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.

Electricity consumption in the United States totaled ***** terawatt-hours in 2023, one of the highest values 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 next decades. 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 2022, the Southwestern state, which houses major refinery complexes and is also home to nearly ** million people, consumed over *** terawatt-hours. California and Florida trailed in second and third, each with an annual consumption of approximately *** terawatt-hours.

Forecast: Primary Energy Consumption Per Capita in the US 2022 - 2026 Discover more data with ReportLinker!

Global primary energy consumption has increased dramatically in recent years and is projected to continue to increase until 2045. Only hydropower and renewable energy consumption are expected to increase between 2045 and 2050 and reach 30 percent of the global energy consumption. Energy consumption by country The distribution of energy consumption globally is disproportionately high among some countries. China, the United States, and India were by far the largest consumers of primary energy globally. On a per capita basis, it was Qatar, Singapore, the United Arab Emirates, and Iceland to have the highest per capita energy consumption. Renewable energy consumption Over the last two decades, renewable energy consumption has increased to reach over 90 exajoules in 2023. Among all countries globally, China had the largest installed renewable energy capacity as of that year, followed by the United States.

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe have further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels determine the amount of electricity the average person requires in each country.

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).

GCAM-USA operates within the Global Change Analysis Model, 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 May 15, 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 2015-2045 (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 key output variables related to the residential building energy sector under the four scenarios, including:

• income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)
• residential energy consumption per capita by service and fuel, by state and income group, 2015-2045
• residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, and technology, by state and income group, 2015-2045
• estimated energy burden (Eq.1), by state and income group, 2015-2045
• residential heating service inequality (Eq.2), by state, 2015-2045

Table 1

Eq. 1

\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)

for income group i and service j

Eq. 2

\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)

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.

Reference

Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7227128

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].

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.

This dataset is about countries per year in South America. It has 768 rows. It features 4 columns: country, capital city, and fossil fuel energy consumption.

This dataset is about countries per year in the United States. It has 64 rows. It features 4 columns: country, capital city, and electricity production from coal sources.

Analysis of ‘Power consumption in India(2019-2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/twinkle0705/state-wise-power-consumption-in-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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 :)

--- Original source retains full ownership of the source dataset ---

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This dataset is about countries per year in South America. It has 768 rows. It features 4 columns: country, capital city, and electricity production from hydroelectric sources.

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.

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

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.

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).

VITAL SIGNS INDICATOR
Greenhouse Gas Emissions (EN3)

FULL MEASURE NAME
Greenhouse gas emissions from primary sources

LAST UPDATED
December 2022

DESCRIPTION
Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels.

DATA SOURCE
US Energy Information Administration: Carbon Dioxide Emissions Coefficients - https://www.eia.gov/environment/emissions/co2_vol_mass.php
1990-2021

California Energy Commission: Retail Fuel Outlet Annual Reporting (Form CEC-A15) - https://www.energy.ca.gov/data-reports/energy-almanac/transportation-energy/california-retail-fuel-outlet-annual-reporting
2010-2021

California Energy Commission: Electricity Consumption by County - http://www.ecdms.energy.ca.gov/elecbycounty.aspx
1990-2021

California Energy Commission: Natural Gas Consumption by County - http://www.ecdms.energy.ca.gov/gasbycounty.aspx
1990-2021

California Department of Finance: Population and Housing Estimates, E-4 - http://www.dof.ca.gov/research/demographic/
1990-2021

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Emissions in this dataset are reported in metric tons. For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the California Energy Commission (CEC) corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Data from the CEC is limited to retail sales, therefore Vital Signs surface transportation emissions estimates are limited to GHG from retail fuel sales. Retail gasoline sales represent most of the gasoline consumed for surface transportation, but retail diesel sales are just a fraction of all diesel consumed for surface transportation. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents. Future refinements to the Vital Signs methodology for monitoring GHG emissions from all surface transportation will seek to more closely align monitoring data with estimates from the California Air Resources Board's EMFAC model.

For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly overestimated. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; data is disaggregated between residential and non-residential customers.

For natural gas consumption, the dataset is based on natural gas consumption data for the nine Bay Area counties; note that this is different than natural gas production as the region imports electricity. Certain types of liquefied natural gas shipped into the region or "makegas" produced at oil refineries during their production process may not be fully reflected in this data. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; data is disaggregated between residential and non-residential customers.

Iran is a country locating in Middle east. Iran is located in a strategic region at the crossroads of Europe, Asia, and Africa. This has made it a major center of trade and commerce for centuries. Iran is also a member of the United Nations, the Non-Aligned Movement, and the Organization of Islamic Cooperation.

Despite its rich history, large population, and abundant economic potential, Iran is a lower-middle-income country (according to the World Bank). It has large reserves of raw materials, including oil, gas, and minerals, but unfortunately, it does not fully utilize these resources.

This dataset is all the data about Iran in the world bank website. Here is a summary:

Economic data(2022/23) - GDP (current US$): 463billion - GDPpercapita(currentUS): $5,211 - Inflation, GDP deflator (annual %): 31.5% - Oil rents (% of GDP): 25.6% - Gini index: 38.8 (2019)

Social data - Population, total: 88.5 million (2022) - Population growth (annual %): 1.1% (2022) - Net migration: 28,080 (2021) - Life expectancy at birth, total (years): 77 (2021) - Human Capital Index (HCI) (scale 0-1): 0.63 (2020)

Environmental data - CO2 emissions (metric tons per capita): 7.2 (2021) - Renewable energy consumption (% of total final energy consumption): 3.6% (2021) - Forest area (% of land area): 7.8% (2020)

You can access the data in this link. There is also lots of plots and other fun tools which you should try.

[World Bank notes] The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In Iran, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. An alternative estimate (“alternative conversion factor” - PA.NUS.ATLS) is thus calculated as a weighted average of the different exchange rates in use in Iran. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. For Iran, this applies to 1972-2022. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.

It is noted that the reporting period for national accounts data is designated as either calendar year basis (CY) or fiscal year basis (FY). For Iran, it is fiscal year based (fiscal year-end: March 20).

Analysis of ‘CAIT - Country Greenhouse Gas Emissions Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/57c6dff7c751df24c697bae5 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

This is a metadata only record. The datasets used in this thesis are open and available via https://databank.worldbank.org/source/world-development-indicators We use panel dataset for 115 countries for the time span 1990-2016. The countries are categorized into four groups as per gross national income (GNI) measured using World Bank Atlas (2018) method [the 9 of low ($1005 or less), 32 of lower-middle ($1006-$3955), 35 of upper-middle ($3956-$12,235), and 39 of high ($12,236 or more) income panels]. The data on different variable of interests are collected from World Development Indicators (CD-ROM, 2018). We use real estimation adjusting inflation. The collected datasets of dependent variables are carbon dioxide (CO2) measured in metric tons per capita, methane (CH4) in Kt. of CO2 equivalent, and the particulate matter (PM2.5) in microgram per cubic meter. The independent variables of the collected datasets are gross domestic product (GDP) per capita (constant 2010 US$), energy consumption (EC) in kg of oil equivalent per capita, trade openness (TO) measured as the share of total trade volume in GDP, urbanization (UR) in terms of the share of urban population in total population and TR is the total transport services in percentage of total commercial service of exports and imports, financial development (FD) measured in domestic credit to private sector, foreign direct investment (FDI) is measured by the net inflows of FDI as a percentage of GDP, the human development index (HDI) measured by the UNDP as a proxy for human capital formation. Moreover, we measure the agricultural sector by its output share of GDP (constant 2010 US$) and the manufacturing sector by its output share of GDP (constant 2010 US$).

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..