Monthly average retail prices for gasoline and fuel oil for Canada, selected provincial cities, Whitehorse and Yellowknife. Prices are presented for the current month and previous four months. Includes fuel type and the price in cents per litre.

Gasoline retail prices weekly average by region dataset provides the weekly average retail gasoline prices for New York State and sixteen New York metropolitan regions in U.S. dollars per gallon. Data is a weekly average from January 2017 through current. Average daily retail gasoline prices are collected from the American Automobile Association (AAA) Daily Fuel Gauge Report. The AAA Daily Fuel Gauge Report prices are averaged to produce a weekly average retail price for New York State and each metropolitan region. The New York State metropolitan regions in the dataset are Albany (Albany-Schenectady-Troy), Batavia, Binghamton, Buffalo (Buffalo-Niagara Falls), Dutchess (Dutchess-Putnam), Elmira, Glens Falls, Ithaca, Kingston, Nassau (Nassau-Suffolk), New York City, Rochester, Syracuse, Utica (Utica-Rome), Watertown (Watertown-Fort Drum), and White Plains. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Gasoline Prices in the United Kingdom decreased to 1.81 USD/Liter in July from 1.84 USD/Liter in June of 2025. This dataset provides the latest reported value for - United Kingdom Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Gasoline Prices in Vietnam decreased to 0.73 USD/Liter in July from 0.80 USD/Liter in June of 2025. This dataset provides the latest reported value for - Vietnam Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Gasoline Prices in Germany decreased to 2.02 USD/Liter in July from 2.06 USD/Liter in June of 2025. This dataset provides the latest reported value for - Germany Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Gasoline Prices in Mexico remained unchanged at 1.25 USD/Liter in July. This dataset provides the latest reported value for - Mexico Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Gasoline Prices in Puerto Rico increased to 0.75 USD/Liter in July from 0.74 USD/Liter in June of 2025. This dataset provides the latest reported value for - Puerto Rico Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset showing how much fuel each council vehicle has consumed, by year and by fuel type.

Dataset guidance

The following information refers to the columns in the data:

• Fleet = fleet number (specific number used to identify a vehicle)
• Vehicle = vehicle type
• Fuel = fuel used in litres
• Dist.Run = Total mileage for the reporting period
• MPG = Miles Per Gallon
• Type = D: Diesel, G: Gas Oil

Due to problems and changes with the fuel system software this data is not available for 2017/18. Data for 2018/19 will be published.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gas City. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gas City. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2021

In terms of income distribution across age cohorts, in Gas City, householders within the 45 to 64 years age group have the highest median household income at $71,450, followed by those in the 25 to 44 years age group with an income of $65,080. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $41,918. Notably, householders within the under 25 years age group, had the lowest median household income at $31,076.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gas City median household income by age. You can refer the same here

This project represents the data used in “Influences of potential oil and gas development and future climate on sage-grouse declines and redistribution.” The data sets describe greater sage-grouse (Centrocercus urophasianus) population change, summarized in different boundaries within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Population changes were based on different scenarios of oil and gas development intensities, projected climate models, and initial sage-grouse population estimates. Description of data sets pertaining to this project: Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with and without effects of climate change. 1. Greater sage-grouse population change (percent change) over 50-years in a high oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 2. Greater sage-grouse population change (percent change) in a low oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 3. Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 4. Greater sage-grouse population change (percent change) in a moderate oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 5. Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

Nigeria Average Fuel Price: Petrol data was reported at 145.701 l in Jan 2019. This records a decrease from the previous number of 145.777 l for Dec 2018. Nigeria Average Fuel Price: Petrol data is updated monthly, averaging 147.773 l from Jan 2017 (Median) to Jan 2019, with 24 observations. The data reached an all-time high of 190.875 l in Jan 2018 and a record low of 109.600 l in Jan 2017. Nigeria Average Fuel Price: Petrol data remains active status in CEIC and is reported by National Bureau of Statistics of the Federal Republic of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.P003: Average Fuel Price.

Gasoline Prices in Qatar remained unchanged at 0.52 USD/Liter in July. This dataset provides - Qatar Gasoline Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Natural gas processing plants in the U.S as of 12/31/2017. Data based on 2017 annual avarages. Source: EIA-757, Natural Gas Processing Plant Survey.

Gasoline Prices in Canada remained unchanged at 1.08 USD/Liter in June. This dataset provides the latest reported value for - Canada Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This is an updated version of Gütschow et al. (2019, http://doi.org/10.5880/pik.2019.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its previous updates. For a detailed description of the changes please consult the CHANGELOG included in the data description document. The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Agriculture is available. Version 2.1 of the PRIMAP-hist dataset does not include emissions from Land use, land use change and forestry (LULUCF). List of datasets included in this data publication:(1) PRIMAP-hist_v2.1_09-Nov-2019.csv: With numerical extrapolation of all time series to 2017. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.1_09-Nov-2019.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.1_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.1_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder) When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources. SOURCES:- Global CO2 emissions from cement production v4: Andrew (2019)- BP Statistical Review of World Energy: BP (2019)- CDIAC: Boden et al. (2017)- EDGAR version 4.3.2: JRC and PBL (2017), Janssens-Maenhout et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2019)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2019)- UNFCCC Biennal Update Reports: UNFCCC (2019)- UNFCCC Common Reporting Format (CRF): UNFCCC (2018), UNFCCC (2019), Jeffery et al. (2018) Full references are available in the data description document.

Gasoline Prices in Philippines decreased to 1.04 USD/Liter in July from 1.06 USD/Liter in June of 2025. This dataset provides the latest reported value for - Philippines Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

ORACLES_TraceGas_AircraftInSitu_Data are in situ trace gas measurements collected onboard the P-3 Orion or ER-2 aircraft during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 – October 27, 2016, August 1, 2017 – September 4, 2017 and September 21, 2018 – October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.Southern Africa produces almost one-third of the Earth’s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 – October 27, 2016; August 1, 2017 – September 4, 2017; September 21, 2018 – October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.