Models, scenarios or ambition? A decomposition analysis of factors influencing future mitigation indicators.
Authors: Al Khourdajie Alaa , Skea Jim , Green Richard 
Published: February 2023

&lt;p&gt;In this paper we apply a statistical decomposition analysis in order to understand the drivers behind key mitigation indicators in long-term mitigation scenarios. These scenarios are generated using integrated assessment models (IAMs) which crucially require input assumptions of alternative futures described by the Shared Socioeconomic Pathways (SSPs). Additionally, IAMs generate these scenarios in order to stabilise the carbon budget to a pre-defined constraint that aligns with certain peak warming level under some probability. Given this framework for generating scenarios, we therefore identify three factors: model, scenario and warming ambition, which collectively determine the future evolution of the chosen key mitigation indicators, or any other variable for that matter, in these scenarios. Using statistical decomposition analysis, particularly variance decomposition, in this paper we attribute the individual contribution of these three factors separately to shaping the evolution of the chosen indicators. Such analysis is key for developing a deeper understanding of which factors are the most important in influencing these mitigating indicators, what does that mean for policymakers and sectoral practitioners in terms of interpreting the findings of these indicators and scenarios, and importantly what are the implications for the scientific community in cases where the contribution of the individual factors reflect lack of consensus within the community on current markets conditions and driving forces of future projections. We apply our analysis to the SSPs database, and focus on decadal years only, i.e. 2020, 2030 and so on. We focus on primary energy, final energy, emissions and land use indicators.&lt;/p&gt;
&lt;p&gt;Our initial analysis indicates that the primary energy generated by solar and wind is primarily influenced by the model factor, with a high level of residuals in the second half of the 21st century, indicating unexplained effects. Nuclear energy is primarily driven by the scenario factor, with the model factor playing an increasingly significant role in the latter half of the century, again with high levels of residual effects. Hydro power is mainly influenced by the model factor, while biomass is primarily driven by the ambition factor. The presence of residual effects in these indicators suggests a lack of consensus among studies as to the key market and technology conditions that shape their evolution. On the other hand, the trajectories of fossil fuels are characterized by high levels of agreement among studies, with the ambition factor playing a dominant role, except when coupled with carbon capture and storage (CCS) technology, in which case the scenario factor becomes more influential. When it come to final energy, electrification is mainly driven by the model factor, with low levels of residuals indicating agreement among studies. Hydrogen, carbon capture and storage, and bioenergy with carbon capture and storage (BECCS) are primarily influenced by the ambition factor, with very high residuals, indicating a lack of consistency in projections for these indicators. Sectors vary, where industry is largely driven by the model factor, buildings by the scenario factor, while transport by the model and scenario factors, with the residuals being small for all of them.&lt;/p&gt;