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Shifts in rainfall patterns and increasing temperatures associated with climate change are likely to cause widespread forest decline in regions where droughts are predicted to increase in duration and severity. One primary cause of productivity loss and plant mortality during drought is hydraulic failure. Drought stress creates trapped gas emboli in the water transport system, which reduces the ability of plants to supply water to leaves for photosynthetic gas exchange and can ultimately result in desiccation and mortality. At present we lack a clear picture of how thresholds to hydraulic failure vary across a broad range of species and environments, despite many individual experiments. Here we draw together published and unpublished data on the vulnerability of the transport system to drought-induced embolism for a large number of woody species, with a view to examining the likely consequences of climate change for forest biomes. We show that 70% of 226 forest species from 81 sites worldwide operate with narrow (<1 megapascal) hydraulic safety margins against injurious levels of drought stress and therefore potentially face long-term reductions in productivity and survival if temperature and aridity increase as predicted for many regions across the globe. Safety margins are largely independent of mean annual precipitation, showing that there is global convergence in the vulnerability of forests to drought, with all forest biomes equally vulnerable to hydraulic failure regardless of their current rainfall environment. These findings provide insight into why drought-induced forest decline is occurring not only in arid regions but also in wet forests not normally considered at drought risk.

In the last two centuries, land-use change (LUC) has been the most important direct change driver for terrestrial ecosystems. In contrast with the consequent ecosystem degradation, forward-looking spatial policies and target landscape and land-use planning processes are needed from a sustainability perspective. The present paper proposes a framework of action, including different landscape-planning and ecological approaches: from spatial modelling to recognize LUC and build different scenarios, to ecosystem service (ES) assessment to evaluate possible environmental impacts. Three different scenarios were explored: Trend, No Tillage, and Energy crops. The sediment delivery ratio and carbon storage and sequestration ESs were assessed and compared for each scenario. The results show that regional development in line with past trends could lead to further land degradation (with ES value losses, in a decade, greater than 5%). Instead, the two scenarios proposed in compliance with EU policies could bring benefits, if only those related to moderate LUCs and respecting the naturally grass-vegetated land. The aim of the paper is to support decision makers and local communities in the landscape planning landscape planning process. From the local to global scale, guided and shared LUC management allows us to implement sustainable development, based not only on a deep knowledge of the physical environment but also of social and economic issues.

With reference to a classical wet tank equipped with a wastewater submersible pump, in this research, an advanced numerical model has been used in order to obtain the optimal pump scheduling of on/off operation and variable pump speed. Then, in order to evaluate the time decay of pump performances, the mechanical wear has been artificially simulated and the performance curves have been experimentally obtained for different rotational speeds. Finally, the benefits, as well as the feasibility, of pump scheduling have been evaluated for differing operating conditions. According to the results, the optimal pump scheduling achieves large energy savings up to 43%, for soft mechanical wear. If the mechanical wear is considered, the energy savings are large as well, between 35.60% and 26.70%, for medium and hard mechanical wear, respectively. On the other hand, the limitation of such a strategy has been highlighted: the feasibility of pump scheduling is limited by the elevation of the downstream tank. According to the results, energy savings can be achieved until the elevation of the downstream tank is 67% of the pressure head at the best efficiency point, whereas such percentage decreases to 50% for hard mechanical wear. Finally, the results show that plant efficiency is strongly affected by the mechanical wear: an accurate maintenance of the pumping system is therefore recommended in order to attenuate the time decay of pump performances.

A Sustainable Urban Mobility Plan (SUMP) is a strategic (long period) transportation plan aimed to improve welfare of both people and workers living in the city as well as in its surrounding area. Compared to “traditional” urban transportation plans, SUMP moves the focus from traffic (vehicles) to people with significant advancements in the direction of a sustainable mobility jointly with the quality of life. According to the recent European Guidelines, this type of strategic plan has to be built on existing planning practices and take due consideration: the integration, the participation, and evaluation. Starting from these considerations, in absence of a detailed national guideline implementing SUMP, the aim of this paper was to apply an innovative theoretical decision-making approachfor the development of the Naples (Italy) SUMP. This case study appears to be interesting because of the specificity of Naples, that is the third largest Italian city and metropolitan area with the highest population density. In 2016, the city of Naples has efficiently concluded and ratified a first formalization of acts: 'The analysis of mobility system' and ‘The statement of strategic objectives

Abstract In recent years, Distributed Energy Systems (DESs) have been recognized as a good option for sustainable development of future energy systems. With growing environmental concerns, design optimization of DESs through economic assessments only is not sufficient. To achieve long-run sustainability of energy supply, the key idea of this paper is to investigate exergy assessments in DES design optimization to attain rational use of energy resources while considering energy qualities of supply and demand. By using low-temperature sources for low-quality thermal demand, the waste of high-quality energy can be reduced, and the overall exergy efficiency can be increased. Based on a pre-established superstructure, the aim is to determine numbers and sizes of energy devices in the DES and the corresponding operation strategies. A multi-objective linear problem is formulated to reduce the total annual cost and increase the overall exergy efficiency. The Pareto frontier is found to provide different design options for planners based on economic and sustainability priorities, through minimizing a weighted-sum of the total annual cost and primary exergy input, by using branch-and-cut. Numerical results demonstrate that different optimized DES configurations can be found according to the two objectives. Moreover, results also show that the total annual cost and primary exergy input are reduced by 20% - 30% as compared with conventional energy supply systems.

Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp–swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.