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Energy efficiency is an increasingly important dimension of household appliances, which is why they are labeled to indicate their energy consumption. In 2020, the European Union countries changed the labeling system from the previous system: ranging from A+++ to D, to the new system: ranging from A to G, assuming it would be more transparent for the consumer. The aim of the study was to find out the extent to which consumers are aware of the new labeling system, and the impact that the new labels have (compared to the previous ones) on the perception of household appliances and consumer decision-making. For this purpose, the survey was conducted on a nationwide representative Polish sample (n = 1054). The research was partly experimental, where the same appliances were presented with new and previous energy labels. The results showed that currently most people do not identify the new energy classes. Furthermore, products with the new labels are perceived as being less energy efficient in comparison with products with the previous labels, which shows that there is some confusion among consumers in terms of the new energy efficiency labeling system.

The 2030 zero-net emission target in the E.U. demands a significant improvement in the energy performance of the building stock. This study analyses the adoption of connected thermostats and Home energy-management system solutions (HEMS) as an effective means to tackle the residential energy footprint. It reviews the main features of HEMS systems in terms of technology, cross-study performances, and the obstacles to widespread adoption; the study adopts the case-study methodology to examine the impact on the Italian real estate stock at a regional level. A matrix of adoption scenarios assesses the potential benefits of global residential energy savings, weighted by local climatic variations, dimension, number of single dwellings, and average primary energy reduction per household. Results demonstrate that all adoption scenarios dramatically reduce residential energy consumption, outperforming the E.U. targets for Italy by 2030.

With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.

Nuclear power can replace fossil fuels and will have a decisive impact on the change in the approach to conventional energy. However, nuclear (or radioactive) wastes are produced by the operation of the nuclear reactors should be safely and properly disposed of. This paper assesses the uranium resources and the global state of nuclear power plants and determines the energy mixes in different countries using the most nuclear energy. Furthermore, this paper analysed the nuclear waste management and disposal and the depletion of abiotic resources, and the primary energy sources of a basic production process using electricity mix and nuclear electricity for a basic production (PET bottle manufacturing) process. The life cycle assessment was completed by applying the GaBi 8.0 (version 10.6) software and the CML method. In this study, we limit our discussion to high-level nuclear waste (HLW) and spent nuclear fuel (SNF) waste. We do not consider waste generated from uranium mining and milling, which is usually disposed of in near-surface impoundments close to the mine or the mill. The investigation of waste management methods is limited to European countries. This research work is relevant because determining abiotic resources is important in a life cycle assessment and current literature available on LCA analysis for nuclear powers remains under-developed. These results can guide and compare manufacturing processes involving a nuclear electricity and electricity grid mix input. The results of this research can be used to develop production processes using nuclear energy with lower abiotic depletion impacts. This research work facilitates the industry in making predictions for a production-scale plant using an LCA of production processes with nuclear energy consumption.

This article discusses the problems of exhaust gas emissions in the context of the possibility of their reduction through the use of fuels with hydrogen as an additive or hydrotreatment. These fuels, thanks to their properties, may be a suitable response to more and more demanding restrictions on exhaust emissions. The use of such fuels in reactivity controlled dual fuel engines (RCCI) is currently the most effective way of using them in internal combustion (IC) engines. Low-temperature combustion in this type of engine allows the use of all modern fuels intended for combustion engines with high thermal efficiency. Thermal efficiency higher than in classic engines allows for additional reduction of CO2 emissions. In this work, the research on this subject was compiled, and conclusions were drawn as to further possibilities of popularizing the use of these fuels in a wide spectrum of applications and the prospect of using them on a mass scale.

Biomass is one of the most important sources of renewable energy in the energy industry. It is assumed that by 2050 the global energy deposit could be covered in 33–50% of biomass combustion. As with conventional fuels, the combustion of biomass produces combustion by-products, such as fly ash. Therefore, along with the growing interest in the use of biomass as a source of energy, the production of ash as a combustion by-product increases every year. It is estimated that approximately 476 million tons of ashes per year can be produced from biomass combustion. For example, the calorific value of dry wood mass tends to be between 18.5 MJ × kg−1 and 19.5 MJ × kg−1, while the ash content resulting from thermal treatment of wood is from 0.4 to 3.9% of dry fuel mass. However, biomass ash is a waste that is particularly difficult to characterize due to the large variability of the chemical composition depending on the biomass and combustion technology. In addition, this waste is, on the one hand, a valuable fertilizer component, as it contains significant amounts of nutrients, e.g., calcium (Ca), potassium (K) and microelements, but on the other hand, it may contain toxic compounds harmful to the environment, including heavy metals and substances formed as a result of combustion, such as polycyclic aromatic hydrocarbons (PAHs) or volatile organic compounds (VOCs). PAHs and VOCs are formed mainly in the processes of incomplete combustion of coal and wood in low-power boilers, with unstable operating conditions. However, it is important to remember that before the fly ash is used in various industries (e.g., zeolite synthesis, recovery of rare earth metals or plastic production) as an additive to building materials or fertilizers for cultivation, a number of analyses are to be conducted so that the by-products of combustion could be used to allow the by-product of combustion to be used. It is important to conduct tests for the content of heavy metals, chlorides, sulphates, microelements and macroelements, grain and phase composition and organic compounds. If such ash is characterized by low pollution levels, it should be used in agriculture and reclamation of degraded land and not directed to landfills where it loses its valuable properties. The purpose of this review is to present the properties of ashes generated as a result of biomass combustion in Poland and the world, to discuss factors influencing changes in its composition and to present the possibilities of their reuse in the environment and in various branches of industry.

Lithium-ion batteries are currently one of the most important mobile energy storage units for portable electronics such as laptops, tablets, smartphones, etc. Their widespread application leads to the generation of large amounts of waste, so their recycling plays an important role in environmental policy. In this work, the process of leaching with sulfuric acid for the recovery of metals from spent Li-ion batteries in the presence of glutaric acid and hydrogen peroxide as reducing agents is presented. Experimental results indicate that glutaric-acid application improves the leaching performance compared to the use of just hydrogen peroxide under the same conditions. Obtained samples of leaching residues after mixed inorganic-organic leaching were characterized with Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy, and X-ray diffraction.

The analyzed research issue provides a model for Carbon Footprint estimation at an early design stage. In the context of climate neutrality, it is important to introduce regenerative design practices in the architect’s design process, especially in early design phases when the possibility of modifying the design is usually high. The research method was based on separate consecutive research works–partial tasks: Developing regenerative design guidelines for simulation purposes and for parametric modeling; generating a training set and a testing set of building designs with calculated total Carbon Footprint; using the pre-generated set to train a Machine Learning Model; applying the Machine Learning Model to predict optimal building features; prototyping an application for a quick estimation of the Total Carbon Footprint in the case of other projects in early design phases; updating the prototyped application with additional features; urban layout analysis; preparing a new approach based on Convolutional Neural Networks and training the new algorithm; and developing the final version of the application that can predict the Total Carbon Footprint of a building design based on basic building features and on the urban layout. The results of multi-criteria analyses showed relationships between the parameters of buildings and the possibility of introducing Carbon Footprint estimation and implementing building optimization at the initial design stage.