Sustainable solvents are a topic of growing interest in both the research community and the chemical industry due to a growing awareness of the impact of solvents on pollution, energy usage, and contributions to air quality and climate change. Solvent losses represent a major portion of organic pollution, and solvent removal represents a large proportion of process energy consumption. To counter these issues, a range of greener or more sustainable solvents have been proposed and developed over the past three decades. Much of the focus has been on the environmental credentials of the solvent itself, although how a substance is deployed is as important to sustainability as what it is made from. In this Review, we consider several aspects of the most prominent sustainable organic solvents in use today, ionic liquids, deep eutectic solvents, supercritical fluids, switchable solvents, liquid polymers, and renewable solvents. We examine not only the performance of each class of solvent within the context of the reactions or extractions for which it is employed, but also give consideration to the wider context of the process and system within which the solvent is deployed. A wide range of technical, economic, and environmental factors are considered, giving a more complete picture of the current status of sustainable solvent research and development.

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In order to reach EU's goal of zero emissions in 2050, the energy system will go through a significant transition over the next decades. To substitute fossil energy carriers, renewable energy sources will be mainly integrated in the power system. Thereby, sector coupling will play a major role by making flexibility from other sectors such as heat or transport accessible to the power system. Planning the cost optimal transition requires a whole-system view over multiple horizons and across all sectors. This imposes the need for multi-energy system (MES) models coupled with multi-horizon investment models. This paper presents two multi-horizon planning approaches to determine the cost optimal pathway of a MES. As a major contribution, we propose a new method to incorporate technology-dependent learning cost curves in the planning problem and show that the resulting mixed-integer linear programming problem can be solved faster with a Benders decomposition technique as compared to a closed optimization. As a further contribution, we demonstrate the usefulness of our approach by showing the MES expansion pathway for a small German test system.

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<p>The marine ice sheet instability may have already been initiated in several glaciers in West Antarctica. Hence controlling global temperatures is unlikely to be an effective way of preventing considerable sea level rise. This limits both the utility of greenhouse gas mitigation and solar radiation geoengineering as control mechanisms. Instead we evaluate various other options such as allowing ice shelves to thicken by reducing bottom melting, or slowing ice streams by drying their beds. We consider the engineering limitations, costs, and practical consequences of various designs and how a ladder of implementation might be climbed with regard to learning from Greenland and small-scale field trials. The governance, ethics, legality and societal implications for the local indigenous and global South are also discussed.</p>

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Abstract. Inspired by recent advancements in the field of computer vision, specifically models for generating higher-resolution images from low-resolution images, we investigate the utility of a deep convolutional autoencoder for downscaling and bias correcting climate projections for South East Asia (SEA). Downscaled projections of 2 m surface temperature are generated, using autoencoders trained with data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and data from the fifth generation ECMWF atmospheric reanalysis (ERA5) project. Using CMIP5 projections as an input, three sets of downscaled data are generated using three methods of autoencoder training, which allow us to determine how autoencoder downscaling and bias correction modify temperature values. Where possible, the downscaled outputs are compared against the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA) project and outputs from available CMIP6 experiments, to evaluate performance. The autoencoders are found to excel at the rapid generation of highly spatially-resolved climate projections for surface temperature. Realistic spatial features due to coastal and topographic variation are generated by the autoencoder, which are not present in the CMIP5 projections. Additionally, the autoencoders are capable of generating forecast data with regional temperature profiles exceeding that of those appearing in the training set (out-of-sample extrapolation). Seasonal temperature cycles are retained after downscaling throughout the region, despite the absence of temporal information provided to the model. However, autoencoders trained to carry out bias correction display a tendency to smooth daily average temperatures and reduce daily highs and lows beyond that which can be expected to be realistic. Without bias correction, downscaled outputs have a reduced improvement in spatial resolution but the daily temperature profiles of the CMIP5 input forecasts are maintained. Autoencoders rely on the presence of structural features in the datasets to carry out downscaling, and so performance over the oceans is reduced as strong temperature gradients are absent. For this reason, ocean warming is not well represented, an artefact which is not immediately clear in the downscaled outputs. This study demonstrates the importance of rigorous analysis of 'black-box' methods, which can generate non-obvious artefacts that could potentially create misleading results. Despite these limitations, Autoencoders are clearly capable of generating much needed high-resolution climate projections, and strategies to improve upon shortcomings are numerous and well established.