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Summary Past climatic fluctuations have played a major role in shaping the current plant biodiversity. Although harbouring an exceptional biota, oceanic islands have received little attention in studies on species demographic history and past vegetation patterns. We investigated the impact of past climatic changes on the effective population size of a tree (Coffea mauritiana) that is endemic to Reunion Island, located in the south‐western Indian Ocean (SWIO). Demographic changes were inferred using summary statistics calculated from genomic data. Using ecological niche modelling and the current distribution of genetic diversity, the paleodistribution of the species was also assessed. A reduction in the effective population size of C. mauritiana during the last glaciation maximum was inferred. The distribution of the species was reduced on the western side of the island, due to low rainfall. It appeared that a major reduction in rainfall and a slight temperature decrease prevailed in the SWIO. Our findings indicated that analyses on the current patterns of intraspecific genetic variations can efficiently contribute to past climatic changes characterisation in remote islands. Identifying area with higher resilience in oceanic islands could provide guidance in forest management and conservation faced to the global climate change.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.

Seagrass systems of the Western Pacific region are biodiverse habitats, providing vital services to ecosystems and humans over a vast geographic range. SeagrassNet is a worldwide monitoring program that collects data on seagrass habitats, including the ten locations across the Western Pacific reported here where change at various scales was rapidly detected. Three sites remote from human influence were stable. Seagrasses declined largely due to increased nutrient loading (4 sites) and increased sedimentation (3 sites), the two most common stressors of seagrass worldwide. Two sites experienced near-total loss from of excess sedimentation, followed by partial recovery once sedimentation was reduced. Species shifts were observed at every site with recovering sites colonized by pioneer species. Regulation of watersheds is essential if marine protected areas are to preserve seagrass meadows. Seagrasses in the Western Pacific experience stress due to human impacts despite the vastness of the ocean area and low development pressures.

AbstractEcologists are increasingly aware of the importance of environmental variability in natural systems. Climate change is affecting both the mean and the variability in weather and, in particular, the effect of changes in variability is poorly understood. Organisms are subject to selection imposed by both the mean and the range of environmental variation experienced by their ancestors. Changes in the variability in a critical environmental factor may therefore have consequences for vital rates and population dynamics. Here, we examine ≥90‐year trends in different components of climate (precipitation mean and coefficient of variation (CV); temperature mean, seasonal amplitude and residual variance) and consider the effects of these components on survival and recruitment in a population of Eurasian beavers (n = 242) over 13 recent years. Within climatic data, no trends in precipitation were detected, but trends in all components of temperature were observed, with mean and residual variance increasing and seasonal amplitude decreasing over time. A higher survival rate was linked (in order of influence based on Akaike weights) to lower precipitation CV (kits, juveniles and dominant adults), lower residual variance of temperature (dominant adults) and lower mean precipitation (kits and juveniles). No significant effects were found on the survival of nondominant adults, although the sample size for this category was low. Greater recruitment was linked (in order of influence) to higher seasonal amplitude of temperature, lower mean precipitation, lower residual variance in temperature and higher precipitation CV. Both climate means and variance, thus proved significant to population dynamics; although, overall, components describing variance were more influential than those describing mean values. That environmental variation proves significant to a generalist, wide‐ranging species, at the slow end of the slow‐fast continuum of life histories, has broad implications for population regulation and the evolution of life histories.

Abstract In the context of climate change, global industrialised nations are grappling with transforming energy networks to support a low carbon future. Using an energy justice framework this work aims to understand holistic outcomes of one low-carbon energy network intervention: demand-side response enacted on domestic heat pumps. By exploring participants’ lived experience of a pilot project, from recruitment to installation and use, this work reveals how injustices were reduced, introduced and amplified. Choice, consent, cost, comfort, disruption, and control are highlighted as key aspects of interest when considering the distributive, procedural, and recognition implications of this domestic innovation. For a net reduction of energy injustices to be realised, we highlight the need for project designers to work in partnership with end users to optimise the benefits for the household and the electricity system. Whilst this is a UK study, the themes and findings are internationally applicable for interventions that aim to harness the flexibility of heating, the largest global energy end-use.

Abstract The hybrid pneumatic power system (HPPS) proposed in this research replaces the battery’s electric-chemical energy with flow work and optimizes the management and utilization of the energy. This power system is able to keep the internal-combustion engine working at its optimal condition and turn its waste energy into effective mechanical energy and so enhance the thermal efficiency of the whole system. Using computer simulation software ITI-SIM, this study simulates the overall dynamic characteristics of the system in accordance with the regulated running-vehicle test-mode ECE47, and, with experimental verification and analysis, proves that this system can meet the requirements of the standard running-car mode. As for recycling the waste energy, the experimental results show that this design could offset the shortcomings of the low-density of pneumatic power and so effectively enhance the efficiency of the whole system.

Abstract In this study, soil temperatures at different depths in Turkey's different regions were investigated theoretically. Soil temperature data are critical for different research interests such as ecology, biology, technique processes, forestry, agriculture, energy, food sector, ground heat exchanger applications, thermal energy storage applications, and so forth. This investigation gives information related to the prediction of soil temperature's dependence with depth and time especially for shallow geothermal applications. Soil temperature values depend on a great deal of varied parameters such as thermal conductivity, short term climatic conditions and moisture content. The main issue is that despite these temperatures are extremely important values, they can not be obtained in a short time. Due to this reason, we study a mathematical model related to the prediction of soil temperature. Within this context, 81 cities and their approximately 300.000 data, both, monthly air and soil temperatures between 1960 and 2015 were studied and finally seven regions in Turkey were investigated and final average soil temperature values were achieved. Measured data taken from the Izmir State Meteorological Station, and predicted soil temperatures at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm were analyzed for each region in Turkey according to data obtained fifty years ago. Finally, at depths of 5 cm, 10 cm, 20 cm, 50 cm and 100 cm, the maximum average percentage errors in Turkey were 16%, 14.8%, 13.5%, 14.4%, 13.9% respectively. In conclusion, we evaluate the relationship between ambient air temperatures and soil temperatures in terms of depths from 5 to 3000 cm.

Highly efficient nanocatalysts which can selectively decompose hydrous hydrazine for hydrogen production are introduced.

The fast decay in PEM fuel cells of a highly active, high performance, but unstable Fe/N/C catalyst like our NC_Ar + NH3 follows a chemical, not an electrochemical, demetallation mechanism for its ORR active FeN4 sites in the catalyst micropores.