Gasoline rose to 1.86 USD/Gal on October 3, 2025, up 0.37% from the previous day. Over the past month, Gasoline's price has fallen 7.23%, and is down 11.22% compared to the same time last year, 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 October of 2025.

Graph and download economic data for US Regular All Formulations Gas Price (GASREGW) from 1990-08-20 to 2025-09-29 about gas, commodities, and USA.

UK Gas rose to 80.96 GBp/thm on October 3, 2025, up 2.19% from the previous day. Over the past month, UK Gas's price has risen 2.32%, but it is still 20.62% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on October of 2025.

Gasoline Prices in France increased to 2.07 USD/Liter in September from 2.05 USD/Liter in August of 2025. This dataset provides the latest reported value for - France 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 presents the distribution of median household income among distinct age brackets of householders in Gas. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gas. 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 2022

In terms of income distribution across age cohorts, in Gas, the median household income stands at $79,656 for householders within the 45 to 64 years age group, followed by $65,512 for the 65 years and over age group. Notably, householders within the 25 to 44 years age group, had the lowest median household income at $59,872.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-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 median household income by age. You can refer the same here

High Frequency Indicator: The dataset contains year- and month-wise historically compiled data from the year 2018-19 to till date on International Free On Board (FOB) price of Petrol and Diesel

Notes:

1. The FOB (Free On Board) price is the price of goods at the frontier of the exporting country or price of a service provided to a non-resident. It includes the values of the goods or services at the basic price, the transport and distribution services up to the frontier, the taxes minus the subsidies
2. Petrol Prices are for 92RON Singapore Gasoline.
3. Diesel Prices are for 10PPM Arab Gulf Gasoil.

We have updated and extended the dataset on giant oil discoveries previously published by Mike Horn under the auspice of the AAPG that contains discoveries from 1868 to 2010 (Halbouty, 2014; Horn, 2004). Beyond incorporating new discoveries after 2010 up to 2018 (and soon 2020), and corrections to the data, the main contribution has been to provide estimates of the US dollar value of the petroleum field presented in terms of Net Present Value (NPV). This is based on the discoveries’ estimated ultimate recoverable (EUR) amount of oil and gas, measured in nominal and real US dollars, using the price of petroleum in the year of discovery. Main publication: Cust, James, David Mihalyi and Alexis Rivera-Ballesteros, 2021, The Economic Effects of Giant Oil and Gas Discoveries in Charles A. Sternbach, Robert K. Merrill, and John C. Dolson, eds., Giant Fields of the Decade: 2010-2020: AAPG Memoir 125, p. 21-36

Beginning with the 2017 sands, the column P_J has been removed. The columns P_RECOIL, P_RECGAS, P_RECBOE, J_RECOIL, J_RECGAS, and J_RECBOE have been combined and renamed to Original Oil, Original Gas, and Original BOE, replacing the former DISCOIL, DISCGAS, DISCBOE columns. The columns P_CUMOIL, P_CUMGAS, and P_CUMBOE have been renamed Cum Oil, Cum Gas, and Cum BOE. The columns P_REMOIL, P_REMGAS, and P_REMBOE have been renamed Oil Reserves, Gas Reserves, and BOE Reserves. These changes were made in order to more closely align with the Estimated Oil and Gas Report. Estimated Oil and Gas Report 2018: https://www.boem.gov/BOEM-2021-052/ . As of 2017, two additional columns, LAT and LONG have been included to provide the bottomhole location of the discovery well for each sand. In order to align with BOEM�s current biostratigraphic chart, the Plio-Pleistocene boundary was adjusted, as well as some units within the Paleogene. This change may be reflected in the chronozone designation of some sands. Within a downloadable geodatabase file, an ArcGIS feature dataset for well location has been provided. A brief workflow for accessing this GIS feature dataset has been included in the zipped file.

Gasoline Prices in Mexico increased to 1.28 USD/Liter in September from 1.26 USD/Liter in August of 2025. 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.

The monthly Greenhouse Gas (GHG) emission data represents Montgomery County Facilities and Fleet by month beginning July 2019. Facilities: The Facilities GHG data represents physical structures used by County residents and County staff who provide services for County residents. Examples include recreation, libraries, theater and arts, health and human services, liquor retail, courthouses, general services, maintenance facilities, correctional facilities, police stations, fire stations, volunteer fire stations, garages, parking lots, bus shelters and park & ride locations. Facilities use the following fuel sources: grid electricity, natural gas, propane and diesel fuel. Facilities GHG data DOES NOT include Montgomery County Public Schools, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC). Fleet: The Fleet GHG data represents Montgomery County vehicles used by County staff who provide services for County residents. Examples include mass transit buses, snowplows, liquor trucks, light duty trucks, police cars, fire engines and fire service equipment, etc. Each County vehicle use different fuel sources (i.e. diesel, mobil diesel, compressed natural gas, unleaded and E-85). Fleet GHG data DOES NOT include Montgomery County Public School buses, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC) vehicles. GHG Calculation Method: Facilities and Fleet fuel sources are converted into one common unit of energy- 1 Million British thermal units (MMBtu) which are then used with emissions factors and 100-year global warming potential (GWP) to calculate GHG emissions into one common unit of measure- Metric Tons of CO2 Equivalent (MTCO2e). For more information go to: • How to Calculate GHG emissions at https://www.youtube.com/watch?v=zq5wTjvLqnY&t=186s • Emissions & Generation Resource Integrated Database (eGRID) at https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid • Emission Factors for GHG Inventories at https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf Update Frequency : Monthly

The gasoline price in the Philippines continued to fluctuate in 2023 and the first quarter of 2025, reaching 56.34 Philippine pesos per liter in April 2025. The retail price of petrol peaked between May and June 2022.   Which countries supply petroleum products to the Philippines? The refined petroleum products supply in the Philippines is mainly imported from South Korea, which accounts for 31 percent of the total import share. Singapore and China also provide a large share of the country’s petroleum product supply. Due to a dormant oil refining capacity, the production of petroleum refinery products in the Philippines has shown sluggish growth recently, further emphasizing the need for importing such products. Leading petroleum companies in the Philippines Shell Pilipinas Corporation held the highest share of the petroleum market in the Philippines, with a market share of about 16 percent in 2023. The company operated its petroleum refinery until 2020, when it decided to focus on imports. There is only one operating oil refinery in the country, which is run by the second-largest oil company – Petron Corporation.

About this dataset

Context

The dataset tabulates the population of Gas by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Gas. The dataset can be utilized to understand the population distribution of Gas by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Gas. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Gas.

Key observations

Largest age group (population): Male # 25-29 years (42) | Female # 70-74 years (80). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Gas population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Gas is shown in the following column.
• Population (Female): The female population in the Gas is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Gas for each 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 Population by Gender. You can refer the same here

This dataset consists of 119,494 lines of data consisting of idle well fluid level depth, auxiliary measurements, and well parameters from California oil and gas wells that were reported to the California Department of Conservation, Geologic Energy Management Division (CalGEM). The dataset was provided by CalGEM in March 2018 and includes measurements made from 1976 to 2018. There are 5 sets of operator-reported data: idle well fluid level depth (N=101,734), well clean out depth (N=8,402), depth of base of fresh water (N=108,216), well top perforation depth (N=93,569), and depth reached (N=15,756). These are associated with a well, defined by API number, well number, operator name, test date, township, section, range, and pool code. While detailed metadata for these measurements was not provided by CalGEM, they are thought to be collected under idle well testing regulations. Present regulations broadly define an idle well as one that has not been used for production or injection for 24 months or longer (California Code of Regulations, 2022, Title 14 §1760). Below, a summary of current regulations related to this program are presented; however, regulations at the time of data collection may be different. Once a well is classified as an idle well, a fluid level test using acoustical, mechanical, or other methods must be conducted within 24 months, and every 24 months beyond that, as long as a well is idle, unless the wellbore does not penetrate an underground source of drinking water (USDW) (California Code of Regulations, 2022, Title 14 §1772.1). Currently, within 8 years of a well becoming idle a clean out tag is required. This is done to demonstrate that the well can be properly plugged and abandoned. A clean out tag is done by passing open-ended tubing or a gauge ring of a minimum diameter equal to that of tubing necessary to plug and abandon a well (California Code of Regulations, 2022, Title 14 §1772.1). This testing must generally be repeated once every 48 months as long as a well is classified as an idle well. Freshwater is defined as water that contains 3,000 milligrams/liter (mg/L) or less of total dissolved solids (California Code of Regulations, 2022, Title 14 §1720.1). The base of freshwater is the depth in a well where the overlying water is freshwater. Neither top perforation depth or depth reached is defined by statute. Top perforation is generally the shallowest active perforated interval. It is not clear what depth reached represents. Well elevation and pool name were added from other datasets to aid in analysis. Pools, identified by pool code and pool name, are defined as independent hydrocarbon zones (California Public Resources Code § 3227.6.b). The accuracy of the values reported to CalGEM by oil-field operators is unknown. Unrealistic values were discarded from the data as noted in the process steps. This dataset was compiled and analyzed as part of the California State Water Resources Control Board Oil and Gas Regional Monitoring Program and the U.S. Geological Survey California Oil, Gas, and Groundwater (COGG) program.

In order to measure the impact of its Open Data, Ores is interested in being kept informed of reuses that will be made of the data made available. Name of the report: Consumption by type of customer Aggregation level: The dataset contains aggregated information by Energy (Electricity/gas), Geographic Area, Client Type Year of consumption: Year concerned Scope: Reporting of all access points with a connection that was contractually active on the last day of the relevant quarter of consumption. Energy: Gas Frequency: Quarterly (published annually) End date of dataset: 31/12/2020 Date of availability of the dataset: 30/10/N+ 1 Injection/Removal: Sampling Consumption Amount consumed of energy (natural gas or electricity) measured by a device in an indoor installation. Electricity or natural gas may be collected and/ or injected into the distribution network through an access point. The levy is the purchase of electricity and/or gas natural to the distribution network. Injection is the excess energy produced by the user of the network who is not consumed for his own use, but who is reinjected into the distribution network. Geographical area: «The geographical sectors correspond to the territories of the former intercommunal before merging. These are territorial subdivisions of ORES Assets grouping together some associates (affiliated municipalities and financing inter-municipals) associated) by geographical area. The link below shows the municipalities affiliated to each sector.’ https://www.oresassets.be/fr/territoires-d-activites-et-secteurs Locality: A locality is a territorial entity of an indeterminate size or not, usually inhabited. A municipality usually corresponding to a city with villages surrounding, to several villages or to an important village surrounded by hamlets. The locality is the name given to these communal subdivisions. Number of access points: The point of the distribution system where electricity or gas is taken or injected and identified by a single EAN. EAN: European Article Numbering. Unique identification code of an access point (point of supply). This code is communicated to the customer concerned by the GRD. Consumption (GWh): “Value” calculated the consumption related to the volume of energy taken and then reduced to calendar year/calendar month. These approximate consumptions are calculated monthly. GWh: Gigawatt-hour symbol, unit of energy measurement which corresponds to the power of an active gigawatt for an hour. » Customer type (until 2020) Amr (Automatic meter reading): Telereading consists of remote reading of consumption values per unit of time, for network users with significant consumption or connection high power. It does not require the intervention of a lifter.Standard consumption profiles, ‘Synthetic Load Profiles’ (SLP), are used in the electricity and liberalised gas market for the allocation of levies from consumers who are not equipped with telemeters. 7 SLP profiles (4 for electricity and 3 for gas) have been established, below for gas

Unusual stellar explosions represent an opportunity to learn about both stellar and galaxy evolution. Mapping the atomic gas in host galaxies of such transients can lead to an understanding of the conditions triggering them. We provide resolved atomic gas observations of the host galaxy, CGCG137-068, of the unusual, poorly-understood transient AT2018cow searching for clues to understand its nature. We test whether it is consistent with a recent inflow of atomic gas from the intergalactic medium, as suggested for host galaxies of gamma-ray bursts (GRBs) and some supernovae (SNe). We observed the HI hyperfine structure line of the AT2018cow host with the Giant Metrewave Radio Telescope. There is no unusual atomic gas concentration near the position of AT2018cow. The gas distribution is much more regular than those of GRB/SN hosts. The AT2018cow host has an atomic gas mass lower by 0.24dex than predicted from its star formation rate (SFR) and is at the lower edge of the galaxy main sequence. In the continuum we detected the emission of AT2018cow and of a star-forming region in the north-eastern part of the bar (away from AT2018cow). This region hosts a third of the galaxy's SFR. The absence of atomic gas concentration close to AT2018cow, along with a normal SFR and regular HI velocity field, sets CGCG137-068 apart from GRB/SN hosts studied in HI. The environment of AT2018cow therefore suggests that its progenitor may not have been a massive star. Our findings are consistent with an origin of the transient that does not require a connection between its progenitor and gas concentration or inflow: an exploding low-mass star, a tidal disruption event, a merger of white dwarfs, or a merger between a neutron star and a giant star. We interpret the recently reported atomic gas ring in CGCG137-068 as a result of internal processes connected with gravitational resonances caused by the bar. Cone search capability for table J/A+A/627/A106/list (List of fits images and datacubes) Associated data

The U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board compiled and analyzed data for the purpose of mapping groundwater salinity in selected oil and gas fields in California. The data for the Fruitvale and Rosedale Ranch oil fields include well construction data, digitized borehole geophysical data, geochemical analyses of water samples from oil and gas wells and groundwater wells, geological formation depths, and the groundwater total dissolved solids (TDS) calculations used in an accompanying manuscript. These data have been compiled from many sources and span several decades. The well construction data includes attributes such as the date drilling began (spud date), well depth, the depth of top producing perforation, and borehole orientation. These data have been in archived scanned pages in raster format on the Division of Oil, Gas, and Geothermal Resources (DOGGR) website. Similarly, the borehole geophysical data, measured by oil companies, has been archived in raster format. This project has converted the borehole geophysical data from selected oil and gas wells into a computer readable numerical format. The geochemical analyses have also been archived in scanned formats, but now have been compiled into numerical datasets in additional data releases by Metzger and others (2018) and Gans and others (2018). All of the data compiled and analyzed are part of the California State Water Resources Control Board's Program of Regional Monitoring of Water Quality in Areas of Oil and Gas Production and the USGS California Oil, Gas, and Groundwater (COGG) program.

The 2025 annual OPEC basket price stood at ***** U.S. dollars per barrel as of August. 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.

Contents

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

Use of the dataset and full description

A full description of a previous version of the dataset can be found in:

Jeffery, M. L., Gütschow, J., Gieseke, R., and Gebel, R.: PRIMAP-crf: UNFCCC CRF data in IPCC 2006 categories, Earth Syst. Sci. Data, 10, 1427-1438, https://doi.org/10.5194/essd-10-1427-2018, 2018

A description paper for this dataset is in preparation.

If you use this dataset, we would appreciate a brief notification to the lead author (johannes.guetschow@pik-potsdam.de) with a description of how the data was used. This information can help to guide the production of future updates to the dataset.

New versions of the UNFCCC CRF data are released annually with an additional year of data. Some countries also submit revised versions of their data through the year. Where possible, the PRIMAP-crf data will be updated accordingly and a revised dataset released. Data releases with an additional year of data are indicated in the naming of the data - the year of data publication is indicated by the dataset name, e.g. PRIMAP-crf-2019 data includes data first released by countries in 2019. Inclusion of subsequent data revisions from the same year are indicated by the version number, for example PRIMAP-crf-2019-v2 will include all CRF2019 data published until 27th April 2020.

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).

Support

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

If you wish to use the .csv file in excel but the data does not appear to display correctly, you need to set the delimiter character. To do so:

• highlight the first column
• Under the 'Data' tab, select 'Text to columns'
• In the first pop-up window, select 'Delimited'
• In the second pop-up, select 'comma' separated values
• No selection needed in the third pop-up, click Finish and the data should display correctly.

Abstract

PRIMAP-crf is a processed version of data reported by countries to the United Nations Framework Convention on Climate Change (UNFCCC) in the Common Reporting Format (CRF). The processing has three key aspects: 1) Data from individual countries and years are combined into one file. 2) Data is re-organised to follow the IPCC 2006 hierarchical categorisation. 3) 'Baskets' of gases are calculated according to different global warming potential estimates from each of the three most recent IPCC reports.

Sources

The original CRF data is all freely available via the UNFCCC website https://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2019. Please consider also citing this source in any work that you produce using PRIMAP-crf.

This dataset includes all 2019 CRF data available as of 27th April, 2020. For later data updates, please check the PRIMAP-crf page of the Paris Reality Check website https://www.pik-potsdam.de/paris-reality-check/primap-crf/.

Files included in the dataset

Guetschow-et-al-2019-PRIMAP-crf_2019-v2.csv : primary data file with data in IPCC 2006 categories
Guetschow-et-al-2019-PRIMAP-crf96_2019-v2.csv : additional data file with data in IPCC 1996 categories
PRIMAP-crf-IPCC2006-category-codes.csv : definitions of IPCC 2006 category codes used in PRIMAP-crf
primap-crf-data-description-2019v2.pdf : data description document

All comma-separated values (CSV) files are also provided as Excel (.xlsx) files for ease of use.

Data format description (columns)

The PRIMAP-crf data in the comma-separated values (CSV) and Excel files is formatted consistently with other PRIMAP emissions datasets, including PRIMAP-hist (Gütschow et al., 2016, 2017 and 2019).

The data contained in each column is as follows:

version

The version refers to the year of release of the dataset (in this case 2019), and the revision number (here v2). 2019-v2 includes all 2019 data released until 27th April 2020. Previous versions are available for the emissions data reported in 2017 (Jeffery et al., 2018) and 2018 (Gütschow et al., 2019).

country

ISO 3166 three-letter country codes.

Additionally, the European Union is included as the sum of its 28 pre-Brexit member states with the code "EU28" and as the sum of it's 27 post-Brexit member states with the code "EU27BX". The EU data is the sum of the data of it's member states, not the data officially reported to the UNFCCC by the EU.

category

IPCC (Intergovernmental Panel on Climate Change) 1996 or 2006 category codes. Please see the accompanying file PRIMAP-crf-IPCC2006-category-codes.csv for a definition of codes used for IPCC 2006 categories.

Data for 1996 categories are shared for the top level categories only, as defined below. Note that 'IPC' is used to indicate 2006 categorisation, and 'CAT' for 1996.

Category code Description

Category descriptions using IPCC 1996 terminology.

| CAT0  | National Total                  |
| ------- | ------------------------------------------------ |
| CATM0EL | National Total, excluding LULUCF         |
| CAT1  | Total Energy                   |
| CAT1A  | Fuel Combustion Activities            |
| CAT1B  | Fugitive Emissions from Fuels          |
| CAT2  | Industrial Processes               |
| CAT3  | Solvent and Other Product Use          |
| CAT4  | Agriculture                   |
| CAT5  | Land Use, Land Use Change, and Forestry (LULUCF) |
| CAT6  | Waste                      |
| CAT7  | Other                      |

entity

Gas categories using global warming potentials from either IPCC Second Assessment Report (SAR), Assessment Report 4 (AR4), Assessment Report 5 (AR5), or Assessment Report 5 with carbon-cycle feedbacks (AR5CCF). Where no global warming potential is specfied, quantities are given in absolute weights of the gas.

| Code      | Description                   |
| :------------- | :----------------------------------------------- |
| CH4      | Methane                     |
| CO2      | Carbon Dioxide                  |
| N2O      | Nitrous Oxide                  |
|        |                         |
| SF6      | Sulfur Hexafluoride               |
| NF3      | Nitrogen Trifluoride               |
|        |                         |
| HFC125     | Pentafluoroethane, HFC-125            |
| HFC134     | Tetrafluoroethane, HFC-134            |
| HFC134A    | Tetrafluoroethane, HFC-134a           |
| HFC143     | Trifluoroethane, HFC-143             |
| HFC143A    | Trifluoroethane, HFC-143a            |
| HFC152A    | 1,1-Difluoroethane, HFC-152a           |
| HFC227EA    | Heptafluoropropane, HFC-227a           |
| HFC23     | Trifluoromethane, HFC-23             |
| HFC236FA    | 1,1,1,3,3,3-hexafluoropropane, HFC-236fa     |
| HFC245CA    | 1,1,2,2,3-pentafluoropropane, HFC-245ca     |
| HFC245FA    | Enovate, HFC-245fa                |
| HFC32     | Difluoromethane, HFC-32             |
| HFC365MFC   | 1,1,1,3,3-pentafluorobutane, HFC-365mfc     |
| HFC41     | Fluoromethane, HFC-41              |
| HFC4310    | 1,1,1,2,3,4,4,5,5,5-decafluoropentane, HFC-43-10 |
| OTHERHFCS   | Unspecified mix of HFCs (GWP as in reporting)  |
| OTHERHFCSAR4  | Unspecified mix of HFCs (GWP as in reporting)  |
| OTHERHFCSAR5  | Unspecified mix of HFCs (GWP as in reporting)  |
| OTHERHFCSAR5CCF| Unspecified mix of HFCs (GWP as in reporting)  |
| HFCS      | Hydrofluorocarbons (SAR)             |
| HFCSAR4    | Hydrofluorocarbons (AR4)             |
| HFCSAR5    | Hydrofluorocarbons (AR5)             |
| HFCSAR5CCF   | Hydrofluorocarbons (AR5CCF)           |
|        |                         |
| C2F6      | Hexafluoroethane, C2F6              |
| C3F8      | Octafluorpropane,

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income.

D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 54 percent of the Estimated Annual kWh Savings and 70 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf.

This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD).

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