Robust Enough? Exploring Temperature-Constrained Energy Transition Pathways under Climate Uncertainty

In this paper, we study how uncertainties weighing on the climate system impact the optimal technological pathways the world energy system should take to comply with stringent mitigation objectives. We use the TIAM-World model that relies on the TIMES modelling approach. Its climate module is inspired by the DICE model. Using robust optimization techniques, we assess the impact of the climate system parameter uncertainty on energy transition pathways under various climate constraints. Unlike other studies we consider all the climate system parameters which is of primary importance since: (i) parameters and outcomes of climate models are all inherently uncertain (parametric uncertainty); and (ii) the simplified models at stake summarize phenomena that are by nature complex and non-linear in a few, sometimes linear, equations so that structural uncertainty is also a major issue. The use of robust optimization allows us to identify economic energy transition pathways under climate constraints for which the outcome scenarios remain relevant for any realization of the climate parameters. In this sense, transition pathways are made robust. We find that the abatement strategies are quite different between the two temperature targets. The most stringent one is reached by investing massively in carbon removal technologies such as bioenergy with carbon capture and storage (BECCS) which have yields much lower than traditional fossil fuelled technologies.

The Role of Industrial Revival in Untapping the Bioeconomy’s Potential in Central and Eastern Europe

The bioeconomy occupies the centre of the Green Deal, the EU’s plan to support transformative growth following the COVID-19 episode. However, parts of the EU, such as countries in Central and Eastern Europe (CEE) continue to lag behind in harnessing the potential held by the bioeconomy. This article argues that in CEE countries, where the primary and conventional bioeconomy sectors play a more important role, ‘early’ transition pathways such as improvements in productivity and practice- as well as commercialisation-oriented innovation (the do–use–interact model: DUI) are just as important as approaches based on (generally publicly supported) R&D, innovation adoption, and technology transfer (science–technology–innovation model: STI), typically associated with high-value bioindustrial applications. The argument is tested by conducting a survey of 352 experts in the region that gives an insight into the CEE macro-region’s assets with respect to deploying the bioeconomy’s potential and assessing the transition pathways relevant to the better performance of bioeconomy (primary, manufacturing, and other related) sectors. The results show the particular relevance of consolidating the primary and traditional sectors to support improvements in productivity based on the vertical and horizontal interaction typically associated with DUI, while the relevance of STI is mostly linked to advanced sectors, which are narrowly distributed across the region. The findings are relevant to policy given that the EU’s bioeconomy policy has thus far chiefly focused on STI support that better corresponds to the needs of countries at more advanced stages of developing the bioeconomy, but is less appropriate for the specific context and needs of CEE.

Complex pathways and memory in compressed corrugated sheets

The nonlinear response of driven complex materials—disordered magnets, amorphous media, and crumpled sheets—features intricate transition pathways where the system repeatedly hops between metastable states. Such pathways encode memory effects and may allow information processing, yet tools are lacking to experimentally observe and control these pathways, and their full breadth has not been explored. Here we introduce compression of corrugated elastic sheets to precisely observe and manipulate their full, multistep pathways, which are reproducible, robust, and controlled by geometry. We show how manipulation of the boundaries allows us to elicit multiple targeted pathways from a single sample. In all cases, each state in the pathway can be encoded by the binary state of material bits called hysterons, and the strength of their interactions plays a crucial role. In particular, as function of increasing interaction strength, we observe Preisach pathways, expected in systems of independently switching hysterons; scrambled pathways that evidence hitherto unexplored interactions between these material bits; and accumulator pathways which leverage these interactions to perform an elementary computation. Our work opens a route to probe, manipulate, and understand complex pathways, impacting future applications in soft robotics and information processing in materials.