Achieving the ambitious climate targets set by the Paris Agreement requires a rapid transition to defossilised energy systems. The Intergovernmental Panel on Climate Change (IPCC) is instrumental in providing comprehensive assessments and policy guidance, with Integrated Assessment Models (IAM) and associated climate change mitigation scenarios being central to these evaluations. This study conducts a systematic analysis of global climate change mitigation scenarios in the IPCC Sixth Assessment Report targeting at most 1.5°C warming levels to evaluate the role of renewable energy and electrification in their mitigation pathways. Remarkably, 8.5% of scenarios analysed a target near the safe and just planetary boundaries of 1.0°C/350 ppm, essential to ensure a safe climate for civilisation. Furthermore, most IAM scenarios overestimate the role of bioenergy and fossil fuels with carbon capture and sequestration, and do not anticipate the expected growth of renewable energy technologies due to the largely missing concept of power-to-X in modelling structures. Of the 143 scenarios targeting warming to 1.5°C, only 30% project a 55% or higher share of primary renewable electricity in primary energy demand and 97% exceed the global sustainable bioenergy potential in 2100. The findings highlight the critical need for greater input assumption transparency in IAMs and to incorporate insights from a wider array of modelling perspectives to ensure credible policy recommendations regarding renewable energy and power-to-X deployment. Such advancements, via improvements to IAMs or collaboration with advanced energy system models, can guide more informed investment strategies, ultimately accelerating the transition towards safe and just climate conditions. • IAM scenarios tend to favour bioenergy instead of primary renewable electricity. • The limited power-to-X routes constrain the growth of renewables. • Greater transparency and inclusion of diverse perspectives are needed in ARs. • Cost assumptions regarding the key renewable technologies should be re-evaluated. • There are severe inconsistencies in reporting practices of models.
Bıçakçı et al. (Sat,) studied this question.