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It's not only the carbon in the trees Forest loss affects climate not just because of the impacts it has on the carbon cycle, but also because of how it affects the fluxes of energy and water between the land and the atmosphere. Evaluating global impact is complicated because deforestation can produce different results in different climate zones, making it hard to determine large-scale trends rather than more local ones. Alkama and Cescatti conducted a global assessment of the biophysical effects of forest cover change. Forest loss amplifies diurnal temperature variations, increases mean and maximum air temperatures, and causes a significant amount of warming when compared to CO 2 emission from land-use change. Science , this issue p. 600

Abstract To accelerate progress in building sustainability as well as to aid balance supply and demand in the presence of renewable energy generation, a tailored characterization method for the energy flexibility (EF) of buildings is needed. In this paper, a novel two-stage non-intrusive EF characterization method is proposed. In the first stage, unlike the previous studies in which an individual meter is installed on appliances to extract their consumption pattern, a novel unsupervised event-matching non-intrusive load monitoring method is utilized which is time and cost-effective. Moreover, previous research characterize the EF considering as early and as late as possible appliances’ start-time. However, the usage behavior of consumers affects the start-time of appliances. To tackle this issue, in the second stage of the proposed method, the usage behavior of consumers is taken into account for the EF characterization. The proposed method is verified in an individual building level and aggregated level including 50 residential buildings. The obtained results show that the proposed usage behavior-oriented method, characterizes the available aggregated EF with higher accuracy, without adding complexity to the system. These results can be used by aggregators to harness the available EF of buildings to flatten demand consumption by incentivizing potential consumers.

The brewer's yeast Saccharomyces cerevisiae displays a much higher ethanol tolerance compared to most other organisms, and it is therefore commonly used for the industrial production of bioethanol and alcoholic beverages. However, the genetic determinants underlying this yeast's exceptional ethanol tolerance have proven difficult to elucidate. In this perspective, we discuss how different types of experiments have contributed to our understanding of the toxic effects of ethanol and the mechanisms and complex genetics underlying ethanol tolerance. In a second part, we summarize the different routes and challenges involved in obtaining superior industrial yeasts with improved ethanol tolerance.

Two new species of Torodorinae: Caveana plenalinea Park, sp. nov. and Antiochtha hemitatos Park, sp. nov., are described from Laos. At the same time, three species of the genus Torodora are reported for the first time from Laos.

This paper discusses the concept of 'sustainable water services' and suggests a multicriteria method to assess it. Although conceptual discussions around this notion are often confined to the triple bottom line (TBL) classification, it seems that the TBL approach does not provide the suitable framework to measure water services sustainability. It is argued that assets (or technical) and governance aspects are also indispensable dimensions. After revisiting the concept in broader terms, several criteria and metrics are suggested to operationalize and quantify the sustainability level of urban water services. To aggregate the numerous aspects that are relevant in this scope a multicriteria decision analysis approach is proposed. Furthermore, to illustrate the real-world application of the method, a multicriteria model applicable to the case of Portugal was developed and calibrated with the input of a decision-maker with extensive experience in the sector. With the suggested framework it is possible to assess the global sustainability level of the water services (e.g. of each utility) and also the performance in each particular dimension ('social', 'environmental', 'economic', 'governance' and 'assets').

Estonia is among the countries with the most energy-efficient Near Zero Energy Buildings (NZEBs) in Europe, i.e. their energy consumption is the lowest. From the energy management prospects, on one hand, the rising price of electricity and decreasing the feed-in prices, and on the other hand, the decreasing price of battery energy storage system (BESS) have made the self-consumption of NZEBs profitable. This paper develops a simple tool to study the optimal sizing of BESS and increase the profitability of NZEBs. The BESS sizing is done considering the installed PV capacity in the NZEB, the energy consumption profile, and the local electricity prices. The results of this paper demonstrate to the NZEB owner and planers that in case of self-consumption, investment in PV systems could be more profitable when it is combined with BESS. Simulation results are presented to validate the proposed BESS sizing tool and analyze the sensitivity of results to changes in electricity and battery system price.

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Photovoltaic (PV) modules and solar plants are one of the main drivers towards zero-carbon future. Energy communities that are engaging citizens through collective energy actions can reinforce positive social norms and support the energy transition. Furthermore, by incorporating Artificial Intelligence (AI) techniques, innovative applications can be developed with huge potential, such as supply and demand management, energy efficiency actions, grid operations and maintenance actions. In this context, the scope of this paper is to present an approach for forecasting an energy cooperative’s solar plant short term production by using its infrastructure and monitoring system. More specifically, four Machine Learning (ML) and Deep Learning (DL) algorithms are proposed and trained in an operational solar plant producing high accuracy short-term forecasts up to 6 hours. The results can be used for scheduling supply of the energy communities and set the base for more complex applications that require accurate short-term predictions, such as predictive maintenance.