United States US: Energy Use: Kg of Oil Equivalent per Capita data was reported at 6,797.621 kg in 2015. This records a decrease from the previous number of 6,955.524 kg for 2014. United States US: Energy Use: Kg of Oil Equivalent per Capita data is updated yearly, averaging 7,651.901 kg from Dec 1960 (Median) to 2015, with 56 observations. The data reached an all-time high of 8,438.403 kg in 1978 and a record low of 5,612.080 kg in 1961. United States US: Energy Use: Kg of Oil Equivalent per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Energy Production and Consumption. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

United States Primary Energy Consumption per Capita data was reported at 77,027.836 kWh/Person in 2023. This records a decrease from the previous number of 78,347.914 kWh/Person for 2022. United States Primary Energy Consumption per Capita data is updated yearly, averaging 89,404.797 kWh/Person from Dec 1965 (Median) to 2023, with 59 observations. The data reached an all-time high of 98,110.680 kWh/Person in 1973 and a record low of 73,294.336 kWh/Person in 2020. United States Primary Energy Consumption per Capita data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s United States – Table US.OWID.ESG: Environmental: CO2 and Greenhouse Gas Emissions: Annual.

Electricity consumption in the United States totaled ***** terawatt-hours in 2024, the highest value in the period under consideration. Figures represent energy end use, which is the sum of retail sales and direct use of electricity by the producing entity. Electricity consumption in the U.S. is expected to continue increasing in the coming years. Which sectors consume the most electricity in the U.S.? Consumption has often been associated with economic growth. Nevertheless, technological improvements in efficiency and new appliance standards have led to a stabilizing of electricity consumption, despite the increased ubiquity of chargeable consumer electronics. Electricity consumption is highest in the residential sector, followed by the commercial sector. Equipment used for space heating and cooling account for some of the largest shares of residential electricity end use. Leading states in electricity use Industrial hub Texas is the leading electricity-consuming U.S. state. In 2023, the southwestern state, which houses major refinery complexes and is also home to over ** million people, consumed almost ****terawatt-hours. Florida and California followed in second and third, with an annual consumption of approximately *** terawatt-hours and 240 terawatt-hours, respectively.

United States US: Electric Power Consumption: per Capita data was reported at 12,984.333 kWh in 2014. This records a decrease from the previous number of 12,996.845 kWh for 2013. United States US: Electric Power Consumption: per Capita data is updated yearly, averaging 10,886.858 kWh from Dec 1960 (Median) to 2014, with 55 observations. The data reached an all-time high of 13,704.577 kWh in 2005 and a record low of 4,049.787 kWh in 1960. United States US: Electric Power Consumption: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Energy Production and Consumption. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL). Compared to the first version of the dataset (https://zenodo.org/record/79880387), this updated dataset is based on model runs where the Inflation Reduction Act (IRA) are implemented in the model scenarios. In addition to the queried and post-processed key output variables related to residential energy sector in .csv tables, we also upload the full model output databases in this repository, so that users can query their desired model outputs.

GCAM-USA operates within the Global Change Analysis Model (GCAM), which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).

GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: https://jgcri.github.io/gcam-doc/toc.html.

The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of September 24, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP per capita) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.

Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2050 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the full model output databases and key output variables related to the residential energy sector under the four scenarios, including:

• income shares by consumer groups at each state over 2015-2050 (Casper et al., 2023)
• residential energy consumption per capita by service, fuel, state, and income group, 2015-2050
• residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, state, and income group, 2015-2050
• estimated energy burden (Eq.1), by state and income group, 2015-2050
• estimated satiation gap (Eq.2), by service, state, and income group, 2015-2050
• residential heating service inequality (Eq.3), by state, 2015-2050

Table 1

Eq. 1

\(Energy\ burden_{i,k} = \dfrac{\sum_j (service\ output_{i,j,k} * service\ cost_{j,k})}{GDP_{i,k}}\)

for income group i and state k, that sums over all residential energy services j.

Eq. 2

\(Satiation\ Gap_{i,j,k} = \dfrac{satiation\ level_{j,k} - service\ output_{i,j,k}} {satiation\ level_{j,k}}\)

for service j, income group i, and state k. Note that the satiation level and service output are per unit of floorspace.

Eq. 3

\(Residential\ heating\ service\ inequality_j = \dfrac{S_j^{d10}}{(S_j^{d1} +S_j^{d2} + S_j^{d3} + S_j^{d4})}\)

for service j where S is the residential heating service output per capita of the highest income group (d10) divided by the sum of that of the lowest four income groups (d1, d2, d3, and d4), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality. Among the key output variables in this repository, we provide the residential heating service inequality output table as an example.

Reference

Casper, K. C., Narayan, K. B., O'Neill, B. C., Waldhoff, S. T., Zhang, Y., & Wejnert-Depue, C. (2023). Non-parametric projections of the net-income distribution for all U.S. states for the shared socioeconomic pathways. Environmental Research Letters. http://iopscience.iop.org/article/10.1088/1748-9326/acf9b8.

IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].

Acknowledgement

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

Context

India is the world's third-largest producer and third-largest consumer of electricity. The national electric grid in India has an installed capacity of 370.106 GW as of 31 March 2020. Renewable power plants, which also include large hydroelectric plants, constitute 35.86% of India's total installed capacity. During the 2018-19 fiscal year, the gross electricity generated by utilities in India was 1,372 TWh and the total electricity generation (utilities and non-utilities) in the country was 1,547 TWh. The gross electricity consumption in 2018-19 was 1,181 kWh per capita. In 2015-16, electric energy consumption in agriculture was recorded as being the highest (17.89%) worldwide. The per capita electricity consumption is low compared to most other countries despite India having a low electricity tariff.

In light of the recent COVID-19 situation, when everyone has been under lockdown for the months of April & May the impacts of the lockdown on economic activities have been faced by every sector in a positive or a negative way. With the electricity consumption being so crucial to the country, we came up with a plan to study the impact on energy consumption state and region wise.

The dataset is exhaustive in its demonstration of energy consumption state wise.

Content

Data is in the form of a time series for a period of 17 months beginning from 2nd Jan 2019 till 23rd May 2020. Rows are indexed with dates and columns represent states. Rows and columns put together, each datapoint reflects the power consumed in Mega Units (MU) by the given state (column) at the given date (row).

Acknowledgements

Power System Operation Corporation Limited (POSOCO) is a wholly-owned Government of India enterprise under the Ministry of Power. It was earlier a wholly-owned subsidiary of Power Grid Corporation of India Limited. It was formed in March 2009 to handle the power management functions of PGCIL.

The dataset has been scraped from the weekly energy reports of POSOCO.

Inspiration

Extensive research on power usage in the country is what inspired us to compile the dataset. We are making it public along with our research of the same. This is our first step towards independent data-based research. We are open to suggestions, compliments and criticism alike.

Do leave an upvote if you found the dataset helpful. It keeps me motivated to keep working hard :)

There are limited open source data available for determining water production/treatment and required energy for cities across the United States. This database represents the culmination of a two-year effort to obtain data from cities across the United States via open records requests in order to determine the state of the U.S. urban energy-water nexus. Data were requested at the daily or monthly scale when available for 127 cities across the United States, represented by 253 distinct water and sewer districts. Data were requested from cities larger than 100,000 people and from each state. In the case of states that did not have cities that met these criteria, the largest cities in those states were selected. The resulting database represents a drinking water service population of 81.4 million and a wastewater service population of 86.2 million people. Average daily demands for the United States were calculated to be 560 liters per capita for drinking water and 500 liters per capita of wastewater. The embedded energy within each of these resources is 340 kWh/1000 m3 and 430 kWh/1000 m3, respectively. Drinking water data at the annual scale are available for production volume (89 cities) and for embedded energy (73 cities). Annual wastewater data are available for treated volume (104 cities) and embedded energy (90 cities). Monthly data are available for drinking water volume and embedded energy (73 and 56 cities) and wastewater volume and embedded energy (88 and 70 cities). Please see the two related papers for this metadata are included with this submission. Each folder name is a city that contributed data to the collection effort (City+State Abbreviation). Within each folder is a .csv file with drinking water and wastewater volume and energy data. A READ-ME file within each folder details the contents of the folder within any relevant information pertaining to data collection. Data are on the order of a monthly timescale when available, and yearly if not. Please cite the following papers when using the database: Chini, C.M. and Stillwell, A.S. (2017). The State of U.S. Urban Water: Data and the Energy-Water Nexus. Water Resources Research. 54(3). DOI: https://doi.org/10.1002/2017WR022265 Chini, C.M., and Stillwell, A. (2016). Where are all the data? The case for a comprehensive water and wastewater utility database. Journal of Water Resources Planning and Management. 143(3). DOI: 10.1061/(ASCE)WR.1943-5452.0000739

{"This article explores the methods of a prior larger research project to understand flows in the US energy economy, quantifying energy use across American history (1800–2020). As a case study, it uses a subset of this data—agricultural energy use—to examine the methods, sources, and problems around estimating the production and consumption of energy at a national level. By combining statistical data with primary sources (like government and private studies on livestock feed demands), we produce a database that sums all energy used both on-field and in the processing and production of food more generally—and offer several counterintuitive conclusions. Per-capita agricultural energy use actually fell between 1800 and 2020. During this time period, the overall per-capita energy expenditure on food (in processing and cooking) remained fairly steady. We conclude the article by noting various uses for the data in reframing long-term agricultural trends and their environmental impacts. Energy flows are a fundamental component of social metabolism research. What this paper adds to this work is an unusual American case, one in which per capita on-field energy use declined."}

Future fine particulate matter (PM2.5) concentrations and health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographics. In this study, a human-earth system model is used to estimate US state-level PM2.5 mortality costs from 2015 to 2050 considering current major air quality and energy regulations. The Logarithmic Mean Divisia Index is applied to quantify the contributions of socioeconomic and energy factors to future changes in PM2.5 mortality costs. National PM2.5 mortality costs are estimated to decrease by 25% from 2015 to 2050, primarily driven by decreases in energy intensity and decreases in PM2.5 mortality cost per unit consumption of electric sector coal and transportation liquids. These factors together contribute to 68% of the net decrease, primarily because of technology improvements and air pollutant emission regulations. Furthermore, the results suggest that states with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PM2.5 mortality costs. In contrast, states with larger projected decreases in mortality costs have smaller increases in population and per capita GDP and greater decreases in electric sector coal share and PM2.5 mortality cost per unit fuel consumption. This dataset includes source code, input data, and model output from the Global Change Assessment Model (GCAM-USA) human-earth system model used in this study. It also includes Excel workbooks and R scripts used in producing the figures in the manuscript. This dataset is associated with the following publication: Ou, Y., S. Smith, J.J. West, C. Nolte, and D. Loughlin. State-level drivers of future fine particulate matter mortality in the United States.. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 14(12): 124071, (2019).

United States US: Renewable Internal Freshwater Resources per Capita data was reported at 8,844.321 Cub m in 2014. This records a decrease from the previous number of 8,974.715 Cub m for 2012. United States US: Renewable Internal Freshwater Resources per Capita data is updated yearly, averaging 11,308.247 Cub m from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 15,106.842 Cub m in 1962 and a record low of 8,844.321 Cub m in 2014. United States US: Renewable Internal Freshwater Resources per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Energy Production and Consumption. Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates.; ; Food and Agriculture Organization, AQUASTAT data.; Weighted average;

This dataset contains data on urban form (the configuration of the built environment) for each census tract in the United States, encompassing density (destination access), land use diversity (entropy), road network properties, road network capacity relative to the surrounding population, and public transit access. Metrics are measured around the centroid of each census tract in multiple given radii. The data also contain other publicly available metrics for each census tract that may be helpful, such as each tract's associated city, zipcode, and county name, area and water area, and centroid coordinates. Certain measures resemble those available in the U.S. Environmental Protection Agencies' Smart Location database or were derived from them, while others were compiled using additional data sources and the statistical model presented in the associated main article. Specifically, the data presented here contain travel energy use indices for each census tract, reflecting the estimated difference in daily land-based mobility energy use per capita relative to the baseline (the U.S. average) as a result of that environment's particular urban form.

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.

Graph and download economic data for Average Price: Electricity per Kilowatt-Hour in U.S. City Average (APU000072610) from Nov 1978 to Sep 2025 about electricity, energy, retail, price, and USA.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

In 2018, food waste in the United States was a significant issue with substantial environmental and economic consequences. Here are some key statistics:

Overall Waste Volume and Percentage:

Approximately 103 million tons (206 billion pounds) of food waste were generated in the US in 2018, according to the EPA.

This amounted to between 30-40% of the entire US food supply going uneaten.

On a per-person basis, it was roughly one pound of food wasted per person per day.

Economic Impact:

The annual food waste in America had an approximate value of $161 billion to $218 billion.

The average American family of four reportedly threw out $1,500 in wasted food per year (based on 2010 price data, which would be higher in 2018).

The restaurant industry alone incurred an estimated $162 billion in costs related to wasted food.

Environmental Impact:

Food waste was the number one material in American landfills, accounting for 24.1% of all municipal solid waste (MSW).

When food rots in landfills, it produces methane, a potent greenhouse gas that is 28 times more powerful than CO2 at trapping heat. Food waste was responsible for an estimated 58% of landfill methane emissions to the atmosphere.

The production of wasted food in the US was equivalent to the greenhouse gas emissions of 37 million cars.

Wasted food also means wasted resources like land, water, and energy. Annually, food loss and waste took up an area of agricultural land the size of California and New York combined, and wasted enough energy to power 50 million US homes for a year.

Approximately 21% of agricultural water resources and 19% of US croplands were wasted for food that was ultimately thrown away.

Sources of Food Waste:

Food waste occurs across the entire supply chain, with significant contributions from:

Households: An estimated 43% of food waste came from homes.

Grocery stores, restaurants, and food service companies: Accounted for about 40% of food waste.

Farms: Responsible for around 16% of food loss.

Manufacturers: Contributed about 2% of food waste.

Breakdown by Material (within MSW):

Food waste comprised the fourth largest material category in total MSW generation, estimated at 63.1 million tons or 21.6% in 2018.

These statistics highlight the significant scale of food waste in the US in 2018 and its wide-ranging negative impacts on the economy and the environment

Dataset's Link: https://catalog.data.gov/dataset/u-s-food-waste-flows-between-sectors-2018-v1-3-2

Food waste flows between waste-generating sectors and waste management routes are captured by these Flow-By-Sector (FBS) databases. Typically, the sectors use codes from the 2012 North American Industry Classification System (NAICS). Method 1 (m1 dataset file), the first dataset, assigns sectors to food waste creation and disposal statistics from the USEPA Wasted Food Report. The National Commercial Non-Hazardous Waste (CNHW) FBS dataset's discarded food data is attributed to sectors using the second approach, method 2 (m2 dataset file).

Column's Information

The CSV file "Food_Waste_national_2018_m2_v1.3.2_9b1bb41.csv" contains the following columns with their likely meanings:

Flowable: The type of material being tracked, in this case, "Food Waste".

Class: A classification for the "Flowable" material, here "Other".

SectorProducedBy: A numerical code indicating the sector that produced the food waste.

SectorConsumedBy: A numerical code indicating the sector that consumed or received the food waste.

SectorSourceName: The source of the sector classification, which is "NAICS_2012_Code" (North American Industry Classification System 2012 Code).

Context: This column appears to be empty in the provided data.

Location: This column seems to contain a location code, e.g., "=""00000""".

LocationSystem: The system used for location identification, which is "FIPS" (Federal Information Processing Standards).

FlowAmount: The quantity of food waste.

Unit: The unit of measurement for "FlowAmount", which is "kg" (kilograms).

FlowType: The type of flow, which is "WASTE_FLOW".

Year: The year the data pertains to, in this case, "2018".

MeasureofSpread: This column appears to be empty in the provided data.

Spread: A value related to the spread of the data, here "0.0".

DistributionType: This column appears to be empty in the provided data.

Min: Minimum value, here "0.0".

Max: Maximum value, here "0.0".

DataReliability: Data reliability value, here "0.0".

TemporalCorrelation: Temporal correlation value, here "0.0".

GeographicalCorrelation: Geographical correlation value, here "0.0".

TechnologicalCorrelation: Technological correlation value, here "0.0".

DataCollection: Data collection method or source, here "CalRecycle_WasteCharacterization".

**MetaSources...

This dataset provides a comprehensive, state-level view of the key factors influencing electric vehicle (EV) adoption across the United States. Compiled from authoritative sources such as the US Census Bureau, Department of Energy, National Renewable Energy Laboratory (NREL), and others, it includes annual data on EV registrations, socioeconomic indicators, infrastructure availability, policy incentives, and energy prices from multiple years.

The dataset is designed to support research and analysis on the drivers of EV adoption, enabling users to explore questions around policy effectiveness, infrastructure planning, and market dynamics.

Context & Motivation The transition to electric vehicles is a cornerstone of US climate and energy policy, yet EV adoption rates remain highly uneven across states. While states like California lead with robust infrastructure and incentives, other regions-particularly in the Midwest and South-lag behind. Understanding what drives these differences is crucial for policymakers, automakers, and energy providers.

This dataset was created as part of a research project investigating the determinants of EV adoption. By making this data publicly available, I hope to empower further research, foster data-driven policy decisions, and encourage innovation in sustainable transportation.

Data Sources EV Registrations: National Renewable Energy Laboratory (NREL)

Socioeconomic Indicators: US Census Bureau (population, income, education, labor force, unemployment)

Charging Infrastructure & Incentives: Alternative Fuels Data Center (AFDC)

Fuel Economy & Vehicle Registrations: Bureau of Transportation Statistics

Gasoline Prices: American Automobile Association (AAA)

Electricity Prices: Energy Information Administration (EIA)

CO2 Emissions: Bureau of Transportation Statistics Variables Included

The CAIT Country GHG emissions applies a besteht methodology to create a six-gas, multi-sector, and internationally vergleichbare 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 visualizations that can be readily shared through single URLs or embedded for further use online.

Data for Land-Use and Forestry indicator are provided by the Food and Agriculture Organization of the United Nations (FAO). WRI has been granted a non-exklusiv, non-transferrable 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

Datenquellen: Boden, T.A., G. Marland und R.J. Andres. 2015. Global, Regional, and National Fossil-Fuel CO2 Emissionen. 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 Organization of the United Nations (FAO). 2014. FAOSTAT Emissions Database. Rom, Italien: FAO. Available at: http://faostat3.fao.org/download/G1/*/E — International Energy Agency (IEA). 2014. CO2 Emissionen from Fuel Combustion (2014 Ausgabe). Paris, Frankreich: 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

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 visualizations 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 Organization of the United Nations (FAO). WRI has been granted a non-exclusive, non-transferrable 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 Organization 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.

The data set records the carbon dioxide emissions of 1960-2014 countries along 65 countries along the belt and road.Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.Data source:Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States.The U.S. Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division's World Energy Data Set) and world cement manufacturing (from the U.S. Department of Interior's Geological Survey, USGS 2011). The dataset contains 2 tables: CO2 emissions(kt),CO2 emissions(metric tons per capita).