• Energy Research
• Open Access close
• Embargo close
• 7. Clean energy close
• 15. Life on land close
• University of Cambridge close

The ERC-funded FRAGSUS Project (Fragility and sustainability in small island environments: adaptation, cultural change and collapse in prehistory, 2013–18), led by Caroline Malone (Queens University Belfast) has explored issues of environmental fragility and Neolithic social resilience and sustainability during the Holocene period in the Maltese Islands. This, the first volume of three, presents the palaeo-environmental story of early Maltese landscapes. The project employed a programme of high-resolution chronological and stratigraphic investigations of the valley systems on Malta and Gozo. Buried deposits extracted through coring and geoarchaeological study yielded rich and chronologically controlled data that allow an important new understanding of environmental change in the islands. The study combined AMS radiocarbon and OSL chronologies with detailed palynological, molluscan and geoarchaeological analyses. These enable environmental reconstruction of prehistoric landscapes and the changing resources exploited by the islanders between the seventh and second millennia bc. The interdisciplinary studies combined with excavated economic and environmental materials from archaeological sites allows Temple landscapes to examine the dramatic and damaging impacts made by the first farming communities on the islands’ soil and resources. The project reveals the remarkable resilience of the soil-vegetational system of the island landscapes, as well as the adaptations made by Neolithic communities to harness their productivity, in the face of climatic change and inexorable soil erosion. Neolithic people evidently understood how to maintain soil fertility and cope with the inherently unstable changing landscapes of Malta. In contrast, second millennium bc Bronze Age societies failed to adapt effectively to the long-term aridifying trend so clearly highlighted in the soil and vegetation record. This failure led to severe and irreversible erosion and very different and short-lived socio-economic systems across the Maltese islands.

This is the data set associated with the journal article titled 'A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles'. The data files are in MATLAB file format. Figure 4 was generated using the vehicle speed profiles of an electric bus (EB) when it operated in London and of the EB's baseline simulation model, and using the state of charge (SOC) profiles of the EB when it operated in London and of the EB's baseline simulation model. Figure 5 was generated using the electricity consumption of the EB's baseline simulation model and of the EB's computationally efficient model for different drive cycles. It shows how the proposed computationally efficient model's electricity consumption compares with that of the baseline model. Figure 6 was generated using the speed profiles of a diesel heavy goods vehicle (HGV) when it operated on-road and of the HGV's baseline simulation model, and using the fuel consumption profiles of the diesel HGV when it operated on-road and of the HGV's baseline simulation model. It also contains the road elevation profile. Figure 7 was generated using the fuel consumption values of the diesel HGV's baseline simulation model and of the HGV's computationally efficient model for different drive cycles. It shows how the proposed computationally efficient model's diesel consumption compares with that of the baseline model. All the files were generated using MATLAB software package from Mathworks.

Frustration in the energy landscape has been quantified using a number of different metrics. These metrics have been applied to the databases in the subdirectories, which contain information about the energy landscape in the form of minima and transition states. The frustration metrics are coded within The PATHSAMPLE program, available for download at: http://www-wales.ch.cam.ac.uk/PATHSAMPLE/ Documentation can also be found at the same web address. The relevant keywords for the work described in this paper can be found at: http://www-wales.ch.cam.ac.uk/PATHSAMPLE.2.1.doc/node5.html Keywords CV, SHANNON and SHANNONR are described in the documentation. SHANNON and SHANNONR calculate frustration measures for the landscape as a function of temperature. The CV keyword gives a heat capacity calculation and hence allows an estimate of the melting temperature. Each Directory holds the database for the relevant system and input files for the PATHSAMPLE program. The output of the frustration calculation, for SHANNON, is given in Shannon.out. Some databases also contain output for the heat capacity calculation in CV.out. If required the OPTIM program is also available for download at: http://www-wales.ch.cam.ac.uk/OPTIM/ Documentation can also be found at the same web address. The program for drawing disconnectivity graphs, disconnectionDPS, can be found at: http://www-wales.ch.cam.ac.uk/software.html There is also input and output for the disconnectivity graph construction in each directory.

See the README file for a detailed description of the dataset. The data has been collected using a number of techniques. Figure 1 contains data gathered using photoelectron spectroscopy and have been saved in a .vms format. The data has been analysed and processed using CasaXPS and Origin respectively. Figure 2 contains data from photoluminescence and Transient absorbance spectroscopy and have been saved as .asc and .txt files. Figure 3 contains plots that have been gathered via X-ray diffraction and Time-of-flight Secondary Ion Mass spectroscopy techniques (ToF-SIMs). The ToF-SIMs data includes the response from all the species that evolves with ion bombardment but the species of interest are I2- and O2-. Figure 4 contains all the photovoltaic performance data in .txt format which can be opened and plotted using the Matlab script provided. The photovoltaic metrics, PCE, Jsc, VOC and FF have been generated with the Matlab script which can be taken forward for the box plot plotting as shown in Figure 4. Figure 5 is a summary of the PICTs data which has been derived from the raw data in Figure S11. Please see the ReadME files for more details.

This repository contains the data related to the Data in Brief article titled: Bulk and critical material demand for selected ‘Starter Kit’ energy system models. The data include the modeled mass of materials and their embodied emissions. A metadata file is also included to clarify the units, materials and scenario names.

Monocultural rubber plantations have replaced tropical forest, causing biodiversity loss. While protecting intact or semi-intact biodiverse forest is paramount, improving biodiversity value within the 11.4 million hectares of existing rubber plantations could offer important conservation benefits, if yields are also maintained. Some farmers practice agroforestry with high-yielding clonal rubber varieties to increase and diversify incomes. Here, we ask whether such rubber agroforestry improves biodiversity value or affects rubber yields relative to monoculture. We surveyed birds, fruit-feeding butterflies and reptiles in 25 monocultural and 39 agroforest smallholder rubber plots in Thailand, the world’s biggest rubber producer. Management and vegetation structure data were collected from each plot, and landscape composition around plots was quantified. Rubber yield data were collected for a separate set of 34 monocultural and 47 agroforest rubber plots in the same region. Reported rubber yields did not differ between agroforests and monocultures, meaning adoption of agroforestry in this context should not increase land demand for natural rubber. Butterfly richness was greater in agroforests, where richness increased with greater natural forest extent in the landscape. Bird and reptile richness were similar between agroforests and monocultures, but bird richness increased with the height of herbaceous vegetation inside rubber plots. Species composition of butterflies differed between agroforests and monocultures, and in response to natural forest extent, while bird composition was influenced by herbaceous vegetation height within plots, the density of non-rubber trees within plots (representing agroforestry complexity), and natural forest extent in the landscape. Reptile composition was influenced by canopy cover and open habitat extent in the landscape. Conservation priority and forest-dependent birds were not supported within rubber. Synthesis and applications. Rubber agroforestry using clonal varieties provides modest biodiversity benefits relative to monocultures, without compromising yields. Agroforests may also generate ecosystem service and livelihood benefits. Management of monocultural rubber production to increase inter-row vegetation height and complexity may further benefit biodiversity. However, biodiversity losses from encroachment of rubber onto forests will not be offset by rubber agroforestry or rubber plot management. This evidence is important for developing guidelines around biodiversity-friendly rubber and sustainable supply chains, and for farmers interested in diversifying rubber production. The accompanying ReadMe.txt file explains the contents of each .csv file, including definitions of each column. Sampling protocols are outlined in the paper in Journal of Applied Ecology.

Abstract for communication: "Bismuth-based compounds have recently gained increasing attention as potentially non-toxic and defect-tolerant solar absorbers. However, many of the new materials recently investigated show limited photovoltaic performance. Herein, we explore in detail one such compound through theory and experiment: bismuth oxyiodide (BiOI). We grow BiOI thin films by chemical vapor transport and find them to maintain the same tetragonal phase in ambient air for at least 197 days. Our computations suggest BiOI to be tolerant to antisite and vacancy defects. We demonstrate an all-inorganic solar cell (ITO|NiOx|BiOI|ZnO|Al) with negligible hysteresis and up to 80% external quantum efficiency under select monochromatic excitation. The short-circuit current densities and power conversion efficiencies under AM 1.5G illumination are nearly double those of previously-reported BiOI solar cells, as well as other bismuth halide and chalcohalide photovoltaics recently explored by many groups. Through a detailed loss analysis using optical characterization, photoemission spectroscopy and device modeling, we provide direction for future improvements in efficiency. Our work demonstrates that BiOI, previously considered to be a poor photocatalyst, is promising for photovoltaics."

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy