The maritime industry is addressing environmental issues, and “cold ironing” offers a promising solution. This method involves supplying ships at port with energy, reducing fossil fuel dependence and emissions, and aiding in global climate change efforts. It is especially important for islands like Lesvos, which suffer from high energy costs and environmental issues due to imported fossil fuel reliance. However, research gaps exist in using renewable energy sources (RES) for cold ironing, mainly due to insufficient data on power needs and lack of monitoring for precise calculations and the very limited applications for the case of non-interconnected islands. This study uses real data from the port of Lesvos to evaluate power requirements for cold ironing and assesses the viability of a wind power park for an electrified port with the novelty and uniqueness of developing the application on a non-interconnected island. It also examines potential CO₂ emission reductions. Data from Marine Traffic S.A. were used, considering factors like ship arrivals, hoteling duration, and engine types. This study also includes a simulation using RETScreen software for a 20 MW wind park intended for port operations. The findings show that the monthly energy demand at Mytilene port is around 6118 MWh, with an average power demand of 8.2 MW. The simulated wind park could supply about 72,080 MWh yearly, with a significant surplus (14,956 MWh annually) exportable to the grid. However, demand fluctuations mean the port might need an extra 924 MWh from the main grid. This underscores the need for additional strategies like energy storage and demand–response practices to fully transition to 100% RES-powered operations.

This study examines consumer preferences for bio-based products. Drawing from a sample of Greek consumers (n = 533) and the use of self-reported scales, we assess the effects of individuals’ characteristics such as innovativeness, trust in science and technology, environmental concern, and previous experience with bio-products on consumer preferences, i.e., willingness to pay (WTP) for (a) a bio-nylon jacket, (b) a pack of bio-based breadsticks, and (c) a bioplastic bottled water. Findings indicate that certain consumer- and product-specific attributes may influence WTP for the three bio-based products included in our study and offer insights on the extent to which such characteristics may shape purchase decisions for products of this growing market. In this respect, the results encapsulate managerial/practical and policy implications toward the development of appropriate advertising messages and awareness-raising campaigns and indicate the need to further explore consumers’ attitudes and purchase decisions for bio-based products in Greece.

The citizen’s carbon footprint is affected by various factors, but mostly by electricity consumption, daily transportation, travelling habits, as well as municipal solid waste management, a critical factor that is not frequently incorporated in carbon footprint estimations. This paper proposes a new carbon footprint calculator, the Carbon Footprint Simulation Dashboard—COMPAIR’s CO₂ calculator. The CO₂ calculator employs Citizen Science (CS) information in order to assist citizens in understanding and comparing how future carbon emissions will be modified in accordance with changes in their daily routines. The calculator offers several statistics, e.g., the country’s average carbon footprint and comparisons between EU members with respect to sectors that shape it, along with several recommendations for those domains where carbon footprint is significantly higher than the desired limits. The novelty of this tool is the integration of waste management strategies as a dimension of the total CO₂ calculation. In this framework, the study presents the development of the Carbon Footprint Simulation Dashboard while two different waste management scenarios are applied with different citizen behaviours on recycling and composting. The applications highlighted the consumption in buildings that account for almost 50% of the total CO₂ emissions, while the crucial role of transportation is also a key parameter. The different waste management scenarios showed a correlation between the CO₂ footprint and the overall consumption/disposal habits of citizens. In the case of advanced recycling and composting, waste management accounted for up to 7% of the CO₂ emissions, and this highlights the significance of behavioural choices.

The standardized precipitation index (SPI) is used for characterizing and predicting meteorological droughts on a range of time scales. However, in forecasting applications, when SPI is computed on the entire available dataset, prior to model-validation, significant biases are introduced, especially under changing climatic conditions. In this paper, we investigate the theoretical and numerical implications that arise when SPI is computed under stationary and non-stationary probability distributions. We demonstrate that both the stationary SPI and non-stationary SPI (NSPI) lead to increased information leakage to the training set with increased scales, which significantly affects the characterization of drought severity. The analysis is performed across about 36,500 basins in Sweden, and indicates that the stationary SPI is unable to capture the increased rainfall trend during the last decades and leads to systematic underestimation of wet events in the training set, affecting up to 22% of the drought events. NSPI captures the non-stationary characteristics of accumulated rainfall; however, it introduces biases to the training data affecting 19% of the drought events. The variability of NSPI bias has also been observed along the country’s climatic gradient with regions in snow climates strongly being affected. The findings propose that drought assessments under changing climatic conditions can be significantly influenced by the potential misuse of both SPI and NSPI, inducing bias in the characterization of drought events in the training data.