University campuses represent a heterogeneous ecosystem as to social, economic, energetic, and personal travel planning with a huge impact on hosting cities and territories. Sustainable policies are thus fundamental to reduce this impact and to adopt ecological behaviors. The measures for any University Sustainability Plan should be evaluated in terms of GHG emissions, as well as the overall impact of the university itself. Carbon footprint (CF) calculation is a relevant Decision Support tool that allows university organizations to measure and communicate the environmental effects of their activities. The aim of this paper is to present a carbon footprint methodology specifically designed to calculate the carbon footprint of large universities. The methodology was applied to calculate the CF of the University of Bologna by following international standards—i.e., the GHG protocol, the ISO 14064, and the ISO/TR 14069 guide—to understand the environmental impact caused by greenhouse gas emissions from direct and indirect university activities. The study was conducted upon the data available in 2020 and then was compared to the 2018 data, with the aim to recognize if the effect of the pandemic could have altered the results. In 2020, the University of Bologna emitted 16,467 tCO₂e which became 15,753 tCO₂e considering the offset and avoided emission provided by the internal production of energy from renewable sources. Comparison between 2020 and 2018 shows how, in 2018, most of the emissions came from transportation, representing 74% of the total emissions, while in 2020 almost 50% of total emissions derived by IT procurements. The case application demonstrates the way with which the methodology may be applied to assess environmental impact for complex university campuses.

In a general urban planning context, in which sustainable active mobility progressively takes up increasing attention, studies of cyclists’ attitudes and behaviors represent a relevant step to help any enhancing measures for urban cycling. Among different categories, university student cyclists represent a still unidentified class, despite the relevant impacts in terms of mass and variability of attitudes in urban areas. The novelty of this paper is to propose an innovative overview on the specific category of university student cyclists. The integrated methodology, based on direct observation through GPS detection, GIS processing, and qualitative survey, permits the evaluation of some interesting issues related to students’ propensity to cycling and their mobility patterns. The approach finds relevance in speed, frequency of movements, routing, and related infrastructure preferences. The methodology has been applied to a sample of more than 300 students of the University of Bologna who were allowed an original university-designed bicycle from February 2021 to June 2021. The analysis was applied in the Bologna urban area and allowed the evaluation of students’ preferences of using existing cycle paths, when available, the limited relevance of speed factors, the main distribution of commuter journeys concentrated in the main avenues directed to city center, and other behaviors.

In recent years, autonomous vehicles have received increasing attention and many studies in the literature have discussed the potentialities and the opportunities they could offer. Despite the potential benefits, mainly related to the expected reduction in accidents and congestion phenomena as well as the potentially improved social inclusion of people with driving difficulties (e.g., people with physical disabilities or elderly people), many aspects remain to be addressed, mainly for understanding users’ acceptance in the case of collective transport vehicles. This study proposes an analysis based on a survey aimed at exploring user’s preferences with respect to the use of autonomous shuttles (ASs) for tourism purposes. The main correlations between the variables considered and the preferences of potential users have been discussed. Interviewees expressed high confidence in AS technology, although the analyses performed about willingness to pay show that users give more relevance to the provided transport services than the AS technology.

Background: Urban air pollution, particularly fine particulate matter (PM2.5 and PM10), poses health risks, including damage to the ocular surface. This pilot study (BIKE-EYE) aimed to assess ocular exposure to airborne pollutants during bicycle commuting and to evaluate particle presence in human tear fluid. Methods: Fifteen healthy volunteers wore portable sensors measuring PM2.5 and PM10 during daily bike commutes over six months. Exposure was calculated as time-weighted integrals over the ten days preceding an ophthalmologic exam assessing conjunctival hyperemia, epithelial damage, tear film quality, and meibomian gland function. Ocular symptoms were assessed via the Ocular Surface Disease Index (OSDI). Tear samples were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Results: Higher pollutant exposure was significantly associated with conjunctival hyperemia and corneal epithelial damage, while temperature and humidity showed no effect. OSDI scores moderately correlated with PM levels. SEM/EDS analysis confirmed airborne particles in post-exposure tear samples, including carbonaceous material, aluminosilicates, iron, and sulfur compounds. Conclusions: Ocular surface alterations and conjunctival hyperemia were significantly associated with air pollution exposure, while subjective symptoms showed weaker trends. The detection of particulate matter in human tear fluid supports the use of the ocular surface as a sensitive, non-invasive tool for biomonitoring. These findings highlight its potential role in early warning systems for pollution-related health effects, with implications for public health surveillance and urban planning.