• Energy Research
• Restricted close
• University of Bergamo close

This book investigates sustainability, CSR, climate change adaptation, the relevance of ESG scores and their impact on firm value and growth. The first part of the book analyses the topics from a conceptual angle. The authors discuss how the concepts of self-consciousness and awareness drive the shift of the traditional concept of corporate mission towards more sustainable business models. The authors propose an in-depth analysis of the main challenges posed by climate change and of the initial policy-makers’ responses and provide their view on the central role of ESG scores and circular economy for growth and development. The authors conclude with an analysis of the main literature on the measurement of the relation between ESG scores and firms’ performance and cost of equity (CoE). The second part of the book contains comparative empirical evidence, supporting these theories across specific industries, and will be of interest to academics, researchers, and students of sustainability and impact finance.

In this paper the Authors give indications for the design of a grid tied Voltage Source Inverters (VSI) aimed to optimal use in energy conversion from wind source. In particular, the design of the inverter is more focused on the control system planning, with a particular glance on the grid synchronization problem. After the exposition of general issues concerning renewable energies and associated topics, the case of a grid tied inverter is considered and a methodology of design is sketched starting from the consideration of the DC side and also considering the power device losses, the protection issue against over voltages and over-currents, snubber circuits, heat sinks and, finally the control system for the grid synchronization. Methods and modern technologies are considered and overviewed with a particular glance to the regulation of the generated and converted energy.

Abstract Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, there is no simple and reliable tool that simultaneously solves the energy and environmental balance of buildings. In this work, the authors address this challenge by proposing the application of an Artificial Neural Network. Due to the high reliability of learning algorithms in the resolution of complex and non-linear problems, it was possible to simultaneously solve two different but strongly dependent aspects after a deep training phase. In previous researches, the authors applied several topologies of neural networks, which were trained on a large and representative database and developed for the Italian building stock. The database, characterised by several building models simulated in different climatic conditions, collects 29 inputs (13 energy data and 16 environmental data) and provides 7 outputs, 1 for heating energy demand and 6 of the most used indicators in life cycle assessment of buildings. A statistical analysis of the results confirmed that the proposed method is appropriate to achieve the goal of the study. The best artificial neural network for each output presented low Root Mean Square Error, Mean Absolute Error lower than 5%, and determination coefficient close to 1. The excellent results confirmed that this methodology can be extended in any context and to any condition (other countries and building stocks). Furthermore, the implementation of this solution algorithm in a software program can enable the development of a suitable decision support tool, which is simple, reliable, and easy to use even for a non-expert user. The possibility to use an instrument to predict a building's performance in its design and planning phase, represent an important result to support decision-making processes toward more sustainable choices.

Renewable sources are increasing their presence in power systems to achieve the goal of decarbonizing the generation of electricity. However, the variability of these resources introduces high uncertainty in the operation of a power system and, hence, fast generating and storage units must be incorporated in the system. To obtain economically and technically efficient solutions in the capacity expansion problem, an adequate representation of the variability of the renewable resource is crucial, but this may increase the size of the problem up to computational intractability. The most suitable representation is often selected through clustering techniques. The clustering algorithms proposed in the literature suffer from a significant trade-off between their ability to capture the inter-temporal correlation of the clustered elements and the representativeness of the clusters when only very few are needed. This paper proposes an innovative clustering technique that overcomes this trade-off. A case study based on the European power system is solved. The outcomes given by the new clustering algorithm and other well-known techniques are investigated through in-sample and out-of-sample analyses applied to multiple cases that differ in to the number of clusters and the minimum renewable production.

La0.6Sr0.4Fe0.95Pd0.05O3−δ shows a reversible crystal structure and exhibits improved performance and redox stability when used as electrode in SOFCs.

Abstract Structure stability and electrical conductivity of La 1-x Sr x Fe 0.8 Cu 0.2 O 3-δ with x = 0.2, 0.4 were investigated both in air and in hydrogen to explore their potential applications as symmetric electrodes for solid oxide fuel cells. La 0.8 Sr 0.2 Ga 0.8 Mg 0.2 O 3-δ (LSGM) electrolyte supported symmetric fuel cells were fabricated and tested in air/hydrogen ambient in 650–800 °C temperature range achieving a maximum power density of 162 mW cm −2 . To improve the cell performance, a composite electrode (70 wt% La 1-x Sr x Fe 0.8 Cu 0.2 O 3-δ −30 wt% Gd 0.2 Ce 0.8 O 3-δ ) was successfully evaluated both at the anode and the cathode sides decreasing the cell polarization resistance to 0.454 Ω cm 2 at 800 °C and reaching a single cell maximum power density of 294 mW cm −2 .

Abstract The achievement of climate change targets needs an expansion of renewable energies and the restructuring of the whole energy system. Investments in renewable and energy efficiency projects require huge amounts of funds. A model based on the direct participation of energy users in energy production and in the fundraising process is a sustainable way in which these climate change targets can be attained and guaranteed. The cooperative model and the crowdfunding model are two ways of engaging citizens/end-users in fostering this energy transition and supplying the missing funds needed for this transition. The study focuses on the role played by citizens in the funding of renewable and energy efficiency projects, by wondering which factors can influence a more effective participation of citizens in the fundraising process. By analysing the ‘Citizenergy’ platform, the first European platform that provides information on sustainable energy projects, the study employs a fuzzy set analysis to identify different combinations of factors that can lead to citizens’ positive engagement in fundraising for renewable and energy efficiency projects seeking funds. The research suggests the relevance of a combined use of cooperative and crowdfunding models to foster the transition to renewable energy and achieve the ambitious climate change goals. Results also highlight the critical role played by social networks in projects promotion and by the use of the English language in projects description. On the other hand, findings show that investment size does not condition the level of citizen engagement in sustainable energy projects fundraising process.

Lecce stone is a natural material extracted from quarries in the Salento area (Lecce, Apulia, Italy), and used for building and ornamental elements. Its nature as a non-renewable resource justifies its considerable exploitation; but extraction and processing activities produce waste that creates a strong environmental impact. The Puglia region is also rich in olive trees. Usually, the pruning waste is used as firewood or for furniture. Moreover, the amount of olive wood waste available has increased with the spread of Xylella Fastidiosa. Starting from the problem of waste management and the depletion of non-renewable resources, the goal of the research was to find sustainable alternatives to give a new life to waste produced from local raw materials and creating exclusive handicraft products. Composite filaments with a polymer matrix (based on polylactic acid-PLA) were produced using different waste materials as additives and then used to create “made in Apulia” products, through the use of Fused Filament Fabrication (FFF) technology. Finally, an eco-friendly coating was tested to improve the durability of PLA 3D products. This work represents a window into the possibility of creating new systems for reusing local waste materials while promoting sustainable economic growth and a circular economy.