• Energy Research
• 2021-2025close
• GB close
• DE close
• EU close
• University of London close

Input data set for OPHELIA optimisation model investigating optimal heat decarbonization pathways through electrification for net zero economy in Great Britain. 

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

Replication data and materials for "Changing Prices in a Changing Climate: Electoral Competition and Fossil Fuel Taxation".

The decarbonisation of heating in the United Kingdom is likely to entail both the mass adoption of heat pumps and widespread development of district heating infrastructure. Estimation of the spatially disaggregated heat demand is needed for both electrical distribution network with electrified heating and for the development of district heating. The temporal variation of heat demand is important when considering the operation of district heating, thermal energy storage and electrical grid storage. The difference between the national and urban heat demands profiles will vary due to the type and occupancy of buildings leading to temporal variations which have not been widely surveyed. This paper develops a high-resolution spatiotemporal heat load model for Great Britain (GB: England, Scotland a Wales) by identifying the appropriate datasets, archetype segmentation and characterisation for the domestic and nondomestic building stock. This is applied to a thermal model and calibrated on the local scale using gas consumption statistics. The annual GB heat demand was in close agreement with other estimates and the peak demand was 219 GWth. The urban heat demand was found to have a lower peak to trough ratio than the average national demand profile. This will have important implications for the uptake of heating technologies and design of district heating.

Dataset 1: Brain and body size Published measures of brain size (g) for 131 species of seabird. Brain size estimates were generated from the measurement of adult skulls in museum collections using the endocast method. For each species, multiple specimens were measured and a mean value was calculated to provide a single, species-specific brain size estimate. To account for the allometric relationship between brain and body size, we also obtained information on body size (g). Dataset 2: Natal and adult dispersal rates We collated data on natal and adult dispersal rates by conducting a systematic literature search using the online database, Web of Science. The literature search generated a total of 793 papers and reports and from these, we extracted natal and adult dispersal rates. Here, natal dispersal is defined as the annual proportion of fledglings recruiting into a colony different from their natal colony, and adult dispersal is defined as the annual proportion of adults relocating to a new breeding colony. The final dataset included dispersal estimates from 47 studies and 29 species. For studies that provided multiple dispersal rate estimates, i.e., for different adult age classes or for males and females (n=9), we calculated a species mean value. For this final list, we also collated information on age at first breeding and fledging time. We selected age at first breeding to reflect the species-specific time that is available to prospect different colonies, and fledging time to reflect maternal investment. Where multiple species-specific values were provided for age at first breeding, we calculated the mean weighted by the percentage of individuals that had bred by each age. Where a range was provided for fledging time, we took the midpoint. Dataset 3: Extinction risk and threats We extracted a species’ threat status from the IUCN Red List database (iucnredlist.org, 2020) and their vulnerability to six relevant anthropogenic threats listed under the Threats Classification Scheme (v. 3.3, IUCN, 2020). The six anthropogenic threats we considered were: climate change, biological resource use (e.g., fishing), human intrusions and disturbance, invasive species, energy production and mining, and pollution. Threat vulnerability was classified as: ‘vulnerable’ or ‘not vulnerable’. We used the IUCN classifications to group species into two broader categories of extinction risk. Here, species classified as Critically Endangered (CR), Endangered (E) and Vulnerable (V) were defined as ‘threatened’, and species listed as Near Threatened (NT) and Least Concern (LC) were defined as ‘non-threatened’. The cognitive buffer hypothesis proposes that species with larger brains (relative to their body size) exhibit greater behavioural flexibility, conferring an advantage in unpredictable or novel environments. Therefore, behavioural flexibility – and relative brain size – are likely to be important predictors of a species’ vulnerability to anthropogenic pressures and, ultimately, extinction risk. However, current evidence linking brain size to species vulnerability and extinction risk is inconclusive. Furthermore, studies examining the relationship between relative brain size and behavioural flexibility have mainly focused on foraging innovations, whilst other forms of behavioural flexibility remain unexplored. In this study, we collate species-specific information and examine links between relative brain size, rates of natal and adult dispersal (a measure of flexibility in breeding site fidelity), vulnerability to six anthropogenic threats and extinction risk for 131 species of seabird. We focused our study on seabirds, a highly threatened group that displays large variation in both relative brain size and dispersal behaviour. We found a significant positive relationship between relative brain size and natal dispersal rate, suggesting that relative brain size could enhance flexibility in breeding site choice in seabirds, consistent with the cognitive buffer hypothesis. However, this relationship does not persist when we consider adult dispersal, possibly reflecting constraints imposed by mate selection and knowledge transfer in seabirds. We also show that relative brain size is negatively associated with vulnerability to climate change. These findings have immediate application for predicting interspecific variation in species’ vulnerability to climate change and identifying priority species for conservation.

We use enhanced-sampling simulations with an effective collective variable to study the activation of the β2-adrenergic receptor in the presence of ligands with different efficacy. The free-energy profiles are computed for the ligand-free (apo) receptor and binary (apo-receptor + G-protein α-subunit and receptor + ligand) and ternary complexes. The results are not only compatible with available experiments but also allow unprecedented structural insight into the nature of GPCR conformations along the activation pathway and their role in the activation mechanism. In particular, the simulations reveal an unexpected mode of action of partial agonists such as salmeterol and salbutamol that arises already in the binary complex without the G-protein. Specific differences in the polar interactions with residues in TM5, which are required to stabilize an optimal TM6 conformation that facilitates G-protein binding and receptor activation, play a major role in differentiating them from full agonists.

The fluctuations in oil have strong implications on many financial assets not to mention its relationship with gold prices, exchange rates, stock markets, and investor sentiment. Recent evidence suggests nonlinear contagion among the factors stated above with bivariate or trivariate settings and a throughout investigation of contagion and causality links by taking especially nonlinearity into consideration deserves special importance for the relevant literature. For this purpose, the paper explores the Markov switching generalized autoregressive conditional heteroskedasticity copula (MS-GARCH—copula) and MS-GARCH-copula-causality method and its statistical properties. The methods incorporate regime switching and causality analyses in addition to modeling nonlinearity in conditional volatility. For a sample covering daily observations for 4 January 2000–13 March 2020, the empirical findings revealed that: i. the incorporation of MS type nonlinearity to copula analysis provides important information, ii. the new method helps in the determination of regime-dependent tail dependence among oil, VIX, gold, exchange rates, and BIST stock market returns, in addition to determining the direction of causality in those regimes, iii. important policy implications are derived with the proposed methods given the distinction between high and low volatility regimes leads to different solutions on the direction of causality.

Climate change threatens to undermine the past 50 years of gains in public health. In response, the National Health Service (NHS) in England has been working since 2008 to quantify and reduce its carbon footprint. This Article presents the latest update to its greenhouse gas accounting, identifying interventions for mitigation efforts and describing an approach applicable to other health systems across the world.A hybrid model was used to quantify emissions within Scopes 1, 2, and 3 of the Greenhouse Gas Protocol, as well as patient and visitor travel emissions, from 1990 to 2019. This approach complements the broad coverage of top-down economic modelling with the high accuracy of bottom-up data wherever available. Available data were backcasted or forecasted to cover all years. To enable the identification of measures to reduce carbon emissions, results were disaggregated by organisation type.In 2019, the health service's emissions totalled 25 megatonnes of carbon dioxide equivalent, a reduction of 26% since 1990, and a decrease of 64% in the emissions per inpatient finished admission episode. Of the 2019 footprint, 62% came from the supply chain, 24% from the direct delivery of care, 10% from staff commute and patient and visitor travel, and 4% from private health and care services commissioned by the NHS.This work represents the longest and most comprehensive accounting of national health-care emissions globally, and underscores the importance of incorporating bottom-up data to improve the accuracy of top-down modelling and enabling detailed monitoring of progress as health systems act to reduce emissions.Wellcome Trust.