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This paper focuses on the following two key research questions in the context of the change in allocation rules in the move from Phase I/II (2005–2012) to Phase III (2013–2020) of the European Emission Trading Scheme (EU ETS): First, how do allocations compare with actual installation-verified emissions in Phase III? For that purpose we analyse changes in sector-country allocations and verified emissions between Phase II and Phase III. The analysis is based on a selection of 2150 installations present in all phases of the EU ETS, taken from the European Union Transaction Log (EUTL) The results show that over-allocation has been considerably reduced in Phase III. Overall, allocation for the selected sectors decreased by 20% in 2013 compared to 2008 but varying across installations. Second, we investigate, whether the introduction of benchmarks in Phase III may have triggered carbon-reducing measures for industrial processes. For that purpose, we analyse for four product groups (cement clinker, pig iron, ammonia and nitric acid) the specific emissions (per tonne of product). Care was taken to define a data set with a similar delimitation of emission and production data. The findings were cross-checked through selected expert interviews. Our findings indicate that there is no evidence so far for improving specific emissions, though the strong improvement for nitric acid, as well as some improvement linked to ammonia occurring before the start of Phase III may have been supported by the introduction of Phase III.

This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R2), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.

Pool boiling is an effective heat transfer process in a wide range of applications related to energy conversion, including power generation, solar collectors, cooling systems, refrigeration and air conditioning. By considering the broad range of applications, any improvement in higher heat-removal yield can ameliorate the ultimate heat usage and delay or even avoid the occurrence of system failures, thus leading to remarkable economic, environmental and energy efficiency outcomes. A century of research on ameliorating critical heat flux (CHF) has focused on altering the boiling surface characteristics, such as its nucleation site density, wettability, wickability and heat transfer area, by many innovative techniques. Due to the remarkable interest of using nanoparticle deposition on boiling surfaces, this review is targeted towards investigating whether or not metal oxide nanoparticles can modify surface characteristics to enhance the CHF. The influence of nanoparticle material, thermo-physical properties, concentration, shape, and size are categorized, and the inconsistency or contradictions of the existing research results are recognized. In the following, nanoparticle deposition methods are presented to provide a worthwhile alternative to deposition rather than nanofluid boiling. Furthermore, possible mechanisms and models are identified to explain the amelioration results. Finally, the present status of nanoparticle deposition for CHF amelioration, along with their future challenges, amelioration potentials, limitations, and their possible industrial implementation, is discussed.

This paper presents a method for the performance analysis of high-speed electric machines supplied with pulse-width modulated voltage source inverters by utilizing a fast analytical model. By applying a strict mathematical procedure, effective expressions for the calculation of rotor eddy current losses and electromagnetic torque are derived. Results obtained by the approach suggested in this study are verified by the finite element model, and it is shown that the proposed method is superior in comparison to the finite element method in terms of computation time. The proposed method enables fast parameter variation analysis, which is demonstrated by changing the inverter switching frequency and electric conductivity of the rotor and analyzing the effects of these changes on rotor eddy current losses. The presented work separately models effects of the permanent magnet and pulse-width modulated stator currents, making it suitable for the analysis of both high-speed permanent magnet machines and high-speed induction machines.

A comprehensive monitoring program is an integral part of the safe operation of geological CO2 storage projects. Noble gases can be used as geochemical tracers to detect a CO2 anomaly and identify its origin, since they display unique signatures in the injected CO2 and naturally occurring geological fluids and gases of the storage site complex. In this study, we assess and demonstrate the suitability of noble gases in source identification of CO2 anomalies even when natural variability and analytical uncertainties are considered. Explicitly, injected CO2 becomes distinguishable from shallow fluids (e.g., subsea gas seeps) due to its inheritance of the radiogenic signature (e.g., high He) of deep crustal fluids by equilibration with the formation water. This equilibration also results in the CO2 inheriting a distinct Xe concentration and Xe/noble gas elemental ratios, which enable the CO2 to be differentiated from deep crustal hydrocarbon gases that may be in the vicinity of a storage reservoir. However, the derivation has uncertainties that may make the latter distinction less reliable. These uncertainties would be best and most economically addressed by coinjection of Xe with a distinct isotope ratio into the CO2 stream. However, such a tracer addition would add significant cost to monitoring programs of currently operating storage projects by up to 70% (i.e., from 1 $US/t to 1.7 $US/t).

Large-scale implementation of (plant) microbial fuel cells is greatly limited by high electrode costs. In this work, the potential of exploiting electrochemically active self-assembled biofilms in fabricating three-dimensional bioelectrodes for (plant) microbial fuel cells with minimum use of electrode materials was studied. Three-dimensional robust bioanodes were successfully developed with inexpensive polyurethane foams (PU) and activated carbon (AC). The PU/AC electrode bases were fabricated via a water-based sorption of AC particles on the surface of the PU cubes. The electrical current was enhanced by growth of bacteria on the PU/AC bioanode while sole current collectors produced minor current. Growth and electrochemical activity of the biofilm were shown with SEM imaging and DNA sequencing of the microbial community. The electric conductivity of the PU/AC electrode enhanced over time during bioanode development. The maximum current and power density of an acetate fed MFC reached 3 mA·m−2 projected surface area of anode compartment and 22 mW·m−3 anode compartment. The field test of the Plant-MFC reached a maximum performance of 0.9 mW·m−2 plant growth area (PGA) at a current density of 5.6 mA·m−2 PGA. A paddy field test showed that the PU/AC electrode was suitable as an anode material in combination with a graphite felt cathode. Finally, this study offers insights on the role of electrochemically active biofilms as natural enhancers of the conductivity of electrodes and as transformers of inert low-cost electrode materials into living electron acceptors.

The Loess Plateau, with a large area of marginal land, holds the potential to produce 62–106 Tg per year of switchgrass biomass; however, the economic feasibility of producing bioenergy in the region is unclear. The farm-gate feedstock production (FGFP) cost of switchgrass was calculated in a spatially explicit way by taking the geographic variation in crop yield, soil properties, land quality, and input costs into consideration in order to evaluate the economic performance of bioenergy production. Cost–supply curves were constructed to explore the energy supply potential of switchgrass feedstock. The calculations were conducted using ArcGIS in a 1 km grid and all the evaluations were conducted under different agricultural management practice (AMP) scenarios in parallel. The FGFP costs showed significant spatial variation ranging from 95 to 7373 CNY (Chinese Yuan) per tonne−1 and that the most economically desirable areas are scattered in the south and southeast region. The weighted average FGFP costs are 710, 1125, and 1596 CNY per tonne−1 for small bale (SB), large bale (LB), and chipping (CP) harvest methods, respectively. The projected energy supply potential is 1927 PJ (Petajoules) per year−1, of which 30–93% can be supplied below the market prices of different fossil fuels according to feedstock formats. Compared to current biomass residual pricing, 50–66 Tg (Teragrams) switchgrass feedstock is competitive. The results demonstrated that the Loess Plateau holds the potential to produce bioenergy that is economically feasible. This study provides a methodological framework for spatially explicit evaluation of the economic performance of perennial energy crops. Detailed information obtained from this study can be used to select the optimal locations and AMPs to produce feedstock production at minimum cost.