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Traditionally, critical infrastructures demand for high dependability, being the services they provide essential to human beings and the society at large. However, more recent attention to cautious usage of energy resources is changing this vision and calls for solutions accounting for appropriate multi-requirements combinations when developing a critical infrastructure. In such a context, analysis supports able to assist the designer in envisioning a satisfactory trade-off among the multi-requirements for the system at hand are highly helpful. In this paper, the focus is on the railway sector and the contribution is a stochastic model-based analysis framework to quantitatively assess trade-offs between dependability indicators and electrical energy consumption incurred by the rail switch heating system.Moving from a preliminary study that concentrated on energy consumption only, the analysis framework has been extended to become a solid support to devise appropriate tuning of the heating policy that guarantees satisfactory trade-offs between dependability and energy consumption. An evaluation campaign in a variety of climate scenarios demonstrates the feasibility and utility of the developed framework.

This study provides the first comprehensive overview of the sustainability performance of the hotel sector in the Himalayan region: Sagarmatha National Park and Buffer Zone, using both environmental, economic, and technical criteria. In particular, the performance of 45 buildings in this region were measured and quantified in terms of life cycle based carbon footprint, life cycle costs, heat loss rate, number of guests, energy consumption, and area. Buildings were classified into three types: traditional, semi-modern and modern. The statistical analysis included testing for significant differences between such categories by means of ANOVA, and determination of the correlation between the same parameters. Results show a significant difference between the buildings’ total carbon footprint and operation stage carbon footprint while, there is no significant difference between the buildings’ life cycle costs. Traditional buildings have on average the largest carbon footprint and life-cycle cost over the typical building lifespan of 50 years of building lifespan. The ANOVA tests highlight how heat loss rate, size of the building and number of tourists in the hotels are significantly different across the building types. A strong positive correlation is observed between environmental impact, economic impact and energy consumption for the household activities, and a negative correlation with the number of guests and building size. By considering several buildings, this study allows to draw new and more general conclusions about effective sustainability strategies in the whole hotel sector in the Himalayan region. In particular, it shows that reducing impacts in the operation stage should be highly prioritized, focusing on reducing energy consumption and heat loss and shifting to the use of renewable energy sources.

AbstractMountain treelines are thought to be sensitive to climate change. However, how climate impacts mountain treelines is not yet fully understood as treelines may also be affected by other human activities. Here, we focus on “closed‐loop” mountain treelines (CLMT) that completely encircle a mountain and are less likely to have been influenced by human land‐use change. We detect a total length of ~916,425 km of CLMT across 243 mountain ranges globally and reveal a bimodal latitudinal distribution of treeline elevations with higher treeline elevations occurring at greater distances from the coast. Spatially, we find that temperature is the main climatic driver of treeline elevation in boreal and tropical regions, whereas precipitation drives CLMT position in temperate zones. Temporally, we show that 70% of CLMT have moved upward, with a mean shift rate of 1.2 m/year over the first decade of the 21st century. CLMT are shifting fastest in the tropics (mean of 3.1 m/year), but with greater variability. Our work provides a new mountain treeline database that isolates climate impacts from other anthropogenic pressures, and has important implications for biodiversity, natural resources, and ecosystem adaptation in a changing climate.

Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.

This research provides new techno-economic insights into integrating flexible combined-cycle gas turbines with post-combustion carbon capture and storage (CCGT-CCS) for low-carbon power systems. This study developed a versatile unit-commitment optimisation model of CCGT-CCS. This research highlights the model’s adaptability, accommodating diverse techno-economic configurations, feed gases (e.g., biomethane or fossil natural gas), carbon capture rates, and policy instruments. This generalisation empowers seamless application in various policy and market contexts, making the model a potent tool for researchers and policymakers. While the case study focuses on the UK, the findings are relevant for most low-carbon power systems with variable renewable supplies. Analysing the UK’s net-zero scenarios from 2030 to 2050, the economic viability of flexible CCGT-CCS was highlighted. Intertemporal flexibility proves highly valuable with greater electricity price volatility, with a total ROI range of 81–246 %, surpassing the CCGT-CCS plant’s ROI (7–64 %). A flexible solvent storage solution should be seen in the context of the overall system ‘flexibility’ requirements of a low-carbon power system. On a cost basis, solvent storage represents just a fraction of the capital costs of more “mainstream” energy storage technologies, such as lithium-ion batteries or hydro-pumped storage, while CCGT-CCS offers firm power. Overall, while seen as a rather technical solution, if abated fossil fuel generation is to be part of a future low-carbon power system, having this flexibility adds economic benefits not just to operators but also improves overall system security and complements high shares of variable renewables on the grid.