policymaking. Especially for energy-emissions models, given their increasing policy-relevance, and the need to provide insights fast for short-term policies (e.g. 2030, or 2050 net-zero goals), such considerations become increasingly pressing. In this paper, we present two different versions of the same energy-emissions model, and we run them for the same study area, planning horizon, and scenario analysis. The two versions differ only in how they approach complexity: Version1 is a more 'detailed', complex model, while Version2 is a 'simpler' and less data-hungry one. A set of evaluation criteria was then used to qualitatively compare these two versions, based on modelling- and policymaking-related considerations, debating modelers' and policymakers' expectations and preferences. We reflect on best modelling practices, discuss different goal-dependent approaches, providing useful guidance for modelers and policymakers. |
