The United Kingdom’s electricity use has been declining since peaking at 357 terawatt-hours in 2005. In 2023, the UK's electricity consumption fell to its lowest level this century, at 266 terawatt-hours. Electricity consumption in the UK typically follows a seasonal trend, peaking in the winter months. How electricity-intensive is the UK? Despite the continual decline in electricity consumption, the UK remains one of the largest electricity consumers in the world. In terms of per capita electricity consumption, however, the UK pales in comparison to other European countries such as Norway, Germany, and France. In 2022, it registered an average of 4,813 kilowatt-hours per person. The race towards a clean power mix In 2010, gas and coal accounted for roughly 75 percent of the UK's power mix. Since then, alongside the EU Renewables Directive, the UK agreed and created its own National Renewable Energy Plan, to increase the use of renewable sources and decrease its fossil fuel dependence. In the past decade, the share of energy consumption in the UK attributable to renewable energy increased slightly, although it was still a small percentage out of the total in 2022.

Peer-to-peer (P2P) energy sharing involves novel technologies and business models at the demand-side of power systems, which is able to manage the increasing connection of distributed energy resources (DERs). In P2P energy sharing, prosumers directly trade energy with each other to achieve a win-win outcome. A research paper titled "Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework" has been published on Applied Energy regarding this topic. In the paper, a general multiagent framework was established to simulate P2P energy sharing, with two original techniques proposed to facilitate simulation convergence. Furthermore, a systematic index system was established to evaluate P2P energy sharing mechanisms from both economic and technical perspectives.In case studies of the paper, two sets of cases were conducted to validate the proposed simulation and evaluation methods and to give some practical implications on applying P2P energy sharing in Great Britain (GB) at present and in the future. The household demand dataset and electric vehicle (EV) dataset used in the paper has been provided for researchers to reproduce the results in the paper or to conduct further related studies. Also, the original numerical data of the results in the case studies of the paper have been provided, for researchers to better understand the results or to use the results for other purposes.The whole dataset includes 9 excel files in total. The detailed description for them are presented as follows:1. “CREST_Demand_Model_v2.2 (Great Britain).xlsm” is a high-resolution stochastic integrated thermal-electrical domestic demand simulation tool developed by Centre for Renewable Energy Systems Technology (CREST) of Loughborough University (refering to http://www.lboro.ac.uk/research/crest/demand-model/). It contains a lot of sheets and VBA codes, which are used to generate “fake” demand curves of domestic customers sampled from statistical distributions that are based on real-life data. In the “Main Sheet”, input parameters like “day of month”, “month of year”, “latitude”, “longitude”, etc. can be entered, and then the “Run simulation” button can be clicked to start the simulation. After the simulation, daily curves like “occupancy and activity”, “total electrical demand”, “total gas demand”, etc. are generated and visualized, with very high time resolution.2. “Electric_Vehicle_Dataset (Great Britain).xlsx” is a dataset based on the research conducted jointly by Centre for Integrated Renewable Energy of Cardiff University and Key Laboratory of Smart Grid of Ministry of Education of Tianjin University (referring to https://doi.org/10.1016/j.apenergy.2015.10.159). It contains two sheets, which provide the parameters of 1000 typical electric vehicles of Great Britain respectively. For each electric vehicle, the parameters include: (1) “Time starting charging / returning home (hour)”, (2) “Time finishing charging / leaving home (hour)”, (3) “Battery capacity (kWh)”, (4) “Energy consumption due to travel (measured by SOC)”, (5) “Lowerlimit of SOC”, (6) “Upperlimit of SOC”, (7) “Maximum charging/discharging power”, (8) “Charging efficiency”, and (9) “Discharging efficiency”.3. “Numerical results and figures _ Case 1-1.xlsx” provides the numerical results of Case 1-1 of the paper. It contains three sheets, providing the data behind Fig. 6, Fig. 7 and Fig. 8 of the paper respectively. In the “Fig. 6” sheet, the “Total Net Consumption (kWh)” and “Total PV Generation (kWh)” under “SDR mechanism” and “conventional paradigm” are provided. In the “Fig. 7” sheet, the “Net energy cost under SDR mechanism (£)” and “Net energy cost under conventional paradigm (£)” of each prosumer are provided. In the “Fig. 8” sheet, the “Internal selling price (£/MWh)”, “Internal buying price (£/MWh)” and “Total Net Energy Cost (£)” of each iteration are provided.4. “Numerical results and figures _ Case 1-2.xlsx” provides the numerical results of Case 1-2 of the paper. It contains two sheets, providing the data behind Fig. 9, Fig. 10 and Fig. 11 of the paper. In the “Fig. 9 and 10” sheet, for Fig. 9, the “The iteration at which the simulation stopped” given different ramping rates are provided; for Fig. 10, the “Overall Performance Index” with different ramping rates given different demand profiles are provided. In the “Fig. 11” sheet, the “Total net energy cost (ramping rate = 0.3) (£)” and “Total Net Energy Cost (ramping rate = 0.6) (£)” at each iteration are provided.5. “Numerical results and figures _ Case 1-3.xlsx” provides the numerical results of Case 1-3 of the paper. It contains only one sheets, providing the data behind Fig. 12 of the paper. In the “Fig. 12” sheet, the “Overall Performance Index” with different learning rates given different demand profiles are provided.6. “Numerical results and figures _ Case 1-4.xlsx” provides the numerical results of Case 1-4 of the paper. It contains two sheets, providing the data behind Fig. 13 and Fig. 14 of the paper. In the “Fig. 13” sheet, the “Overall Performance Index” with different ramping rates given different initial values are provided. In the “Fig. 14” sheet, the “Overall Performance Index” with different learning rates given different initial values are provided.7. “Numerical results and figures _ Case 1-5.xlsx” provides the numerical results of Case 1-5 of the paper. It contains only one sheet, providing the data behind Fig. 15 and Fig. 16 of the paper. In the “Fig. 15 and 16” sheet, for Fig. 15, the number of iterations when the simulation stopped given different maximum number of iterations and ramping rates are provided; for Fig. 16, the overall performance given different maximum number of iterations and ramping rates are provided.8. “Numerical results and figures _ Case 2-2.xlsx” provides the numerical results of Case 2-2 of the paper. It contains only one sheet, providing the data behind Fig. 17 of the paper. In the “Fig. 17” sheet, the overall performance scores of the three mechanisms (SDR, MMR and BS) and conventional paradigm in scenarios with different PV and EV penetration levels are provided.

1. “Numerical results and figures _ Appendix B.xlsx” provides the numerical results of the cases in Appendix B of the paper. It contains two sheets, providing the data behind Fig. B1, Fig. B2, Fig. B3 and Fig. B4 of the paper. In the “Fig. B1 and B2” sheet, for Fig. B1, the EWH power consumption (kW) at t=1 and t=2 for each iteration without any techniques for convergence are provided; for Fig. B2, the Internal buying price (pence/kWh) at t=1 and t=2 without any techniques for convergence are provided. In the “Fig. B3 and B4” sheet, for Fig. B1, the EWH power consumption (kW) at t=1 and t=2 for each iteration with a limitation for its power change are provided; for Fig. B2, the Internal buying price (pence/kWh) at t=1 and t=2 with a limitation for its power change are provided.Research results based upon these data are published at https://doi.org/10.1016/j.apenergy.2018.02.089

Daily data showing the System Price of electricity, and rolling seven-day average, in Great Britain. These are official statistics in development. Source: Elexon.

The GIS (Geographic Information System) vectorisation project will deliver the incremental digital conversion of our legacy geospatial network records. This dataset defines the sub-areas which will be incrementally delivered, detailing corresponding current status and planned completion dates. This allows users to understand the current and future coverage of digital geospatial network records as the project progresses.

Methodological Approach Progress against a defined project plan is captured and updated throughout the day. A script is run to convert into a shapefile. This shapefile is then uploaded onto the Open Data Portal.

Quality Control Statement

This dataset is provided "as is".

Assurance Statement The Open Data Team has checked outputs to validate.

Other Download dataset information: Metadata (JSON)Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

This dataset includes the actual daily reconciled levy rates and eligible demand volume (EII and GEE exempted) for each performed settlement run type. Energy Intensive Industries (EII) are those industries that are determined by BEIS as intensive users of electricity. They are exempted from paying CfD levy, up to 85% of Supplier's daily gross demand. Eligible power generated abroad and supplied to GB can be used to reduce a suppliers’ liability for the CfD levy. Green Excluded Electricity (GEE) means any amount of electricity determined as such. This dataset is updated following the end of each Quarterly Obligation Period.

Increasing penetration of renewable generation results in lower inertia of electric power systems. To maintain the system frequency, system operators have been designing innovative frequency response products. Enhanced Frequency Response (EFR) newly introduced in the UK is an example with higher technical requirements and customized specifications for assets with energy storage capability. We published a paper on IEEE Transactions on Industrial Informatics, proposing a method to estimate the EFR capacity of a population of industrial heating loads, bitumen tanks. A decentralized control scheme was also devised to enable them to deliver EFR. Case study was conducted using real UK frequency data and practical tank parameters. Results showed that bitumen tanks delivered high-quality service when providing service-1-type EFR, but underperformed for service-2-type EFR with much narrower deadband. Bitumen tanks performed well in both high and low frequency scenarios, and had better performance with significantly larger numbers of tanks or in months with higher power system inertia.The dataset regarding this paper includes 6 EXCEL files in total. The detailed description for them are presented as follows:1. “Evaluation results and figures _ Case 1.xlsx” provides the numerical results of Case 1 of the paper, showing the performance of bitumen tanks to provide EFR service in the base case settings (the day with the highest frequency in 2016, 16 Nov 2016, used for evaluation). It contains three sheets, providing the data behind Fig. 10, Fig. 11 and Fig. 12 of the paper respectively. In the “Fig. 10” sheet, the “Actual Response” is provided along with “Upper Limits of Delivery Envelope” and “Lower Limits of Delivery Envelopes”. In the “Fig. 11” sheet, the “SPM” (Service Performance Measure) and “AF” (Availability Factor) are provided for the settlement periods over the day. In the “Fig. 12” sheet, the “Actual Ramp Rate” is provided along with “Upper Limits of Ramp Rate” and “Lower Limits of Ramp Rate”.2. “Evaluation results and figures _ Case 2.xlsx” provides the numerical results of Case 2 of the paper, showing the results of time delay. It contains only one sheet, providing the data behind Fig. 13 of the paper. In the “Fig. 13” sheet, the “Correlation” is provided along with the corresponding “Lag (s)”.3. “Evaluation results and figures _ Case 3.xlsx” provides the numerical results of Case 3 of the paper, showing the results given different types of EFR service. It contains two sheets, providing the data behind Fig. 14 of the paper. In the “Fig. 14(a)” sheet, the “SPM” (Service Performance Measure) for the settlement periods over the day is provided for both types of EFR. In the “Fig. 14(b)” sheet, the “AF” (Availability Factor) for the settlement periods over the day is provided for both types of EFR.4. “Evaluation results and figures _ Case 5.xlsx” provides the numerical results of Case 5 of the paper, showing the results with various population sizes of bitumen tanks. It contains two sheets, providing the data behind Fig. 15 of the paper. In the “Fig. 15(a)” sheet, the “Average SPM” (Service Performance Measure) for the settlement periods over the day is provided for various population sizes. In the “Fig. 15(b)” sheet, the “AF” (Availability Factor) for the settlement periods over the day is provided for various population sizes.5. “Evaluation results and figures _ Case 6.xlsx” provides the numerical results of Case 6 of the paper, showing the month-level evaluation results. It contains five sheets, providing the data behind Fig. 16 through Fig. 20 of the paper. In the “Fig. 16” sheet, the “SPM” (Service Performance Measure) and “AF” (Availability Factor) for the settlement periods over the whole January is provided for service-1-type EFR. In the “Fig. 17” sheet, the “SPM” (Service Performance Measure) and “AF” (Availability Factor) for the settlement periods over the whole January is provided for service-2-type EFR. In the “Fig. 18” sheet, the “SPM” (Service Performance Measure) and “AF” (Availability Factor) for the settlement periods over the whole July is provided for service-1-type EFR. In the “Fig. 19” sheet, the “SPM” (Service Performance Measure) and “AF” (Availability Factor) for the settlement periods over the whole July is provided for service-2-type EFR. In the “Fig. 20” sheet, the “SPM” (Service Performance Measure) and “AF” (Availability Factor) for the settlement periods over the whole July is provided for various population sizes.6. “Baseline Estimation _ Fig 5.xlsx” provides the numerical data behind Fig. 5 of the paper, which is about the baseline load calculation for the tank population (200 tanks) over one-month basis. It contains only one sheet, in which the “Aggregated Load (MW)” of the tanks is provided for the whole month on a second-by-second basis, and the “Baseline load (MW)” estimated for the month is provided.Research results based upon these data are published at http://doi.org/10.1109/TII.2018.2879907

A comparative table of weekly UK wholesale market prices across key energy commodities, including gas, electricity, coal, EUA carbon, UKA carbon, and Brent crude oil. The table includes current, previous, and year-on-year values for both day-ahead and year-ahead contracts, as well as 12-month highs and lows.

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

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The EEG data were recorded from 20 human volunteers (10 females) while they were performing a sequential neurofeedback-behaviour task, with the neurofeedback reflecting the occurrence of beta bursts over sensorimotor cortex (C3 or C4) quantified in real time.

Each participant was recorded three times over three different days. On each recording day, the participant performed the neurofeedback training task with each hemisphere using the EEG signals recorded from C3 or C4 (in a random order), and the contralateral hand for the motor task. Participants completed four experimental runs for each hemisphere on each training day. Each experimental run consisted of a 30 s of rest recording for calibration, 10 continuous trials in the training condition, and another 10 continuous trials in the no training condition. The order of training and no training blocks in each experimental run was randomized. In total, we recorded data from 20 hemispheres with 120 trials in each of the 'training' and 'no training' conditions for each hemisphere for each of the real feedback and sham feedback groups.

The details of the experimental design and behavioural task are described in He et al (2020).

The data files are in MATLAB format. The dataset consists of 120 raw data files (20 subject × 3 days × 2 hemispheres). Due to the size of this dataset (38 GB), it has been split into segments. However, you may still have trouble downloading it, in which case please contact ben.micklem@bndu.ox.ac.uk.

Task

The participants were pseudo-randomly assigned to a sham feedback group or a real feedback group, with ten participants in each group. The neurofeedback training composed of multiple short trials. Each trial consisted of a 2 s period where the participants were instructed to get ready and a 4 s neurofeedback phase, which was followed by black screen presented for a time randomly drawn between 2 and 3 s and then a movement go-cue. The participants were instructed to perform a thumb of finger pinch movement as fast as possible in response to the go-cue to generate a force overshooting a predefined force level (50% of the maximum voluntary force measured before starting the task).

Instructions given to the participants were the same for both groups. In the training trials, the participants were instructed to keep the basketball floating at the top of the screen, which would require them to suppress the beta bursts. In the no training trials, the participants were instructed to simply pay attention to the movement of the ball displayed on the screen and get ready for the subsequent movement go-cue.

Each participant was recorded three times over three different days. On each recording day, the participant performed the neurofeedback training task with each hemisphere using the EEG signals recorded from C3 or C4 (in a random order), and the contralateral hand for the motor task. Participants completed four experimental runs for each hemisphere on each training day. Each experimental run consisted of a 30 s of rest recording to calibrate the threshold for triggering the vertical movement of the basketball, 10 continuous trials in the training condition, and another 10 continuous trials in the no training condition. The order of training and no training blocks in each experimental run was randomized. In total, we recorded data from 20 hemispheres with 120 trials in each of the training and no training conditions for each hemisphere for each of the real feedback and sham feedback groups.

Group information

Real feedback group: Subject - 1 4 6 7 10 11 13 15 17 20 Sham feedback group: Subject - 2 3 5 8 9 12 14 16 18 19

Data description

For each subject (e.g., Sub1), there are three subfolders (i.e., Day1, Day2, Day3) with two files (i.e., Raw_C3.mat and Raw_C4.mat) in each subfolder. The data are in Matlab format. In each .mat file, there are five fields indicate five data streams recorded using OpenVibe. The name of each data stream can be found in rawData.info.name.

The data stream 'openvibeSignal' contains the 32 channels raw data, including: *** 24 monopolar EEG, channel 1-24: FP1, FP2, Fz, FCz, Cz, CPz, Pz, Oz, FC1, C1, CP1, FC3, C3, CP3, FC2, C2, CP2, FC4, C4, CP4, P3, P4, O1, O2. *** 2 bipolar EMG from the flexor carpi radialis of both arms, channel 25-26: EMGL, EMGR. *** 2 accelerometer measurements for both hands recorded from z-axis: Aclz, Acrz. *** 2 pinch force: FrcL, FrcR. Note that these channel information were not included in the structure, but they were the same as indicated above for all recordings.

The data stream 'TriggerStream' contains the trigger information during the experiment. Here below are some keys triggers used to segment the trials: *** 100 or 101: The start of a block, with 100 and 101 indicating no-training and training conditions, respectively. *** 1-10: The onset of the basketball movement in trial 1-10. Note that there were 10 trials in each block. *** -2: Trigger for the participants to get ready before the basketball movement. *** 14: Tigger for the pinch task.

The data stream 'BallMove' contains the recorded positions of the basketball for each individual trial, which indicated the neurofeedback training performance.

The other two streams including 'openvibeMarkers' and 'Matlab' could be ignored. In each data stream, the variable 'time_series' indicated the raw data and 'time_stamps' indicated the time stamps for each sample (column) in 'time_series'.

The following script was used to match the time stamps between 'openvibeSignal' and other two streams:

rawData{1,1}.time_stamps = rawData{1,1}.time_stamps+str2double(rawData{1,1}.info.created_at) Here we assume rawData{1,1} was the 'openvibeSignal' data stream.

The sampling frequency was 2048 Hz.

The peak frequencies with maximum movement-related power reduction was: BetaC3 = [15 19 15 20 18 17 22 19 18 16 20 22 20 18 24 19 20 22 23 19]; BetaC4 = [15 19 15 18 16 21 18 19 17 18 19 20 22 24 19 18 21 20 23 18]; For each hemisphere, a 5-Hz frequency band around the peak frequency was used for the neurofeedback training.

This dataset describes hourly time series of discharge and suspended sediment flux at four sites in the Vietnamese Mekong Delta (Chau Doc, Tan Chau, Can Tho and My Thaun) for the period 2005 – 2015. This data was calculated from historic Acoustic Doppler Current Profiler (aDcp)data obtained as part of routine flood monitoring conducted by the Vietnamese Hydrological Agency. The data were collated by the authors. The data were processed to back out sediment fluxes through the delta through calibration of the acoustic backscatter signal to suspended sediment concentrations collected in Chau Doc (May 2017) and Can Tho (September 2017). For each aDcp instrument acoustic backscatter signal was calibrated to observed suspended sediment concentrations (SSCs). These concentrations values were then matched to measured acoustic backscatter values (dB) from the depth at which each sample was taken to generate power law calibration curves. To generate daily fluxes, the point specific ADCP fluxes were used to generate sediment ratings curves between sediment flux (kg/s) and discharge (m3/s). These ratings curves were then propagated over recorded daily discharge values measured by the Vietnamese hydrological agency to provide daily fluxes over the period of record. The work was funded through NERC grant reference NE/P008100/1 - Deciphering the dominant drivers of contemporary relative sea-level change: Analysing sediment deposition and subsidence in a vulnerable mega-delta.

Airbnb has a total of 6,132 employees that work for the company. 52.5% of Airbnb workers are male and 47.5% are female.

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

The number of Pinterest users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+3.14 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 9.88 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

In the century between Napoleon's defeat and the outbreak of the First World War (known as the "Pax Britannica"), the British Empire grew to become the largest and most powerful empire in the world. At its peak in the 1910s and 1920s, it encompassed almost one quarter of both the world's population and its land surface, and was known as "the empire on which the sun never sets". The empire's influence could be felt across the globe, as Britain could use its position to affect trade and economies in all areas of the world, including many regions that were not part of the formal empire (for example, Britain was able to affect trading policy in China for over a century, due to its control of Hong Kong and the neighboring colonies of India and Burma). Some historians argue that because of its economic, military, political and cultural influence, nineteenth century Britain was the closest thing to a hegemonic superpower that the world ever had, and possibly ever will have. "Rule Britannia" Due to the technological and logistical restrictions of the past, we will never know the exact borders of the British Empire each year, nor the full extent of its power. However, by using historical sources in conjunction with modern political borders, we can gain new perspectives and insights on just how large and influential the British Empire actually was. If we transpose a map of all former British colonies, dominions, mandates, protectorates and territories, as well as secure territories of the East India Trading Company (EIC) (who acted as the precursor to the British Empire) onto a current map of the world, we can see that Britain had a significant presence in at least 94 present-day countries (approximately 48 percent). This included large territories such as Australia, the Indian subcontinent, most of North America and roughly one third of the African continent, as well as a strategic network of small enclaves (such as Gibraltar and Hong Kong) and islands around the globe that helped Britain to maintain and protect its trade routes. The sun sets... Although the data in this graph does not show the annual population or size of the British Empire, it does give some context to how Britain has impacted and controlled the development of the world over the past four centuries. From 1600 until 1920, Britain's Empire expanded from a small colony in Newfoundland, a failing conquest in Ireland, and early ventures by the EIC in India, to Britain having some level of formal control in almost half of all present-day countries. The English language is an official language in all inhabited continents, its political and bureaucratic systems are used all over the globe, and empirical expansion helped Christianity to become the most practiced major religion worldwide. In the second half of the twentieth century, imperial and colonial empires were eventually replaced by global enterprises. The United States and Soviet Union emerged from the Second World War as the new global superpowers, and the independence movements in longstanding colonies, particularly Britain, France and Portugal, gradually succeeded. The British Empire finally ended in 1997 when it seceded control of Hong Kong to China, after more than 150 years in charge. Today, the United Kingdom consists of four constituent countries, and it is responsible for three crown dependencies and fourteen overseas territories, although the legacy of the British Empire can still be seen, and it's impact will be felt for centuries to come.

The 2025 annual OPEC oil price stood at ***** U.S. dollars per barrel, as of April. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.