Energy Consumption and Mix Dataset by Our World in Data

Dataset Description

This dataset is a comprehensive collection of key metrics related to energy consumption and energy mix, maintained by Our World in Data. It includes global, regional, and country-level data on primary energy consumption, energy mix, electricity mix, fossil fuel production, and related energy metrics.

Key Metrics

The dataset contains several important metrics related to global energy:

• Energy Consumption (primary energy, per capita consumption, growth rates)
• Energy Mix (share of renewables, fossil fuels, etc.)
• Electricity Mix (sources of electricity generation such as coal, hydro, wind, solar)
• Fossil Fuel Production
• Primary Energy Consumption
• Per Capita and Per GDP Energy Indicators
• Yearly data by country and global aggregates

Possible Analyses

The "Energy Consumption and Mix" dataset offers a wide range of opportunities for analysis. Here are some examples of what can be done with this dataset:

1. Global Energy Consumption Trends

• Objective: Examine how global primary energy consumption has evolved over time.
• Approach:
  • Plot global energy consumption year by year, broken down by energy source (e.g., coal, oil, natural gas, renewables).
  • Analyze growth rates in total energy consumption.
  • Investigate how the share of renewables has changed in the global energy mix.
• Potential Insights: This analysis can provide insight into which energy sources are becoming more dominant globally and how the energy landscape has shifted.

2. Energy Mix by Country

• Objective: Compare the energy mix of different countries.
• Approach:
  • For each country, visualize the breakdown of energy sources (e.g., renewables, fossil fuels) over time.
  • Analyze which countries have transitioned towards cleaner energy sources.
  • Compare the energy mix of developed vs. developing countries.
• Potential Insights: This could show which countries are leading in the transition to renewable energy and which are still reliant on fossil fuels.

3. Electricity Generation Sources by Region

• Objective: Explore regional differences in electricity generation.
• Approach:
  • Compare regions based on the percentage of electricity generated from different sources (e.g., coal, hydro, wind, solar).
  • Analyze trends in renewable electricity generation by region.
• Potential Insights: This analysis can identify regions that have made the most progress in transitioning to cleaner electricity sources.

4. Energy Consumption Per Capita

• Objective: Investigate energy consumption on a per capita basis.
• Approach:
  • Calculate the per capita energy consumption for different countries and regions.
  • Compare energy consumption per capita over time.
  • Identify which countries have the highest and lowest per capita energy consumption.
• Potential Insights: This can help uncover disparities in energy access and usage between different countries or regions.

5. Energy Consumption and Economic Growth

• Objective: Analyze the relationship between energy consumption and GDP.
• Approach:
  • Plot energy consumption per capita vs. GDP per capita for different countries and regions.
  • Look for correlations between energy consumption growth and economic growth.
  • Explore how energy consumption patterns differ between high-income and low-income countries.
• Potential Insights: This analysis can highlight the role of energy in driving economic development and the efficiency of energy usage across income levels.

6. Carbon Emissions from Energy Sources

• Objective: Assess the impact of different energy sources on carbon emissions.
• Approach:
  • Calculate emissions based on the share of fossil fuels in the energy mix.
  • Analyze trends in carbon emissions as countries transition to cleaner energy sources.
  • Compare countries or regions with high fossil fuel dependency to those with higher renewable energy shares.
• Potential Insights: This analysis could highlight the environmental impact of different energy sources and track progress towards emissions reductions.

Citation

Hannah Ritchie, Pablo Rosado and Max Roser (2023) - “Energy” Published online at OurWorldinData.org. Retrieved from: https://ourworldindata.org/energy [Online Resource]

There are large inequalities in energy consumption between countries. The average US citizen still consumes more than ten times the energy of the average Indian, 4-5 times that of a Brazilian, and three times more than China. The gulf between these and very low-income nations is even greater- a number of low-income nations consume less than 100 kilowatt-hour equivalents per person.

Secondly, global average per capita energy consumption has been consistently increasing; between 1970 and 2014, average consumption increased by approximately 45%.

This growth in per capita energy consumption does, however, vary significantly between countries and regions. Most of the growth in per capita energy consumption over the last few decades has been driven by increased consumption in transitioning middle-income (and to a lesser extent, low income countries). In the chart we see a significant increase in consumption in transitioning BRICS economies (China, India and Brazil in particular); China’s per capita use has grown by nearly 250 percent since 2000; India by more than 50 percent; and Brazil by 38 percent.

Whilst global energy growth is growing from developing economies, the trend for many high-income nations is a notable decline. As we see in exemplar trends from the UK and US, the growth we are currently seeing in transitioning economies ended for many high-income nations by over the 1970s and 80s. Both the US and UK peaked in terms of per capita energy consumption in the 1970s, plateauing for several decades until the early 2000s. Since then, we see a reduction in consumption; since 2000, UK usage has decreased by 20 to 25%.

Source

Hannah Ritchie (2019) - "Access to Energy". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/energy-access'

The data this week comes from Our World in Data's Energy Data Explorer. Complete dataset available via https://github.com/owid/energy-data.

The complete Energy dataset is a collection of key metrics maintained by Our World in Data. It is updated regularly and includes data on energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix and other relevant metrics.

This data has been collected, aggregated, and documented by Hannah Ritchie, Pablo Rosado, Edouard Mathieu, Max Roser.

Our World in Data makes data and research on the world's largest problems understandable and accessible.

Data Dictionary

owid-energy.csv

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 has 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.

The 2005 edition of the Energy Statistics Database contains comprehensive energy statistics on more than 215 countries or areas for production, trade, transformation and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. In addition, mid-year population estimates are included to enable the computation of per capita data. Data on heating (calorific) values are also provided to enable conversion to a common unit (terajoules) for interfuel comparison and analyses.

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 :)

Global primary energy consumption has increased dramatically in recent years and is projected to continue to increase until 2045. Only renewable energy consumption is expected to increase between 2045 and 2050 and reach almost 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, Qatar, Singapore, the United Arab Emirates, and Iceland had the highest per capita energy consumption. Renewable energy consumption Over the last two decades, renewable electricity consumption has increased to reach over 48.8 exajoules in 2024. Among all countries globally, China had the largest installed renewable energy capacity as of that year, followed by the United States.

The world is becoming more modernized by the year, and with this becoming all the more polluted.

This data was pulled from the US Energy Administration and joined together for an easier analysis. Its a collection of some big factors that play into C02 Emissions, with everything from the Production and Consumption of each type of major energy source for each country and its pollution rating each year. It also includes each countries GDP, Population, Energy intensity per capita (person), and Energy intensity per GDP (per person GDP). All the data spans all the way from the 1980's to 2020.

Feature Descriptions:

• Country - Country in question
• Energy_type - Type of energy source
• Year - Year the data was recorded
• Energy_consumption - Amount of Consumption for the specific energy source, measured (quad Btu)
• Energy_production - Amount of Production for the specific energy source, measured (quad Btu)
• GDP - Countries GDP at purchasing power parities, measured (Billion 2015$ PPP)
• Population - Population of specific Country, measured (Mperson)
• Energy_intensity_per_capita - Energy intensity is a measure of the energy inefficiency of an economy. It is calculated as units of energy per unit of capita (capita = individual person), measured (MMBtu/person)
• Energy_intensity_by_GDP- Energy intensity is a measure of the energy inefficiency of an economy. It is calculated as units of energy per unit of GDP, measred (1000 Btu/2015$ GDP PPP)
• CO2_emission - The amount of C02 emitted, measured (MMtonnes CO2)

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.

Overview

This dataset provides a comprehensive collection of time series data sourced from the World Bank Open Data Platform, covering a wide range of global indicators from 1960 to the most recently published year. It includes economic, social, environmental, and demographic metrics, making it an ideal resource for researchers, data scientists, and policymakers interested in global development trends, economic forecasting, or socio-economic analysis.

A tutorial on how to combined the dataset topics together into one large dataset can be found here

Why this Dataset?

My motivation for this project was to curate a high-quality collection of datasets for World Bank indicators organized by topics and structured in time-series, making them more accessible for data science projects. Since the World Bank’s Kaggle datasets have not been updated since 2019 https://www.kaggle.com/organizations/theworldbank, I saw an opportunity to provide more current data for the data analysis community.

Dataset Collection Contents

This collection brings together more than 800 World Bank indicators organized into 18 topic‑specific CSV files. Each file is structured as a country‑year panel: every row represents a unique combination of year (1960‑present) and ISO‑3 country code, while the columns hold the topic’s indicators.

The collection includes datasets with a variety of indicators, such as: - Economic Metrics: GDP growth (%), GDP per capita, consumer price inflation, merchandise trade, gross capital formation, and more.
- Social Metrics: School enrollment (primary, secondary, tertiary), infant mortality rate, maternal mortality rate, poverty headcount, and more.
- Environmental Metrics: Forest area, renewable energy consumption, food production indices, and more.
- Demographic Metrics: Urban population, life expectancy, net migration, and more.

Usage

This dataset is ideal for a variety of applications, including: - Economic forecasting and trend analysis (e.g., GDP growth, inflation).
- Socio-economic studies (e.g., education, health, poverty).
- Environmental impact analysis (e.g., renewable energy adoption).
- Demographic research (e.g., population trends, migration).

Topic datasets can be merged with each other using year and country code. This tutorial with notebook code can help you get started quickly.

Collection Methodology

The data is collected via a custom software application that discovers and groups high-quality indicators with rules-based logic & artificial intelligence, generates metadata, and performs ETL for the data from the World Bank API. The result is a clean, up‑to‑date collection of World Bank indicators in time-series format that is ready for analysis—no manual downloads or data wrangling required.

Modifications

The original World Bank data has been aggregated and transformed for ease of use. Missing values have been preserved as provided by the World Bank, and no significant transformations have been applied beyond formatting and aggregation into a single file.

Source & Attribution

The World Bank: World Development Indicators

This dataset is publicly available and sourced from the World Bank Open Data Platform and is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. When using this data, please attribute the World Bank as follows: "Data sourced from the World Bank, licensed under CC BY 4.0." For more details on the World Bank’s terms of use, visit: https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Feel free to use this data in Kaggle notebooks, academic research, or policy analysis. If you create a derived dataset or analysis, I encourage you to share it with the Kaggle community.

This dataset was used in the publication of "All Roads Lead to Paris: The Eight Pathways to Renewable Energy Target Adoption" in the journal of Energy Research & Social Science. The objective was to compile data on the first national adoption of a renewable energy target in each country to analyze its mechanisms of diffusion (learning, economic competition, emulation, and coercion). The data were compiled for 187 countries for the period ranging from 1975 to 2017. The list of countries was gathered from the Annex I of IRENA's "Renewable Energy Target Setting" report. We used primarily the IEA policies database (https://www.iea.org/policies) to identify the first adoption of a renewable energy target in each country. Other sources were used when data was unavailable in such repository for specific countries. Additionally, we include the data gathered from various sources, as they were used in our paper for measuring variables. The variables in this dataset include: target adoption (or “Target”, from various sources listed in the dataset); year of adoption (or “Year”, from various sources listed in the dataset); cumulative membership to energy-related international environmental agreements (or “IEA”, with data from Mitchell’s International Environmental Agreements Database Project); net energy imports as a percentage of energy use (or “Energy”, with data from the World Bank); a similarity index (or “Similarity”, created with data from the Polity Index, population and GDP per capita from the World Bank, and revenue from the World Bank); official development assistance as a percentage of gross national income (or “ODAGNI”, with data from the World Bank and OECD); income level (“Income”, with data from the World bank); and the international price for oil (“Oil”, with data from the Federal Reserve Bank of St. Louis). For more details, refer to the manuscript. Note that in 2018 the “IEA’s policy database” was actually the “IEA/IRENA RE Policies and Measures database”. The links for the sources for renewable energy target adoption for Norway and Albania were lost in the transition from one to the other; all other sources could be retrieved by the authors.

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).

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.

The United Nations Energy Statistics Database (UNSTAT) is a comprehensive collection of international energy and demographic statistics prepared by the United Nations Statistics Division. The 2004 version represents the latest in the series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The database contains comprehensive energy statistics for more than 215 countries or areas for production, trade and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. Mid-year population estimates are included to enable the computation of per capita data. Annual questionnaires sent to national statistical offices serve as the primary source of information. Supplementary data are also compiled from national, regional and international statistical publications. The Statistics Division prepares estimates where official data are incomplete or inconsistent. The database is updated on a continuous basis as new information and revisions are received. This metadata file represents the population statistics during the expressed time. For more information about the country site codes, click this link to the United Nations "Standard country or area codes for statistical use": https://unstats.un.org/unsd/methodology/m49/overview/

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$) and the manufacturing sector by its output share of GDP (constant 2010 US$).

Context

This complete CO2 and Greenhouse Gas Emissions dataset is a collection of key metrics maintained by Our World in Data. It is updated regularly and includes data on CO2 emissions (annual, per capita, cumulative and consumption-based), other greenhouse gases, energy mix, and other relevant metrics.

Content

• CO2 emissions: this data is sourced from the Global Carbon Project. The Global Carbon Project typically releases a new update of CO2 emissions annually.
• Greenhouse gas emissions (including methane, and nitrous oxide): this data is sourced from the CAIT Climate Data Explorer, and downloaded from the Climate Watch Portal.
• Energy (primary energy, energy mix and energy intensity): this data is sourced from a combination of two sources. The BP Statistical Review of World Energy is published annually, but it does not provide data on primary energy consumption for all countries. For countries absent from this dataset, we calculate primary energy by multiplying the World Bank, World Development Indicators metric Energy use per capita by total population figures. The World Bank sources this metric from the IEA.
• Other variables: this data is collected from a variety of sources (United Nations, World Bank, Gapminder, Maddison Project Database, etc.). More information is available in our codebook.

Acknowledgements

Our World in Data Edouard Mathieu Bobbie Macdonald Hannah Ritchie Daniel Dias

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.

India is the third largest producer of electricity in the world. The national electric grid in India has an installed capacity of 399.467 GW as of 31 March 2022. Renewable power plants, which also include large hydroelectric plants, constitute 39.2 % of total installed capacity. During the fiscal year (FY) 2019-20, the gross electricity generated by utilities in India was 1,383.5 TWh and the total electricity generation (utilities and non utilities) in the country was 1,598 TWh.The gross electricity consumption in FY2019 was 1,208 kWh per capita.[7] In FY2015, 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.

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*****************************RES means renewable energy***********************************

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Dataset Title: Global Climate Change Indicators: A Comprehensive Dataset (2000-2024)

Subtitle: Tracking Temperature, Emissions, Sea Level Rise, and Environmental Trends Across Countries

Description: This dataset provides a comprehensive overview of key climate change indicators collected across different countries from the year 2000 to 2024. It includes 1000 data points capturing various environmental and socio-economic factors that reflect the global impact of climate change. The dataset focuses on average temperature, CO2 emissions, sea-level rise, rainfall patterns, and more, enabling users to analyze trends, correlations, and anomalies.

Fields Explanation: Year: The year in which the data was recorded, ranging from 2000 to 2024. It helps track historical trends in climate change and related variables over time.

Country: The country or region where the climate data was collected. The dataset includes a diverse set of countries from across the globe, representing different geographic regions and climates.

Average Temperature (°C): The average annual temperature recorded in each country, measured in degrees Celsius. This field allows for comparisons of temperature changes across regions and time.

CO2 Emissions (Metric Tons per Capita): The average amount of CO2 emissions per capita in metric tons, reflecting the country's contribution to greenhouse gases. This field is useful for analyzing the link between human activity and environmental changes.

Sea Level Rise (mm): The recorded annual sea-level rise in millimeters for coastal regions. This indicator reflects the global warming effect on melting glaciers and thermal expansion of seawater, critical for studying impacts on coastal populations.

Rainfall (mm): The total annual rainfall recorded in millimeters. This field highlights changing precipitation patterns, essential for understanding droughts, floods, and water resource management.

Population: The population of the country in the given year. Population data is important to normalize emissions or other per-capita analyses and understand human impact on the environment.

Renewable Energy (%): The percentage of total energy consumption in a country that comes from renewable energy sources (solar, wind, hydro, etc.). This metric is vital for assessing the progress made toward sustainable energy and reducing reliance on fossil fuels.

Extreme Weather Events: The number of extreme weather events recorded in each country, such as hurricanes, floods, wildfires, and droughts. Tracking these events helps correlate the increase in climate change with the frequency of natural disasters.

Forest Area (%): The percentage of the total land area of a country covered by forests. Forest cover is a critical indicator of biodiversity and carbon sequestration, with reductions often linked to deforestation and habitat loss.

Applications: Climate Research: This dataset is invaluable for researchers and analysts studying global climate change trends. By focusing on multiple indicators, users can assess the relationships between temperature changes, emissions, deforestation, and extreme weather patterns.

Environmental Policy Making: Governments and policy analysts can use this dataset to develop more effective climate policies based on historical and regional data. For example, countries can use emissions data to set realistic reduction goals in line with international agreements.

Renewable Energy Studies: Renewable energy data provides insights into how different regions are transitioning toward greener energy sources, offering a comparison between high-emission and low-emission countries.

Predictive Modeling: The data can be used for machine learning models to predict future climate scenarios, especially in relation to global temperature rise, sea-level changes, and extreme weather events.

Public Awareness & Education: This dataset is a useful educational tool for raising awareness about the impacts of climate change. Students and the general public can use it to explore real-world data and learn about the importance of sustainable development.