Gasoline rose to 2.19 USD/Gal on July 11, 2025, up 1.65% from the previous day. Over the past month, Gasoline's price has risen 1.03%, but it is still 12.72% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on July of 2025.

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.

Iowa motor fuel retailers are businesses that offer a range of fuel options, including gasoline, diesel, ethanol, and biodiesel. Iowa Code section 452A.33 requires all Iowa fuel retailers to report motor fuel and diesel gallons to the Iowa Department of Revenue and for the Department to prepare and submit an annual report of fuel gallons to the Iowa Governor and Legislature. The full annual reports related to this dataset are published on the Iowa Department of Revenue web page: https://tax.iowa.gov/report-category/retailers-annual-gallons. Over the years, the Retailers Fuel Gallons Annual Report has been updated to reflect changes in law and trends in biofuel sales. Reported data available by year reflects these changes with the pure biofuel sales category added to the dataset in 2018. Counties with five or less locations are reported in aggregate as “other” between 2011 and 2020 due to confidentiality requirements. Starting in 2021, these counties are reported individually, but categories within the dataset that would violate confidentiality requirements are left blank. The State of Iowa set a goal to replace 25.0 percent of petroleum in Iowa with biofuel by 2020. The biofuel distribution percentage measures how the State is doing toward meeting that goal. The formula for determining the biofuel distribution percentage is as follows: Biofuel Distribution Percentage = (Pure Ethanol Gallons + Pure Biodiesel Gallons) / Total Gasoline Gallons.

The Undiscovered Oil and Gas Resources dataset shows the locations of the Assessment Units (AUs) in the U.S. Geological Survey (USGS) World Assessment of Undiscovered Oil and Gas Resources. The assessment was conducted in 2012 and evaluated 313 AUs within 171 geologic provinces (areas where oil and gas occur in commercial quantities). An AU is a mappable volume of rock with homogenous geologic properties. Each AU was assessed for undiscovered oil and gas resources using data from published literature. In each geologic province, total petroleum systems (TPS) were also defined. A TPS is the group of geologic elements needed for oil and gas formation. The Undiscovered Oil and Gas Resources dataset provides an understanding of the quantity, quality and distribution of global conventional oil and gas resources. Conventional oil and gas resources, such as crude oil and natural gas, are found in high porosity/permeability reservoirs. Unconventional oil and gas resources are found in low porosity/permeability reservoirs, such as shale and tar sands. Conventional resources are relatively easy to extract from the earth and do not require fracking. The USGS World Assessment of Undiscovered Oil and Gas Resources determined that a total of 565,298 million barrels of oil, 5,605,626 billion cubic feet of gas and 166,668 million barrels of natural gas liquids remained undiscovered as of 2011. All of these resources are conventionally extractable. However, conventional oil and gas resources are dwindling. Knowing where and how much conventional oil and gas remains undiscovered is important for understanding the world’s energy future. The Undiscovered Oil and Gas Resources dataset is a geologic basis for making decisions about energy. It can help predict future energy production trends and increase understanding of the social and environmental consequences of oil and gas resource exploitation.

Saudi Arabia Fuel Prices: Retail: Gasoline 91 data was reported at 2.180 SAR/l in Apr 2025. This stayed constant from the previous number of 2.180 SAR/l for Mar 2025. Saudi Arabia Fuel Prices: Retail: Gasoline 91 data is updated monthly, averaging 2.180 SAR/l from Jul 2020 (Median) to Apr 2025, with 58 observations. The data reached an all-time high of 2.180 SAR/l in Apr 2025 and a record low of 1.290 SAR/l in Jul 2020. Saudi Arabia Fuel Prices: Retail: Gasoline 91 data remains active status in CEIC and is reported by Saudi Arabian Oil Company. The data is categorized under Global Database’s Saudi Arabia – Table SA.P016: Fuel Prices. [COVID-19-IMPACT]

Gasoline Prices in Norway increased to 1.95 USD/Liter in May from 1.90 USD/Liter in April of 2025. This dataset provides the latest reported value for - Norway Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset illustrates the median household income in Gas, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Gas decreased by $7,548 (10.64%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 6 years and declined for 5 years.

https://i.neilsberg.com/ch/gas-ks-median-household-income-trend.jpeg" alt="Gas, KS median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

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.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

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 median household income. You can refer the same here

Information is needed to mitigate dryland soil greenhouse gas (GHG) emissions by using novel management practices. We evaluated the effects of cropping sequence and N fertilization on dryland soil temperature and water content at the 0- to 15-cm depth and surface CO2, N2O, and CH4 fluxes in a Williams loam in eastern Montana. Treatments were no-tilled continuous malt barley (Hordeum vulgaris L.) (NTCB), no-tilled malt barley-pea (Pisum sativum L.) (NTB-P), and conventional-tilled malt barley-fallow (CTB-F) (control), each with 0 and 80 kg N ha-1. Gas fluxes were measured at 3 to 14 d intervals using static, vented chambers from March to November, 2008 to 2011. Soil temperature varied but water content was greater in CTB-F than in other treatments. The GHG fluxes varied with date of sampling, peaking immediately after substantial precipitation (>15 mm) and N fertilization during increased soil temperature. Total CO2 flux from March to November was greater in NTCB and NTB-P with 80 kg N ha-1 than in other treatments from 2008 to 2010. Total N2O flux was greater in NTCB with 0 kg N ha-1 and in NTB-P with 80 kg N ha-1 than in other treatments in 2008 and 2011. Total CH4 uptake was greater with 80 than with 0 kg N ha-1 in NTCB in 2009 and 2011. Because of intermediate level of CO2 equivalent of GHG emissions and known favorable effect on malt barley yield, NTB-P with 0 kg N ha-1 might mitigate GHG emissions and sustain crop yields compared to other treatments in eastern Montana. For accounting global warming potential of management practices, however, additional information on soil C dynamics and CO2 associated with production inputs and machinery use are needed. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/60e0612c-8144-46fc-a41a-07fc83b4ad83

Gasoline Prices in Ireland decreased to 1.99 USD/Liter in June from 2.04 USD/Liter in May of 2025. This dataset provides the latest reported value for - Ireland Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Most communities in the western and interior parts of the state rely primarily on electricity generated with diesel fuel. These communities had the most expensive electricity in 2011. Most remote rural communities are eligible for the Power Cost Equalization (PCE) program instituted by the state to offset the high fuel prices in these communities. The program pays 95% of residential electricity cost However, the program has not been fully funded by the Legislature in 15 out of its 25 years of existence, and electricity rates in rural Alaska with PCE are still higher than in urban Alaska. There has been a recent dramatic increase in fuel prices throughout Alaska. This dataset shows the change in price of a gallon of diesel over the 1991-2000 and 2000-2010 decades. Prices are inflation adjusted 2013 US dollars.

The CAIT Country GHG emissions collection applies a consistent methodology to create a six-gas, multi-sector, and internationally comparable data set for 186 countries.

Cait enables data analysis by allowing users to quickly narrow down by year, gas, country/state, and sector. Automatic calculations for percent changes from prior year, per capita, and per GDP are also available. Users are presented with clear and customizable data visualisations that can be readily shared through unique URLs or embedded for further use online.

Data for Land-Use and Forestry indicator are provided by the Food and Agriculture Organisation of the United Nations (FAO). WRI has been granted a non-exclusive, non-transferable right to publish these data. Therefore, if users wish to republish this dataset in whole or in part, they should contact FAO directly at copyright@fao.org

Data sources: — Boden, T.A., G. Marland, and R.J. Andres. 2015. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi 10.3334/CDIAC/00001_V2015 Available online at:http://cdiac.ornl.gov/trends/emis/overview_2011.html . — Food and Agriculture Organisation of the United Nations (FAO). 2014. FAOSTAT Emissions Database. Rome, Italy: FAO. Available at: http://faostat3.fao.org/download/G1/*/E — International Energy Agency (IEA). 2014. CO2 Emissions from Fuel Combustion (2014 edition). Paris, France: OECD/IEA. Available online at:http://data.iea.org/ieastore/statslisting.asp. © OECD/IEA, [2014]. World Bank. 2014. World Development Indicators 2014. Washington, DC. Available at: http://data.worldbank.org/ Last Accessed May 18th, 2015 — U.S. Energy Information Administration (EIA). 2014. International Energy Statistics Washington, DC: U.S. Department of Energy. Available online at:http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8 — U.S. Environmental Protection Agency (EPA). 2012. “Global Non-CO2 GHG Emissions: 1990-2030.” Washington, DC: EPA. Available at: http://www.epa.gov/climatechange/EPAactivities/economics/nonco2projections.html

Recommended citation

Gütschow, J.; Günther, A.; Pflüger, M. (2021): The PRIMAP-hist national historical emissions time series v2.3.1 (1850-2019). zenodo. doi:10.5281/zenodo.5494497.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

• Use of the dataset and full description
• Abstract
• Support
• Sources
• Files included in the dataset
• Notes
• Data format description (columns)
• References
• Changelog

Abstract

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 1750 to 2019, 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 Product Use (IPPU), and Agriculture are available. Due to data availability and methodological issues, version 2.3.1 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care.

The PRIMAP-hist v2.3.1 dataset is an updated version of

Gütschow, J.; Günther, A.; Pflüger, M. (2021): The PRIMAP-hist national historical emissions time series v2.3 (1750-2019). zenodo. doi:10.5281/zenodo.5175154

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset.

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data is published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation.

We have also, for the first, time included files with more than three significant digits. This file is mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag “v2.3.1” in any issue you create regarding this dataset.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

Sources

• Global CO$_2$ emissions from cement production v210723 (Andrew 2021)** data, paper: Andrew (2019a)
• BP Statistical Review of World Energy website: British Petroleum (2021)
• CDIAC data: Boden et al. (2017): Gilfillan et al. (2020), paper Gilfillan and Marland (2021)
• EDGAR versions 4.2 and 4.2 FT2010: EDGAR v4.2, EDGAR v4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)
• EDGAR version 6.0: data, website, Paper in prep.: Crippa et al. (n.d.), JRC (2021)
• EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)
• FAOSTAT database data: Food and Agriculture Organization of the United Nations (2021)/li>
• RCP historical data data, paper: Meinshausen et al. (2011)
• UNFCCC National Communications and National Inventory Reports for developing countries website, data: UNFCCC (2021c), Gieseke and Gütschow (2021)
• UNFCCC Biennial Update Reports website: UNFCCC (2021b)
• UNFCCC Common Reporting Format (CRF) website, paper, data: Gütschow et al. (2021b), UNFCCC (2021a) (processed as described in Jeffery et al. (2018a))
• Official country repositories (non-UNFCCC)
  • Taiwan / Republic of China: website: Republic of China - Environmental Protection Administration (2020)
  • South Korea: website: Republic of Korea (2020)

Files included in the dataset

For each dataset we have three files: the .nc file contains the data and metadata in the native PRIMAP2 netCDF based format. The .csv file contains the data in a csv format following the specifications of the PRIMAP2 interchange format. The metadata for the interchange format file is included in the .yaml file.

• Guetschow-et-al-2021-PRIMAP-hist_v2.3.1_20_Sep_2021.X: The main dataset with numerical extrapolation of all time series to 2019 and three significant digits.
• Guetschow-et-al-2021-PRIMAP-hist_v2.3.1_no_extrap_20_Sep_2021.X: Variant without numerical extrapolation of missing values and not including the country groups mentioned in section [“country”] (three significant digits).
• Guetschow-et-al-2021-PRIMAP-hist_v2.3.1_no_rounding_20_Sep_2021.X: The main dataset with numerical extrapolation of all time series to 2019 and eleven significant digits.
• Guetschow-et-al-2021-PRIMAP-hist_v2.3.1_no_extrap_no_rounding_20_Sep_2021.csv: Variant without numerical extrapolation of missing values and not including the country groups mentioned in section [“country”] (eleven significant digits).
• PRIMAP-hist_v2.3.1_data-description.pdf: Data description including changelog.
• PRIMAP-hist_v2.3.1_updated_figures.pdf: Updated figures from the PRIMAP-hist paper published in ESSD.

Notes

• Emissions from international aviation and shipping are not included in the dataset.
• Emissions from Land Use, Land-Use Change, and Forestry (LULUCF) are not included in the main version of this dataset. They are included in the

DISCOVERAQ_Maryland_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, and PTR-MS. This data product contains only data from the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

The EV-GHG Mobile Source Data asset contains measured mobile source GHG emissions summary compliance information on light-duty vehicles, by model, for certification as required by the 1990 Amendments to the Clean Air Act, and as driven by the 2010 Presidential Memorandum Regarding Fuel Efficiency and the 2005 Supreme Court ruling in Massachusetts v. EPA that supported the regulation of CO2 as a pollutant. Manufacturers submit data on an annual basis, or as needed to document vehicle model changes. This asset will be expanded to include medium and heavy duty vehicles in the future.The EPA performs targeted GHG emissions tests on approximately 15% of vehicles submitted for certification. Confirmatory data on vehicles is associated with its corresponding submission data to verify the accuracy of manufacturer submissions beyond standard business rules.Submitted data comes in XML format or as documents, with the majority of submissions sent in XML, and includes descriptive information on the vehicle itself, emissions information, and the manufacturer's testing approach. This data may contain proprietary information (CBI) such as information on estimated sales or other data elements indicated by the submitter as confidential. CBI data is not publically available; however, CBI data can accessed within EPA under the restrictions of the Office of Transportation and Air Quality (OTAQ) CBI policy [RCS Link]. Pollutants data includes CO2, CH4, N2O. Datasets are divided by vehicle/engine model and/or year with corresponding emission, test, and certification data. Data assets are stored in EPA's Verify system.Coverage began in 2011, with summary light duty data available to the public on request. Raw data is only available to select EPA employees.EV-GHG Mobile Source Data submission documents with metadata, certificate and summary decision information is stored in Verify after it has been quality assured. Where summary data appears inaccurate, OTAQ returns the entries for review to their originator.

This dataset contains the surface underway pCO2 data of the first dedicated West Coast Ocean Acidification cruise (WCOA2011). The cruise took place August 12-30, 2011 aboard the R/V Wecoma. Ninety-five stations were occupied from northern Washington to southern California along thirteen transect lines. At all stations, CTD casts were conducted, and discrete water samples were collected in Niskin bottles. Underway measurements of pCO2 were collected during the duration of the cruise. The cruise was designed to obtain a synoptic snapshot of key carbon, physical, and biogeochemical parameters as they relate to ocean acidification (OA) in the coastal realm. During the cruise, some of the same transect lines were occupied as during the 2007 West Coast Carbon cruise, as well as many CalCOFI stations. This effort was conducted in support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP).

Mechanical fuel treatments are a primary pre-fire strategy for potentially mitigating the threat of wildland fire, yet there is limited information on how they impact shrubland ecosystems. This publication contains data related to vegetation structure and composition in mechanically masticated chaparral communities used to assess the impact of these fuel treatments on shrubland vegetation and to determine the extent to which they emulate postfire succession. Data were collected from within chaparral dominated communities on the Angeles, Cleveland, Los Padres, and San Bernardino national forests of southern California. The climate of the region is Mediterranean with mild, wet winters and hot, dry summers and the topography is rugged and steep with elevations from near sea level to over 3500 m in the Transverse and Peninsular ranges. The rocky and shallow soils of the area are predominantly granitic and support a wide range of shrubland communities that include stands dominated by a single species (>50% cover) such as Adenostoma fasciculatum (chamise), Arctostaphylos spp. (manzanita), Ceanothus spp. (wild lilac) and Quercus spp. (oak) and mixed stands without a single dominant. The mechanically masticated fuel treatments utilized for this study were identified using the USGS Southern California Fuel Treatment Data Set (http://www.calfiresci.org) and were limited to single-entry mastication treatments with no follow-up treatment of burning or re-mastication. The size and shape of available treatments were highly variable and thus a random sampling design was used to maximize the number of study sites. This was accomplished by selecting sites from within treatment boundaries using the random-point generator in ArcGIS and a buffer of at least 400 m between points. The final sample size of accessible locations included 149 mechanically masticated study sites, each with a treatment plot and a control. All treatments were completed between 2004 and 2011 using a variety of masticating equipment and ranged in size from less than a hectare to large-scale treatments spanning thousands of hectares across entire ridgelines. The timing of mastication treatments extended across all seasons and ranged in completion time from several days to several years depending on their size. In order to evaluate the differences between mechanically masticated and early successional postfire vegetation two comparisons were made. The first was a single site case study on the Cleveland National Forest where a spark from a masticator ignited the 39 acre Long Canyon Wildfire on September 23rd, 2010 that burned next to the mechanical treatment being implemented and comprised similar pre-disturbance vegetation. This comparison consisted of four study plots in the masticated treatment and four study plots in the adjacent burned area that were monitored for the first two years following the disturbances. The second was a regionally broad comparison of two-year old mechanically masticated study plots from this fuel treatment study (n = 25) to a subset of two-year-old postfire plots (n = 56) from a regional study of early postfire succession in southern California chaparral published in an earlier paper (Keeley et al. 2008). This study investigated factors determining fire severity and ecosystem responses in 250 randomly selected study plots within the 2003 Cedar, Grand Prix, Old, and Paradise fire perimeters. The subset of 56 plots chosen from the original 250 plots were based on the criteria that the site was located within one of the four southern California national forests, was in chaparral vegetation, and had a pre-disturbance stand age and elevation within the same range as the two-year-old masticated sites used in the regional comparison. These data support the following publication: Brennan, Teresa J., Keeley, J.E. In Press. Response of chaparral shrubland vegetation to mechanical mastication fuel treatments. Regional postfire data were extracted from this publication: Keeley J.E., T.J. Brennan, and A.H. Pfaff. 2008. Fire severity and ecosystem responses following crown fires in California shrublands. Ecological Adaptations 18: 1530-1546.

Gasoline Prices in Egypt remained unchanged at 0.35 USD/Liter in June. This dataset provides - Egypt Gasoline Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gasoline Prices in Philippines increased to 1.06 USD/Liter in June from 0.98 USD/Liter in May 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.

Gasoline Prices in Indonesia remained unchanged at 0.61 USD/Liter in June. This dataset provides the latest reported value for - Indonesia Gasoline Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.