• Energy Research

The world of business is rapidly changing, not only thanks to digitization and technological transformation, but also to address challenges related to the environment and climate change, and to reduce its impact in terms of waste, emissions, and raw materials. The COVID-19 crisis and the European Green New Deal have also accelerated this transformation process. In this context, companies must be able to evaluate their commitment and contribution to sustainable development, and to adopt lower impact business models. To achieve this aim, companies need easy and accessible measurement tools. The tools currently available are based on quantitative or statistical approaches and require the process of large amounts of data. This approach is easily accessible to large companies, while small companies or craft businesses may be scared off, as they may lack the structures and expertise. This study fills this gap by presenting an innovative and easy-to-access methodology for assessing sustainability in companies. Through a qualitative assessment of interdependence among nine categories grouping multiple environmental, social, and governance indicators, companies can evaluate their impact on the 17 SDGs and on the 3 ESG dimensions. The result can be used by the companies to design strategies for their businesses and plan future actions to improve circular models, thanks to the awareness and benefits gained from the analysis. The methodology has been applied to the case study of Ohoskin © 2021,

In this study, we introduce an innovative application of clustering algorithms to assess and appraise Italy’s alignment with respect to the Sustainable Development Goals (SDGs), focusing on those related to climate change and the agrifood market. Specifically, we examined SDG 02: Zero Hunger, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Change, to evaluate Italy’s performance in one of its most critical economic sectors. Beyond performance analysis, we administered a questionnaire to a cross-section of the Italian populace to gain deeper insights into their awareness of sustainability in everyday grocery shopping and their understanding of SDGs. Furthermore, we employed an unsupervised machine learning approach in our research to conduct a comprehensive evaluation of SDGs across European countries and position Italy relative to the others. Additionally, we conducted a detailed analysis of the responses to a newly designed questionnaire to gain a reasonable description of the population’s perspective on the research topic. A general poor performance in the SDGs indicators emerged for Italy. However, from the questionnaire results, an overall significant interest in the sustainability of the acquired products from italian citizens.

A model for the degradation of xylose and ethanol production by Klebsiella planticola is proposed and compared with the exponential and Michaelis-Menten approaches. This model is based on the energy system diagrams and it is a simplified version of a previous model developed for the glucose and ethanol kinetics of the yeast Saccharomices cerevisiae. In this model the dynamics of the substrate and of the final product are strictly related by means of the cellular activity. This model shows superior performances with respect to the two alternatives, behaving better along the whole dynamics.