Latvia Electricity Consumption data was reported at 622.000 GWh in Mar 2025. This records an increase from the previous number of 621.000 GWh for Feb 2025. Latvia Electricity Consumption data is updated monthly, averaging 595.000 GWh from Jan 2006 (Median) to Mar 2025, with 231 observations. The data reached an all-time high of 776.000 GWh in Jan 2008 and a record low of 481.000 GWh in Jun 2013. Latvia Electricity Consumption data remains active status in CEIC and is reported by Central Statistical Bureau of Latvia. The data is categorized under Global Database’s Latvia – Table LV.RB002: Electricity Statistics.

This statistic shows the consumption of pickles in the United States from 2011 to 2020 and a forecast thereof until 2024. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ****** million Americans consumed pickles in 2020. This figure is projected to increase to ****** million in 2024.

Sweden Energy Consumption: Annual data was reported at 568.993 TWh in 2016. This records an increase from the previous number of 540.281 TWh for 2015. Sweden Energy Consumption: Annual data is updated yearly, averaging 558.736 TWh from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 618.819 TWh in 2004 and a record low of 421.300 TWh in 1971. Sweden Energy Consumption: Annual data remains active status in CEIC and is reported by Swedish Energy Agency. The data is categorized under Global Database’s Sweden – Table SE.RB001: Energy Statistics.

This dataset provides annual estimates developed by the U.S. Bureau of Economic Analysis on consumer spending in the State of Iowa beginning in 1998. Personal consumption expenditures (PCE) is the value of the goods and services purchased by, or on the behalf of, Iowa residents. PCE is reported in millions of current dollars. Also provided is per capita PCE which is reported in current dollars. The Census Bureau’s annual midyear (July 1) population estimates are used for per capita variables.

Consumption category indicates the goods or services associated with personal consumption. All includes both goods and services.

Goods include both durable goods and non durable goods. Durable goods include: motor vehicles and parts, furnishings and durable household equipment, recreational goods and vehicles, and other durable goods. Non durable goods include: food and beverages purchased for off-premises consumption, clothing and footwear, gasoline and other energy goods, and other non durable goods.

Services include household consumption expenditures (for services) and final consumption expenditures of nonprofit institutions serving households (NPISHs). Household consumption expenditures include: housing and utilities, health care, transportation services, recreation services, food services and accommodations, financial services and insurance, and other services. NPISH is the gross output of nonprofit institutions less receipts from sales of goods and services by nonprofit institutions.

This statistic shows the consumption of nutritional snacks in the United States from 2015 to 2020 and a forecast thereof until 2024. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ****** million Americans consumed nutritional snacks in 2020. This figure is projected to decrease to ****** million in 2024.

Indonesia Energy: Consumption: Households and Other Cosumers: Other Consumers: Briquettes and Cokes data was reported at 92.000 TJ in 2009. This records an increase from the previous number of 87.000 TJ for 2008. Indonesia Energy: Consumption: Households and Other Cosumers: Other Consumers: Briquettes and Cokes data is updated yearly, averaging 84.000 TJ from Dec 2006 (Median) to 2009, with 4 observations. The data reached an all-time high of 92.000 TJ in 2009 and a record low of 51.000 TJ in 2006. Indonesia Energy: Consumption: Households and Other Cosumers: Other Consumers: Briquettes and Cokes data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA004: Energy Statistics: Consumption.

Indonesia Water Statistic: Consumption data was reported at 14,253,691.000 IDR mn in 2017. This records an increase from the previous number of 12,854,363.000 IDR mn for 2015. Indonesia Water Statistic: Consumption data is updated yearly, averaging 8,250,734.000 IDR mn from Dec 2003 (Median) to 2017, with 14 observations. The data reached an all-time high of 14,253,691.000 IDR mn in 2017 and a record low of 4,240,740.000 IDR mn in 2003. Indonesia Water Statistic: Consumption data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RIG002: Water Consumption.

Indonesia Energy: Consumption: Transportation: Electricity data was reported at 865.000 TJ in 2017. This records an increase from the previous number of 727.000 TJ for 2016. Indonesia Energy: Consumption: Transportation: Electricity data is updated yearly, averaging 527.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 925.000 TJ in 2015 and a record low of 277.000 TJ in 2006. Indonesia Energy: Consumption: Transportation: Electricity data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA004: Energy Statistics: Consumption.

Semiannual statistics of consumption and expenditure in Spanish households carried out by the CNMC generated through surveys carried out on a sample of users. Statistics focus on the following areas: * Audiovisual sector statistics * Fuel statistics * Electricity and gas statistics * Satisfaction statistics for e-invoicing services * Postal sector statistics * Telecommunications statistics * Statistics of Internet usage and OTT services All these statistics are updated every six months.

Price quote data (for locally collected data only) and consumption segment indices that underpin consumer price inflation statistics, giving users access to the detailed data that are used in the construction of the UK’s inflation figures. The data are being made available for research purposes only and are not an accredited official statistic. From October 2024, private school fees and part-time education classes have been included in the consumption segment indices file. For more information on the introduction of consumption segments, please see the Consumer Prices Indices Technical Manual, 2019. Note that this dataset was previously called the consumer price inflation item indices and price quotes dataset.

Indonesia Consumption: Statistical Differences: LPG and RG data was reported at 163.000 TJ in 2017. This records a decrease from the previous number of 6,209.000 TJ for 2016. Indonesia Consumption: Statistical Differences: LPG and RG data is updated yearly, averaging 4,333.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 114,204.000 TJ in 2014 and a record low of -75,697.000 TJ in 2015. Indonesia Consumption: Statistical Differences: LPG and RG data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.

1. Consumer expenditure and consumption data of non-profit organizations of 12 categories released by season.2. Purpose of collection: To demonstrate the data of private consumption by consumption purpose each season.3. Data collection method: Mainly based on surveys and statistical data of people's travel situation, education expenditure, household income and expenditure survey, wholesale, retail, and catering industry turnover.

Abstract

Most countries collect official statistics on energy use due to its vital role in the infrastructure, economy and living standards.

In Palestine, additional attention is warranted for energy statistics due to a scarcity of natural resources, the high cost of energy and high population density. These factors demand comprehensive and high quality statistics.

In this contest PCBS decided to conduct a special Energy Consumption in Transport Survey to provide high quality data about energy consumption by type, expenditure on maintenance and insurance for vehicles, and questions on vehicles motor capacity and year of production.

The survey aimed to provide data on energy consumption by transport sector and also on the energy consumption by the type of vehicles and its motor capacity and year of production.

Geographic coverage

Palestine

Analysis unit

Vehicles

Universe

All the operating vehicles in Palestine in 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Target Population: All the operating vehicles in Palestine in 2014.

2.1Sample Frame A list of the number of the operating vehicles in Palestine in 2014, they are broken down by governorates and vehicle types, this list was obtained from Ministry of transport.

2.2.1 Sample size The sample size is 6,974 vehicles.

2.2.2 Sampling Design it is stratified random sample, and in some of the small size strata the quota sample was used to cover them.

The method of reaching the vehicles sample was through : 1-reaching to all the dynamometers (the centers for testing the vehicles) 2-selecting a random sample of vehicles by type of vehicle, model, fuel type and engine capacity

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire was based on the experiences of other similar countries in energy statistics subject to cover the most important indicators for energy statistics in transport sector, taking into account Palestine's particular situation.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: The survey questionnaire was uploaded on office computers. At this stage, data were entered into the computer using a data entry template developed in Access Database. The data entry program was prepared to satisfy a number of requirements: ·To prevent the duplication of questionnaires during data entry. ·To apply checks on the integrity and consistency of entered data. ·To handle errors in a user friendly manner. ·The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS. Audit after data entered at this stage is data entered scrutiny by pulling the data entered file periodically and review the data and examination of abnormal values and check consistency between the different questions in the questionnaire, and if there are any errors in the data entered to be the withdrawal of the questionnaire and make sure this data and adjusted, even been getting the final data file that is the final extract data from it. Extraction Results: The extract final results of the report by using the SPSS program, and then display the results through tables to Excel format.

Response rate

80.7%

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are anticipated in comparison with the real values obtained through censuses. The variance was calculated for the most important indicators: the variance table is attached with the final report. There is no problem in the dissemination of results at national and regional level (North, Middle, South of West Bank, Gaza Strip).

Data appraisal

The survey sample consisted of around 6,974 vehicles, of which 5,631 vehicles completed the questionnaire, 3,652 vehicles from the West Bank and 1,979 vehicles in Gaza Strip.

Quarterly household final consumption expenditure components in chained (2017) and current dollars.

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

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