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The transformation of the energy sector, based on the development of low-carbon technologies, is essential to achieve climate neutrality. The Life Cycle Assessment (LCA) is a powerful methodology for assessing the environmental impact of energy technologies, which proves to be a useful tool for policy makers. The paper is a review of the main LCA studies of power generation systems performed over the past ten years aiming at comparing the energy technologies to identify those with the lowest impact on the environment, evaluated in terms of gCO2eq/kWh emissions. Screening criteria were established to include only studies of the highest qualitative significance. The authors decided to assign greater weight to emission values reported in more recent studies. For nuclear and renewable energy technologies, most of the emissions are related to the pre-operational phases. Notably, both nuclear and wind technologies, along with other renewable sources throughout their entire life cycle, exhibit significantly lower and less variable emissions compared with conventional gas- and coal-fired technologies.

The real environment impacts the fuel and energy consumption of any vehicle: technology, physical and social phenomena, traffic, drivers’ behaviour, and so on; many of them are difficult to quantify. The authors’ methodology was used to test the real impact of vehicles in “standard” urban conditions, and many generations of hybrid powertrains are compared. One of the latest performance indexes is the percentage of time the vehicle runs with zero emissions (ZEV). For example, the hybrid vehicle tested ran up to 80% with no emissions and fuel consumption below 3 L per 100 km. A few energy performance indicators were compared between five vehicles: one battery electric vehicle (BEV), two hybrid gasoline–electric vehicles (HEVs), and two traditional vehicles (one diesel and one gasoline). Their potential to use only renewable energy is unrivalled, but today’s vehicles’ performances favour hybrid power trains. This paper summarises the most sustainable powertrain for urban use by comparing experimental data from on-road testing. It also evaluates the benefits of reducing emissions by forecasting the Italian car fleet of 2025 and three use cases of the evolution of car fleets, with a focus on Rome.

The idea of a circular economy (CE) has gained ground over the past ten years as a means of addressing sustainable development and getting around the limitations of the current and linear dominant production and consumption patterns. The primary goal of a CE is to encourage the adoption of closing-the-loop production methods to improve resource use efficiency, modify chemical processes, and increase product and material lifespan. According to the 2030 Agenda for Sustainable Development, which focuses on 17 Sustainable Development Goals, 14 of which call for the appropriate application of green chemistry (GC) concepts and patterns, the role that chemistry may play in the shift toward more sustainable models is critical. By serving as the foundation for novel products made from renewable feedstocks and designed to be reused, recycled, or recovered with the associated minimum energy requirements, green and sustainable chemistry could be the key to unlocking the economic potential of the CE toward new product design and ultimately solving waste management problems. The aim of this perspective paper, while using a variety of literature sources, is to essentially capture the main issues associated with the CE and GC paradigms and how these two approaches can merge toward sustainable business models and the production of new materials. This integration focuses on reducing waste, conserving resources, and minimizing negative environmental impacts, while also considering economic viability. However, the obstacles to achieving implementation of the CE and GC principles are investment, environmental education, and legislation. To advance toward the circular economy and green chemistry, international agreements should be reconsidered to provide an appropriate framework, including the creation of incentives for businesses and individuals to adopt circular practices, the establishment of education programs to promote the benefits of circular practices, and the development of regulations to support the transition to sustainable production and consumption patterns.

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy-consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens’ engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854.

In questo tempo di crisi, scopriamo la necessità di valori antropologici per guidare la navigazione della nostra specie su questo pianeta. Dal 2000 EcoOne, sezione ecologica dell’Associazione Internazionale New Humanity-NGO (status consultivo generale dell’ECOSOC delle Nazioni Unite, partner ufficiale dell’UNESCO, accreditata presso l’UNEP) ha organizzato numerose conferenze internazionali su temi ambientali. L’incontro del 2020 “Nuove vie verso l’ecologia integrale” ha discusso le sfide economiche, ambientali e sociali della transizione ecologica: alcuni paper selezionati della conferenza sono pubblicati in questo rapporto. In this time of crisis, we discover the need for anthropological values to guide the navigation of our species on this planet. Since 2000 EcoOne, the ecological section of the International New Humanity Association-NGO (general consultative status of the United Nations ECOSOC, official partner of UNESCO, accredited to UNEP) has organized numerous international conferences on environmental issues. The 2020 meeting “New Pathways to Integral Ecology” discussed the economic, environmental and social challenges of the ecological transition: some selected papers from the conference are published in this report.

The sustainability of industrial production, especially for highly water-demanding processes, is strictly related to water resource availability and to the dynamic interactions between natural and anthropogenic requirements over the spatial and temporal scales. The increase in industrial water demand raises the need to assess the related environmental sustainability, facing the occurrence of global and local water stress issues. The identification of reliable methodologies, based on simple indices and able to consider the impact on local water basins, may play a basilar role in water sustainability diagnosis and decision-making processes for water management and land use planning. The present work focalized on the definition of a methodology based on the calculation of indicators and indices in the view of providing a synthetic, simple, and site-specific assessment tool for industrial water cycle sustainability. The methodology was built starting from geo-referenced data on water availability and sectorial uses derived for Italian sub-basins. According to the data monthly time scale, the proposed indices allowed for an industrial water-related impacts assessment, able to take into account the seasonal variability of local resources. Three industrial factories, located in northern (SB1, SB2) and central (SB3) Italian sub-basins, were selected as case studies (CS1, CS2, CS3) to validate the methodology. The companies were directly involved and asked to provide some input data. The methodology is based on the calculation of three synthetic indexes: the Withdrawal and Consumption water Stress Index (WCSI) allowed for deriving a synthetic water stress level assessment at the sub-basin scale, also considering the spatial and temporal variations; the industrial water use sustainability assessment was achieved by calculating the Overall Factory-to-Basin Impact (OFBI) and the Internal Water Reuse (IWR) indices, which allowed a preliminary evaluation of the factories' impacts on the sub-basin water status, considering the related water uses and the overall pressures on the reference territorial context. The WCSI values highlighted significant differences between the northern sub-basins, characterised by limited water stress (WCSI

The need for Artificial Intelligence (AI) and Machine Learning (ML) technologies is increasingly being leveraged for optimizing Data centers’ (DCs’) operations as the volume of operations management data increase tremendously. These strategies can assist operators in better understanding their DC operations and making informed decisions up front to preserve service reliability and availability. Aiming at creating models that optimize energy efficiency, identify inefficient resource utilization and scheduling policies, and predict outages. Apart from model hyperparameter tuning, feature selection is a crucial step to identify relevant features with the objective of providing insight into the data, improving performance, and reducing computational expenses. Although several feature selection methods have been discussed in various domains, none have been discussed in the context of the data center. This paper introduces SHapley Additive exPlanation (SHAP), a class of additive feature attribution values-based feature selection that is rarely discussed in literature. We compared the effectiveness of SHAP method with several widely used methods. We used a real DC dataset obtained from the ENEA CRESCO6 cluster with 2,0832 cores to evaluate the methods. To demonstrate the comparison of the methods, we picked the top 10 most important features from each method, the predictions were retrained, and their performance was evaluated using MAE, RMSE, and MPAE. The results show that the SHAP-assisted feature selection performed best and align with human intuition.