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Abstract This study aims to assess the efficiency of the fossil fuel taxation scheme currently in effect in Switzerland. To this end, the concept of implicit CO2 prices is introduced, based on which prices for different fossil fuel uses are derived. Implicit CO2 prices are defined as the difference between actual prices paid by consumers and efficient domestic fuel prices. Efficient domestic fuel prices, in turn, consist of private production costs, a uniform value added tax and only local external costs, not including external costs due to CO2 emissions and global climate change. The resulting prices differ substantially, which suggests that there is considerable cost-saving potential in reducing CO2 emissions in Switzerland. For passenger cars and air traffic, the implicit prices are negative. For these uses, higher fuel charges would therefore be beneficial from a purely domestic perspective, i.e., without considering the negative repercussions of global warming.

Abstract In this study, a flat-parallel-plate active magnetic regenerator made of gadolinium is investigated. The coupling of a one-dimensional thermal model with a two-dimensional magnetic model is presented. The thermal model takes into consideration the magnetocaloric effect as a source term and the energy conservation between a solid and a fluid. The magnetic model considers the space distribution of the internal magnetic field strength, and thus it includes the demagnetization effect. Measurements on an experimental magnetic refrigeration test device are performed using distilled water as the working fluid. At cyclic steady states, the temperatures of the fluid on both sides of the regenerator are calculated numerically and compared with the measurement results. The inability to provide a constant internal magnetic field during the heat transfer process with a permanent magnet is demonstrated. A reasonable agreement between simulations and experiments confirms the validity of the proposed model.

Abstract Sugarcane is a major crop cultivated globally and the residue left over after the crop harvest and extraction of juice is a good biomass source that can be used for the production of several useful chemicals. The sugarcane bagasse is an excellent substrate for the production of various biochemicals and enzymes through fermentation. Now major interest is focused on the utilization of these residue for biofuel production. The sugarcane crop residue is rich in cellulose and hemicellulose, hence it can be used for the production of bioethanol and other liquid transportation fuels. The present review gives a detailed account of the availability of sugarcane residue and various commercially important products that can be produced from this residue. It also provides recent developments in R&D on the bioconversion of sugarcane crop residue for value added products.

SummaryThe German government has adopted a law that requires sewage plants to go beyond the recovery of phosphorus from wastewater and to promote recycling. We argue that there is no physical global short‐ or mid‐term phosphorus scarcity. However, we also argue that there are legitimate reasons for policies such as those of Germany, including: precaution as a way to ensure future generations’ long‐term supply security, promotion of technologies for closed‐loop economics in a promising stage of technology development, and decrease in the current supply risk with a new resource pool.

Silicon heterojunction solar cells use crystalline silicon (c-Si) wafers as optical absorbers and employ bilayers of doped/intrinsic hydrogenated amorphous silicon (a-Si:H) to form passivating contacts. Recently, we demonstrated that such solar cells increase their operating voltages and thus their conversion efficiencies during light exposure. We found that this performance increase is due to improved passivation of the a-Si:H/c-Si interface and is induced by injected charge carriers (either by light soaking or forward-voltage biasing of the device). Here, we discuss this counterintuitive behavior and establish that: (i) the performance increase is observed in solar cells as well as modules; (ii) this phenomenon requires the presence of doped a-Si:H films, but is independent from whether light is incident from the a-Si:H(p) or the a-Si:H(n) side; (iii) UV and blue photons do not play a role in this effect; (iv) the performance increase can be observed under illumination intensities as low as 20 W m(-1) (0.02-sun) and appears to be almost identical in strength when under 1-sun (1000 W m(-1)); (v) the underlying physical mechanism likely differs from annealing-induced surface passivation.

This paper presents an integrated vehicle model to simulate simultaneously the driver, powertrains, chassis, body, road condition, vehicle dynamics and the Active Suspension (AS) system with/without an energy harvesting module. The developed model is used to investigate the ride comfort and influences of energy harvesting AS system on the total energy consumption of battery Electric Vehicles (EVs) relative to EVs with a passive suspension system. Preliminary simulation results show that compared to EVs with a passive suspension system, the ones with AS system improve ride comfort, up to 31% reduction of the vehicle body acceleration RMS value, with an expense of higher energy consumption. This expense can be reduced to about 2.8% when using an energy harvesting AS system. Simulation results also demonstrate that the available energy for recuperation during the AS system operation is significant in relation to the regenerative braking energy of the propulsion system, up to approx. 70% on bumpy road surfaces.

Abstract This paper provides a forecast of electricity consumption in Cyprus up to the year 2030, based on econometric analysis of energy use as a function of macroeconomic variables, prices and weather conditions. If past trends continue electricity use is expected to triple in the coming 20–25 years, with the residential and commercial sectors increasing their already high shares in total consumption. Besides this reference scenario it was attempted to assess the impact of climate change on electricity use. According to official projections, the average temperature in the Eastern Mediterranean is expected to rise by about 1 °C by the year 2030. Using our econometrically estimated model, we calculated that electricity consumption in Cyprus may be about 2.9% higher in 2030 than in the reference scenario. This might lead to a welfare loss of 15 million Euros in 2020 and 45 million Euros in 2030; for the entire period 2008–2030 the present value of costs may exceed 200 million Euros (all expressed in constant Euros of 2007). Moreover, we assessed the additional peak electricity load requirements in the future because of climate change: extra load may amount to 65–75 Megawatts (MW) in the year 2020 and 85–95 MW in 2030.

Abstract Gate-to-gate process energy consumption is an important metric for sustainability as it affects both costs and environmental impact. As only little process information is available in early phases of chemical process design, a detailed energy consumption calculation is substantially restrained. Therefore, a reliable estimation of energy consumption in early phases of process design is an important alternative. In this work, an index representing process energy consumption was evaluated and tested for 14 organic solvent case studies. By using simplified process models the indices were calculated and compared to literature values for gate-to-gate energy consumption. The predictability of the process energy consumption on the basis of this indicator, including possible modifications in its original definition, was evaluated with the Pearson's and Spearman's correlation coefficients. The results further validated the use of the EI (energy index) in its original form as a proxy indicator of the process energy consumption for decision making in early stages of process design. For assessing the production of new classes of chemicals the EI should be evaluated as shown in this paper in order to establish its practicability. In certain cases an adjustment of the indicator categories may be necessary.

Data Availability: The data that support the findings of this study are available from the corresponding author, C. Wen, upon reasonable request. ; Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisition systems has led to low diagnosis accuracy. Additionally, deep neural networks can encounter issues like gradient vanishing and insufficient feature learning during backpropagation when the model is too deep. This article introduces a novel approach that is based on dynamic weight loss functions to modulate unbalanced data and improve diagnostic accuracy by focusing on misclassification of a small sample number. The proposed approach employs a 1D-CNN model and an ensemble-learning-based convolution neural network (EL-CNN) to enhance diversity of models and complementarity of feature learning. The EL-CNN model addresses the problem of local features being overlooked and provides more accurate results. The effectiveness of this proposed approach is well demonstrated through experimental cases on real wind turbine pitch system fault data. Two different networks using three different loss functions and three state-of-the-art fault diagnosis models are tested, demonstrating the EL-CNN model’s superiority. ; This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 820776 (INTEGRADDE), the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, the Alexander von Humboldt Foundation of Germany, the BRIEF Award of Brunel University London, the National Natural Science Foundation of China under Grant 61973209, the Natural Science Foundation of Shanghai of China under Grant 20ZR1421200, and the Capacity Building Project of Shanghai Local Colleges and Universities of China under Grant 22010501100.