The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

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 gCO_2eq/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.

Data envelopment analysis (DEA) is a useful method for determining relative efficiency in many types of businesses, including the transport sector. In line with the European Union’s (EU) policy of sustainable development of transport, external costs become the competitiveness factor of the transport route valorization. Presenting specific DEA settings, the paper aims to show and test a developed model for determining the optimal transport route among alternatives towards the same destination where external cost as a socio-ecological factor is included in DEA, along with transport cost (quantitative factor) and transport time (qualitative factor). In order to adhere to the principles of the least possible energy consumption, the given distance that also included in DEA settings represents the shortest route between the starting point and destination, as a unique and constant output variable. Therefore, the optimal direction selected by the DEA stands for the green route. The capabilities of the DEA, set up in this way within the broader model, are demonstrated in the practical case.