ALBERT

Description: The paper addresses the sustainability of the European Union’s food consumption through a water footprint assessment of selected vulnerable agricultural imports from a two-pronged perspective: (1) the degree of the EU’s dependence on global green water resources embedded in the apparent consumption of selected water-intensive agricultural products and (2) the degree of commitment of countries of origin to sustainability policies. The study argues that the vulnerability of the EU’s agricultural imports to water risks can be estimated based on the amount of green water consumed in producing crops in the countries of origin. The results show that the EU’s consumption of agricultural goods is highly dependent on virtual water imports for all six selected vulnerable agricultural products, from the lowest footprint for bananas (5 mil. km3) to the largest for coffee (69 mil. km3). The analysis also points to a greater concern for quality issues in the countries of origin (56.53%) relative to management (26.52%) and availability issues (16.85%), but the latter are to arise in importance for sustainable production in the years to come. Our conclusions contribute to building up a responsible commitment towards (1) development of environmental policies and the design of practical measures by providing quantitative information that makes problems more clearly defined and tangible, and (2) assessing the outcome of policies and practical measures by understanding their effects on the sustainability of food consumption.

Description: Residents of nursing homes have been significantly affected by COVID-19 in Spain. The factors that have contributed to the vulnerability of this population are very diverse. In this study, physical agents, chemical pollutants, population density and different capacities of residences were analysed to understand their influence on the number of elderly people who have died in geriatric centres in different autonomous communities (AACCs) of Spain. A statistical analysis was carried out on the variables observed. The results show that many residences with a larger number of deaths were private, with some exceptions. Physical agents and pollutants were found to be determining factors, especially for the communities of Extremadura and Castilla–La Mancha, although the large number of factors involved makes this study complicated. The compromise between air quality and energy efficiency is of great importance, especially when human health is at stake.

Description: This paper investigates information leakage and financing simultaneously in a supply chain (SC) consisting of one capital-constrained supplier and two retailers with private demand-forecast signals. The supplier invests in corporate social responsibility (CSR) events and displays supply uncertainty. The supplier decides whether to leak information (L) or not (N). Additionally, the supplier has two financing strategies: bank credit financing (B) and trade credit financing (T). Thus, by combining the supplier’s information leakage and financing decisions, we formulated four possible strategies (i.e., NB, NT, LB, LT) and built a game analysis model to address the interaction of information leakage and financing decisions. We first provide the SC members’ optimal operational decisions (including the order quantity, the wholesale price and CSR effort level) under four strategies. Subsequently, we compare the profits of the suppliers and retailers under four strategies by combining analytical and numerical analysis. Several interesting results were found: (1) the supplier’s optimal wholesale price, CSR effort level, and profit under information leakage were higher than those under no information leakage; (2) the supplier’s financing decisions are dependent on the loan interest rate as low supply uncertainty and low supply correlation motivate the supplier to prefer choosing trade credit financing; and (3) finally, several interesting insights in managing SCs are provided.

Description: Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.

Description: Buildings of the future are called to meet increasingly high-performance requirements and to ensure adequate environmental sustainability of the production and construction chain. This issue has stimulated a keen interest in the use of natural materials in construction. Among these, raw earth has proved to be particularly interesting for its intrinsic availability, sustainability, and recyclability. In Europe, the spread of raw earth building technologies has often been hindered by the lack of specific legislation regulating its use for load-bearing structures, even if in many countries, it can be noticed a widespread and well-established constructive tradition. Some transoceanic research experiences attest that unfired earth can be used, together with different types of reinforcements, to create seismic-resistant buildings. After presenting a review of the main raw earth reinforced technologies, the present study focuses on a novel reinforced and modular rammed earth construction made with natural or recycled materials, developing a technology with low energy consumption and low environmental impact, specifically designed for areas with high seismic risk. In particular, the work presents the results of a prototyping procedure aiming at developing a new seismic-resistant construction system that combines rammed earth with timber reinforcement elements and nylon/polyester ropes. These elements have a dual function: (1) they are fundamental components of the construction process (as they integrate the formwork system), and (2) they act as seismic-resistant devices once the structure is completed. In line with the performance-based approach required by the construction sector, the study aims at defining a controlled and standardised supply chain for rammed earth construction.

Description: This study investigated seasonal variation, spatial distribution, sources, composition and potential ecological risks of 16 polycyclic aromatic hydrocarbons (PAHs) in Red Sea coastal water. Surface seawater samples were collected at fourteen different locations. The sum of 16 PAHs concentrations in Red Sea showed clear variation between seasons, ranged from 1.08–6.10, 0.79–50.86, 1.37–54.47 and 0.21–7.18 μg/L in summer, autumn, winter, and spring respectively. PAHs levels in Red Sea coastal waters were relatively high at certain sites during autumn and winter seasons. They could be classified as highly polluted by PAHs contaminants (〉10 μg/L). According to the diagnostic ratios measured, the majority of PAHs in this study originated from pyrogenic sources, while minor amounts originated from petrogenic sources. Four rings PAHs accounted for more than 64% of the total PAHs studied. PAHs pose a high ecological risk along the period of study, according to the ecological risk assessment by Risk quotients (RQNCs and RQMPCs).

Description: In recent years, green finance has emerged as a commonly used strategy for dealing with environmental problems. However, it still remains to be seen whether green finance deals effectively with current global environmental problems. More recently, proposals regarding greening monetary policy have emerged. The goal of this paper is to provide a conceptual framework that helps to distinguish between different forms of green finance and monetary policy. We systematically analyse forms, tools and measures of green finance and monetary policy against different theoretical backgrounds. In so doing, we fill a research gap by providing an appropriate classification that is intended to facilitate future academic research. We provide different categories and distinguish, on an abstract level, between neoliberal, reformist and progressive forms of green finance. Furthermore, we provide sub-categories on a more concrete level of abstraction. With this, we focus on both financial market regulation and monetary policy strategies. Against the background of our categorisation, the different focuses on green finance and green monetary policy and the (often implicitly) underlying theoretical assumptions become transparent. The classification has significant implications for the evaluation of different perspectives and is, therefore, important for academic debate. The classification also potentially represents a basis for policy related discussions. We conclude that neoliberal forms of green finance and monetary policy that rely on the assumption of the effectiveness of markets for contributing to sustainability tend to neglect or abstract from potentially adverse distributional effects. Reformist forms of green finance and monetary policy are more skeptical of the effectiveness of market processes and, therefore, consider a greater role for government policies. In addition, reformist approaches are more concerned about the potentially adverse distributional effects of environmental policies. Finally, progressive green finance and monetary policy adopts a more global perspective on environmental issues and links the discussion intrinsically with questions of global inequality and socio-ecological transformation. Moreover, progressive approaches are skeptical of global capitalism at a systemic level and therefore demand global rules and financial and monetary regimes that allow for solutions of environmental problems based on global solidarity and a democratic economic governance.

Description: The precipitation phase (PP) affects the hydrologic cycle which in turn affects the climate system. A lower ratio of snow to rain due to climate change affects timing and duration of the stream flow. Thus, more knowledge about the PP occurrence and drivers is necessary and especially important in cities dependent on water coming from glaciers, such as Quito, the capital of Ecuador (2.5 million inhabitants), depending in part on the Antisana glacier. The logistic models (LM) of PP rely only on air temperature and relative humidity to predict PP. However, the processes related to PP are far more complex. The aims of this study were threefold: (i) to compare the performance of random forest (RF) and artificial neural networks (ANN) to derive PP in relation to LM; (ii) to identify the main drivers of PP occurrence using RF; and (iii) to develop LM using meteorological drivers derived from RF. The results show that RF and ANN outperformed LM in predicting PP in 8 out of 10 metrics. RF indicated that temperature, dew point temperature, and specific humidity are more important than wind or radiation for PP occurrence. With these predictors, parsimonious and efficient models were developed showing that data mining may help in understanding complex processes and complements expert knowledge.

Description: Typically, the main control on alternating current (AC) power systems is performed by the scheduling of rotary machines of synchronous generators and static machines of on-load tap changer (OLTC) transformers and volt-ampere reactive (VAR) sources. Large machines of synchronous generators can be managed by utilizing terminal voltage control when synchronized in parallel to the power system. These machines are typically terminal voltage regulated. In addition, substation on-load tap changer (OLTC) transformers improve system voltage management by controlling variable turn ratios that are adjusted in different levels known as taps along either the primary or secondary winding. Moreover, volt-ampere reactive (VAR) sources of static VAR compensators (SVCs), which are automated impedance devices connected to the AC power network, are designed for voltage regulation and system stabilization. In this paper, scheduling of these machines is coordinated for optimal power system operation (OPSO) using a recent algorithm of social network search optimizer (SNSO). The OPSO is performed by achieving many optimization targets of cost of fuel, power losses, and polluting emissions. The SNS is a recent optimizer that is inspired from users in social networks throughout the different moods of users such as imitation, conversation, disputation, and innovation mood. The SNSO is developed for handling the OPSO problem and applied on an IEEE standardized 57-bus power system and real Egyptian power system of the West Delta area. The developed SNSO is used in various assessments and quantitative analyses with various contemporary techniques. The simulated findings prove the developed SNSO’s solution accuracy and resilience when compared to other relevant techniques in the literature.

Description: In standard district cooling (DC) plants, central chillers produce cold energy for space cooling throughout the district network. In recent times, the integration of the vapor compression system, which includes the functionalities of vapor compression chillers (VCC), and thermal energy storage (TES) tanks in the DC setup, has gained more implementation across the globe. This is due to the possibility of load shifting by using the VCC to produce chilled water for charging the TES tanks during off peak periods. Since the environmental implications of various energy intensive systems are largely determined by the amount of material and energy consumed throughout their life cycle, it is critical to conduct a sustainability assessment of these systems in terms of environmental contributions, and suggest design options to reduce these impacts. A cradle to grave life cycle assessment (LCA) model is created in response to these issues and in order to meet the project’s objectives. The life cycle impact assessment (LCIA) results of the analysis reveal that the carbon footprint per 1 RTh of the produced chilled water is estimated at 0.72 kg CO2 eq/RTh. The operation phase of the system’s life cycle accounted for the most impact, about 98%, with other life cycle phases having negligible contributions. In substantiating the study’s investigation, the environmental performance based on several design options were discussed and compared to the case study. Among the several scenarios considered, incorporating the Sweden mix technology provided the case study with the most significant environmental savings, of about 94%.